The client replies to your recap within ten minutes, asking where \"launch moved to the 14th\" came from, because nobody agreed to that.
You check. An AI tool wrote the draft, you skimmed it, it read well, but ultimately it was wrong.
It happens. It happens even to the biggest global companies. Deloitte Australia delivered a government report, worth about US$290,000 (A$440,000), that turned out to include references to research papers that don't exist and a made-up quote from a court judgment. Deloitte disclosed it had used Azure OpenAI, corrected the report, and refunded the final payment.
It's accurate at language and unreliable on facts it doesn't have. Grammar, structure, and tone are where modern models rarely slip. Specific facts (a date, a budget, a client's name, who owns what) are different, because a model can't know what it wasn't told, and it won't always say so.
Independent testing backs this up. When journalists from 22 public service broadcasters checked more than 3,000 answers to news questions from four major AI assistants, 45% had at least one significant issue, and 20% had major accuracy problems like hallucinated details or outdated information.
Tools that pull from a set of source documents before answering do better, though not perfectly. Stanford researchers tested leading AI legal research tools, several marketed as close to hallucination-free, and found they still hallucinated on 17% to 33% of queries. GPT-4 on its own did worse, at 43%.
![\"Stacked](\"https://framerusercontent.com/images/kXKPSJpeKgpVS8Rsm51tqTGSho.webp\")
So is AI accurate? Often. Accurate enough to send to a client without checking? No.
A wrong answer from AI doesn't look wrong. It arrives in the same calm, confident tone as everything else.
Some of that comes down to how models are trained and scored. OpenAI's researchers compare it to a multiple-choice exam: leave a question blank and you get zero, guess and you might get lucky. Grade models only on how many answers they get right and they learn to guess rather than say \"I don't know.\" Their paper, now peer reviewed and published in Nature, argues this is a big reason hallucinations persist. OpenAI's own summary is the easier read.
Researchers at MIT's CSAIL found the same pattern from a different angle. Standard training for reasoning models rewards right answers and gives nothing for expressing doubt, which makes models \"more capable and more overconfident at the same time\".
In April 2025, OpenAI rolled back a ChatGPT update that had made it overly flattering and agreeable. A tool that's eager to please you isn't the one you want checking your recap.
The University of Maryland's library guide to AI lists guessing to cover gaps in information as one of the ways it goes wrong. In agency work, that's the big one. A few that will sound familiar:
The deadline - The call ended with \"let's aim for mid-month.\" The draft says \"launch on the 14th\"
The budget - Nobody said a number, so the model borrowed a plausible one
The reversal - Scope got cut on Tuesday's call. The model only saw Monday's notes
The wrong client - Two accounts, similar names, one pasted transcript too many
![\"A](\"https://framerusercontent.com/images/PbpqO5jWS5MFjeM9HsdMOdGpA.webp\")
Valerie Dennis Craven ran into this on a client whitepaper this spring, asking Claude for an outline and brief using only the SME interview and information she provided.
\"It inferred information incorrectly, even when I asked it to raise questions or leave placeholders for a human to find information… It got a concept wrong and it made its own assumption of how the technology that the whitepaper was including worked… the SME and I didn't need to discuss it since he, I and the writer all fundamentally knew it.\"
Valerie Dennis Craven, Principal and Strategist, True North Content
In each of these, the model did what it's built to do: fill empty space with something that sounds right. The less it knows about the account, the more space there is to fill, which is why an agency's own context (past calls and decisions) is worth keeping somewhere it can be found.
If gaps cause many of these errors, the fix is fewer gaps. The Nature paper lists retrieval and tool use among the mitigations that work. In plain terms, give the model the source material instead of asking it to remember or guess.
In client work, most of the source material is conversation. What the client asked for, what you agreed, what changed since. When the AI writing your recap has the notes from that call, plus the project's earlier meetings and docs, it's working from what was said. It's also how you avoid the reversal problem from earlier. If the AI has every call on the project, it can see that Tuesday's call cut what Monday's call agreed.
\"For us it's rarely the model going crazy. It's usually a mistake in the research step. The AI finds accurate information about the wrong thing, like a business or a city with a similar name, or it works from something outdated because the newer version lives somewhere else and it never read it. Then it writes confidently from that, and the draft reads fine.\"
Jens Rhoades, Owner, Floodlight SEO
Supernormal is built to have that context. Its meeting notetaker takes notes on your calls in the background, with no bot joining. Supernormal then uses those notes along with your email and docs when you ask it for a status report or a follow-up. If something wasn't settled on the call, like an owner or a date, ask it to flag the gap instead of filling it in, and still check it did. The weekly client status report template is a good place to start, and the guide to client status reports covers what should go in one.
Context cuts errors down. It won't take them to zero. The Stanford legal tools had source documents and still got things wrong. Transcripts mishear names. Someone says \"Friday\" and means next Friday. A draft grounded in the call is a much safer starting point, and it still needs a person to read it.
For a lot of agencies, the bigger worry is whether they can stand behind what the AI wrote, and explain it when a client asks.
In a Q1 2026 survey of 250 agencies by Digital Applied, hallucination ranked seventh among blockers to rolling out AI, named by 18%. Client trust and explainability ranked second, at 37%. It's research from a company that sells AI services, and the sample leaned toward agencies already interested in AI, so read the exact numbers loosely. The direction makes sense though. Clients want to know where a claim came from.
That's the practical case for drafts built from your own calls. If every line in a recap traces back to something said in a meeting, you can answer \"where did this come from?\" in seconds.
\"I check AI-assisted work in two ways. A 'stop slop' skill strips out phrasing that reads as AI-generated, and a QA council skill catches any glaring errors… Then I manually review for both tone and accuracy before it ever goes out to a client.\"
Jenn Feldmann, Senior SEO Strategist, VisualFizz
The checks below are for that last manual review. They take a few minutes.
Names, dates, and numbers - Check each one against the transcript or doc. This is where plausible-but-wrong hides
Commitments - Every \"we'll\" and \"you'll\" should trace back to someone agreeing to it
Anything without a source - If you can't point to where a line came from, cut it or confirm it
The latest version - Make sure a later call didn't undo what this one decided
What should stay internal - Team chat, pricing debates, and opinions about the client don't go in the email
If your team sends client updates every week, write these checks into the SOP for that update so everyone reviews the same things.
The client who caught that wrong date was reading carefully. Start from a draft that knows what was said, and run the five checks before you send. Try the Supernormal meeting notetaker on your next client call.
", "url": "https://www.supernormal.com/blog/ai-accuracy-client-work", "title": "Is AI accurate enough for client work? | Supernormal", "summary": "AI sounds just as sure when it's guessing. Where client work errors come from, and five checks to run before you send.", "date_modified": "2026-10-05T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "AI and agents" ] }, { "id": "urn:sha256:499469cd1ff84c66e42bea8efa2c0240b644e655564350e4d8f4c52e0347a523", "content_html": "McKinsey presentations follow four principles. Lead with the answer (the Pyramid Principle). One idea per slide. Data presented as a story. Executive-ready formatting. Openings use the SCQA framework: Situation, Complication, Question, Answer. The result is decks that respect executive time and force clarity through structure.
A McKinsey associate writes the answer on slide one. A McKinsey partner cuts ten slides for every one that survives. Both habits trace to a choice the firm made in the 1960s: structure beats narrative. Today, that choice still shapes how the world's most influential consulting decks get built.
We analyzed the public McKinsey deck record, including the 44 decks cataloged by Plus AI, with deep dives into Women in the Workplace 2022 (McKinsey & Company), the 2023 DSNY Future of Trash report (prepared with McKinsey support), Jobs Lost, Jobs Gained (2017), and Reinventing Construction (2017). Across decades, industries, and clients ranging from the United Nations to Fortune 100 boards, the same patterns hold.
If you want to see these principles in action, our McKinsey-style presentation template applies SCQA, the Pyramid Principle, and consultant-grade formatting to your own client work.
Across the public record of McKinsey decks, four principles surface again and again, regardless of industry, geography, or use case.
The Pyramid Principle was created by Barbara Minto, the first female post-MBA hire at McKinsey, in the 1960s. While editing reports at McKinsey's Cleveland office, Minto noticed she was always reorganizing ideas into a pyramid shape. Her method became so effective that John Tomb, the Cleveland office manager, had her train all Associates on report-writing. Eventually, every McKinsey office adopted her approach. McKinsey's alumni network still credits Minto with shaping the firm's communication standards.
The Pyramid Principle inverted traditional business communication by putting the answer at the top of the structure, supported by key arguments, which are supported by data.
The structure has three levels:
Top level: The summary point (your answer or recommendation)
Second level: 3-5 key points that support the top-level recommendation
Third level: Data, analysis, and evidence supporting each second-level point
![\"Three-tier](\"https://framerusercontent.com/images/In6tlL5DxmRlM0ALi1vITqq0A.webp\")
Why this works: Executive attention is the scarcest resource in the room. If a CEO only reads slide one, they get the complete recommendation. If they want depth, the logic is organized hierarchically. This structure respects time and ensures the key message lands regardless of how much of the deck gets read.
The strategic choice: McKinsey consultants bet that starting with the answer builds credibility faster than building suspense. They are right. In high-stakes business contexts, confidence signals expertise.
McKinsey decks never try to communicate two points on a single slide. Each slide has one purpose, one insight, one message.
Why this works: Cognitive load. When a slide tries to make multiple points, the audience has to work to separate them. A single-focus slide is scannable in 5 seconds. This matters in board meetings where executives flip ahead while you are still speaking.
The strategic choice: McKinsey would rather have a 30-slide deck with perfect clarity than a 15-slide deck where each slide requires explanation.
Every claim in a McKinsey presentation is backed by evidence. But the data is presented as a story with a clear throughline, not as raw analysis. (For more on what executive-grade decks actually look like, see our breakdown of real pitch deck examples.)
Why this works: Data without narrative is just numbers. McKinsey consultants select data that advances the argument and visualize it in ways that make the insight obvious. The chart type is chosen specifically to make the conclusion unavoidable.
The strategic choice: McKinsey shows less data, not more. They exclude analysis that does not directly support the recommendation, even if that analysis was expensive to produce.
McKinsey slides are designed for boardrooms. Clean layouts, consistent formatting, strategic white space, and minimal text make complex information accessible to C-suite executives scanning between meetings.
Why this works: Visual consistency reduces cognitive load. When every slide follows the same grid, uses the same fonts, and structures information identically, the audience stops thinking about the format and focuses entirely on the content.
The strategic choice: McKinsey invests heavily in formatting because it signals rigor. A well-formatted deck suggests well-structured thinking. It is a proxy for quality.
![\"SCQA](\"https://framerusercontent.com/images/hWn6d9kyOGC6YdpjgVC5deNRstQ.webp\")
McKinsey consultants structure the opening of every presentation using SCQA: Situation, Complication, Question, Answer. The framework traces back to Barbara Minto, who paired it with the Pyramid Principle as the setup that leads into the recommendation. It creates engagement and frames the answer.
Start with facts the audience already knows and accepts. This establishes common ground.
Example: \"Your company has been the market leader in retail banking for 15 years, with consistent 12% annual growth.\"
Why it works: Starting with agreement creates psychological safety. The audience nods along, building trust before you introduce tension.
Describe what changed, what is wrong, or what opportunity emerged. This disrupts the comfortable situation.
Example: \"However, digital-first competitors have captured 40% of new account openings in the past 18 months, and your growth rate has declined to 3%.\"
Why it works: Tension creates urgency. Without a complication, there is no reason to act on your recommendation.
State the question that naturally arises from the complication.
Example: \"How should you respond to digital competition while maintaining your core customer relationships?\"
Why it works: The question focuses everyone on the same problem. It prevents the conversation from drifting to adjacent issues.
The top of your pyramid: the core recommendation, stated clearly.
Example: \"Launch a digital-first subsidiary targeting customers under 35, while enhancing personalization for your existing customer base.\"
Why it works: By the time you reach the answer, you have earned the right to be direct. The audience is primed to hear your recommendation.
SCQA frames the opening (slides 1-3). The Pyramid Principle structures the supporting analysis (the rest of the deck). Together, they create presentations that are both engaging and logically rigorous.
The same SCQA opening structure shows up in Bain and BCG decks too, which is why former consultants from any of the three firms tend to describe it as the universal default.
Some McKinsey consultants use a simplified variant called SCR (Situation-Complication-Resolution), which skips explicitly stating the question and jumps directly to the resolution.
McKinsey presentations have a distinctive look. These are not aesthetic choices. They are functional decisions based on how executives process information.
As a common guideline, McKinsey slides rarely exceed about 30 words. They use short phrases and bullet points, never paragraphs.
Why it works: Text-heavy slides force the audience to choose between reading and listening. They cannot do both. Minimal text keeps attention on the speaker.
What they do instead: The header carries the weight. It states the conclusion in a complete sentence.
This is McKinsey's most distinctive design choice. Headers are complete sentences that communicate the point. Compare a generic corporate header against three real McKinsey headers from Women in the Workplace 2022:
Generic header: \"Leadership representation analysis\"
McKinsey header (slide 4): \"Despite modest progress, women are still dramatically underrepresented in leadership\"
McKinsey header (slide 5): \"'The great breakup': women leaders are leaving their companies at the highest rate in years\"
McKinsey header (slide 6): \"The 'broken rung' is still broken. Women of color especially are less likely to overcome this barrier\"
Why it works: A well-written McKinsey deck can be understood by reading headers alone. Read the three headers above and you already have the argument: leadership representation is stuck, attrition is accelerating it, and the entry barrier still penalizes women of color hardest. The charts and text support what the headers already state.
The strategic choice: This forces clarity. If you cannot write a clear header, you do not understand the slide's purpose yet.
White space is not wasted space. McKinsey designers use it to create visual hierarchy, separate ideas, and give the eye places to rest.
Why it works: A cluttered slide suggests cluttered thinking. White space signals confidence and control.
The strategic choice: McKinsey shows one chart per slide, not three. They would rather create three slides than crowd one.
McKinsey chooses chart types strategically. A waterfall chart shows buildup. A 2x2 matrix reveals strategic positioning. A stacked bar chart shows composition over time. Women in the Workplace 2022 uses pipeline visualizations that combine bar charts with year-over-year deltas, making leadership underrepresentation visible at a glance. Reinventing Construction: A Route to Higher Productivity (2017) uses cross-industry productivity bar charts to make the construction sector's lag impossible to miss.
Why it works: The right chart makes the insight obvious at a glance. The wrong chart requires explanation. McKinsey never wants to explain a chart.
What they do: They test multiple chart types for the same data and choose the one where the conclusion is unavoidable.
McKinsey and BCG both commissioned their own typefaces, but the fonts in their client slides are more ordinary than you'd expect.
McKinsey commissioned two typefaces in its 2019 rebrand: Bower, a serif named after Marvin Bower, and McKinsey Sans for body text. You'll see them across the firm's website and marketing.
Open a client deck, though, and the fonts change. McKinsey swaps its custom faces for Georgia and Arial, because custom fonts don't travel between computers and can break a layout the moment a file lands on someone else's machine. System fonts keep the deck looking the same everywhere.
BCG works the same way behind its own pair, Henderson Serif and Henderson Sans, built on Baskerville. A serif for headers, a sans for body, used consistently.
For your own slides, pick two fonts that sit on every machine, one serif for headers and one sans for body, and use them the same way on every slide. Georgia and Arial get you most of the McKinsey look with nothing to install.
McKinsey builds its slides on blue. A deep, almost-black blue does the structural work, and one brighter blue turns up now and then for emphasis. The effect comes from contrast: dark type on white, with a single accent to send the eye where it matters.
There's no long list of colors on a good McKinsey slide. Most run on that blue plus black, white, and gray, and they save any strong color for the one number in a chart the reader needs to catch. If you want the same result, cut your palette back to one dark base and one accent, give the slide plenty of white space, and let the data carry the color.
Understanding what McKinsey excludes is as revealing as understanding what they include. These are not oversights. They are strategic choices.
McKinsey decks do not open with \"About McKinsey\" or team bios. They jump directly into the situation.
Why: Executive time is too valuable. The audience already knows who McKinsey is. Starting with context about the firm wastes the first 2 minutes when attention is highest.
The strategic choice: Earn credibility through insight, not credentials.
McKinsey does not build to a conclusion. They state it on slide one, then support it.
Why: Business decisions are not mystery novels. Leaders need to know the recommendation immediately so they can evaluate the supporting logic with the answer in mind.
The strategic choice: Confidence over cleverness. Starting with the answer signals you have done the work.
You will never see a McKinsey slide with a spreadsheet screenshot or a dense table of numbers.
Why: Tables do not tell stories. They make the audience work to find the insight. McKinsey visualizes data in ways that make the conclusion unavoidable.
The strategic choice: If the data matters, it deserves a proper visualization. If it does not matter enough to visualize, it does not belong in the deck.
McKinsey recommendations are direct. They do not say \"we might consider potentially exploring\" or \"one option could be to possibly...\"
Why: Hedging language signals uncertainty. While McKinsey consultants acknowledge risks and limitations, they separate them from the core recommendation. The recommendation is stated with conviction.
The strategic choice: Leaders need decisiveness. Consultants who hedge are not providing value.
No clip art, no stock photos of handshakes, no decorative divider lines, no logo watermarks on every slide.
Why: Every element that does not serve a purpose is a distraction. Decorative elements are cognitive clutter.
The strategic choice: Clean beats clever. Professional beats creative. Clarity beats personality.
You will not find phrases like \"Just a quick thought...\" or \"This might not be perfect, but...\" or \"We are still refining this, but...\"
Why: Language that seeks permission undermines authority. McKinsey consultants are hired for expertise. Apologizing for the recommendation wastes that investment.
The strategic choice: Present with confidence or do not present at all.
McKinsey consultants rely on proven structures that organize complex information clearly.
MECE, also created by Barbara Minto, is the foundation of McKinsey problem-solving. When breaking down a problem or market, categories do not overlap and nothing is left out.
![\"MECE](\"https://framerusercontent.com/images/pI9IQmYyr0PXPYisiDjp9awI.webp\")
💡 Side note: Barbara Minto pronounces MECE as \"meece\" (one syllable, rhyming with \"niece\"), not \"Mee-cee\" as most at McKinsey say it. As she puts it: \"I invented it, so I get to say how to pronounce it.\"
Why it works: MECE eliminates the \"what about...\" questions. If your breakdown is truly MECE, you have covered everything without redundancy.
Where you see it: Market segmentation slides, problem decomposition, strategic option analysis.
Waterfall charts show how individual components contribute to a final total.
Why McKinsey uses them: They make buildup or breakdown intuitive. You can see exactly how you got from revenue to profit, or from last year's performance to this year's.
Where you see them: Revenue bridges, cost breakdowns, profit variance analysis.
The classic 2x2 matrix plots options across two dimensions (like market attractiveness and competitive strength) to reveal strategic choices.
Why McKinsey uses them: They force clarity about tradeoffs. A 2x2 shows that you cannot optimize for everything. You have to choose.
Where you see them: Portfolio analysis, market prioritization, investment decisions.
When showing composition over time, stacked bars reveal both total size and internal mix.
Why McKinsey uses them: They show two stories at once: how the whole is changing and how the parts are shifting.
Where you see them: Market share evolution, revenue mix shifts, customer segment trends.
A typical McKinsey presentation follows the SCQA framework for the opening, then uses the Pyramid Principle to structure the supporting analysis. StrategyU has a deeper walkthrough of how these two frameworks integrate inside the strategy consulting process.
Situation slide: Establish context and current state
Complication slide: Introduce the problem or opportunity
Question slide: Frame the key question (sometimes implicit)
Answer slide (executive summary): State the core recommendation with 3-5 supporting points
Supporting analysis chapter 1: First key point with data and evidence
Supporting analysis chapter 2: Second key point with data and evidence
Supporting analysis chapter 3: Third key point with data and evidence
Each chapter typically contains 5-8 slides that build the case for that specific point.
Next steps and implications: What happens next, what decisions are needed
Why this structure works: It is modular. If time gets cut, you can skip chapters 2 and 3 and still have a complete presentation. The opening and closing always deliver a coherent story.
McKinsey's Women in the Workplace 2022 deck (published with LeanIn.org on the McKinsey & Company SlideShare) follows this structure almost line for line:
Slide 2: Methodology and scale (333 companies, 12 million+ employees, 40,000+ surveyed). Establishes the situation and earns credibility before any argument lands.
Slide 3: \"Companies are facing two major [challenges].\" Names the complication.
Slides 4 to 6: The core findings, each with an action title that states the conclusion (\"Despite modest progress, women are still dramatically underrepresented in leadership,\" \"The great breakup,\" \"The broken rung is still broken\").
Slide 7: Chapter divider, \"Women's experiences.\" Slides 8 to 12 build the case.
Slide 13: Chapter divider, \"The future of work.\" Slides 14 to 19 build the case.
Slide 20: Chapter divider, \"Recommendations for companies.\" Closing slides deliver the call to action.
Read by headers alone, the argument is fully there. That is the test of a well-built McKinsey deck.
You don't have to guess at the style, because McKinsey publishes a lot of its own work.
The annual Women in the Workplace study McKinsey runs with LeanIn.org is available in full on McKinsey's site. It shows the house style on live content: conclusion-first headers, one idea per slide, and charts that make a single point.
The McKinsey Global Institute publishes long-form research on its site too. The formatting runs denser than a client deck, but the same habits show up: a clear line of argument, and charts built to be read in seconds.
Read one the way you'd reverse-engineer it. Look at how each header states a finding instead of naming a topic, and how the writing lands its point before it explains the detail. A few passes through a genuine example teach the pattern faster than any checklist.
Beyond presentation structure, McKinsey has developed frameworks that appear repeatedly in their decks.
McKinsey's 7S model analyzes organizational effectiveness across seven elements: Strategy, Structure, Systems, Shared Values, Style, Staff, and Skills.
Why it is effective: It is comprehensive without being overwhelming. Seven is memorable. The alliteration makes it sticky.
This framework separates growth initiatives into three time horizons: Horizon 1 (core business), Horizon 2 (emerging opportunities), and Horizon 3 (future bets).
Why it is effective: It legitimizes investment in unproven ideas by creating a portfolio approach. It prevents \"all or nothing\" thinking about innovation.
Break down business activities into primary activities (operations, sales, service) and support activities (HR, technology, procurement) to identify where value is created and where costs can be reduced.
Why it is effective: It forces systematic analysis of every business function. Nothing gets overlooked.
Rather than analyzing everything, McKinsey consultants start with a hypothesis about the answer, then test it.
Why it is effective: It creates focus and speeds up analysis. You know what you are trying to prove or disprove.
After analyzing the playbook, patterns emerge.
McKinsey optimizes for executive attention. Every choice (minimal text, conclusion-first structure, one idea per slide) respects that executive time is scarce and attention is fragile.
McKinsey builds credibility through structure. The rigor of MECE frameworks, the discipline of the Pyramid Principle, and the consistency of visual formatting all signal: \"We have done the work.\"
McKinsey removes friction. By excluding decorative elements, dense tables, hedging language, and narrative suspense, they make it easy for executives to focus solely on the recommendation and its logic.
McKinsey forces clarity on themselves. When you have to state your conclusion in the header, you cannot hide behind vague thinking. The format demands precision.
Understanding McKinsey's playbook does not mean copying it blindly. But the principles reveal useful questions:
Can someone understand your deck by reading headers alone?
Does your opening establish situation, complication, and answer?
Are you showing less data more clearly, or more data less clearly?
What are you including that does not directly support your recommendation?
Is your structure hierarchical (Pyramid Principle) or chronological?
Are you building to a conclusion or starting with one?
The McKinsey playbook isn't about looking or sounding like McKinsey, but is instead about making strategic choices that respect your audience's time and cognitive capacity.
You can get the look in the tools you already have. Four moves do most of the work.
Put the takeaway in the title as a full sentence, so the reader knows the point before they reach the chart. \"Revenue grew 12% on new accounts\" works harder than \"Revenue overview\".
If a slide is making two arguments, split it into two slides. One message per slide keeps the logic easy to follow and easy to skim.
Choose a serif for headers and a sans for body, set them in your template, and leave wide margins so the content has room. Consistency is what reads as polished.
Give each chart a single job. Label the series directly instead of leaning on a legend, and put color on the one figure that matters, so the eye goes straight to it.
You don't need a design team for any of this, just a bit of restraint and a template you reuse every week.
If you'd rather start from a structure than a blank slide, work from a McKinsey style presentation template. It lays out the header-as-conclusion format, an SCQA opening, and room for the chart types McKinsey leans on, so you drop in your own content and keep the shape.
McKinsey presentations work because they are built on disciplined choices about what to include and what to exclude. They respect executive attention. They force clarity through structure. They build credibility through consistency.
These are not proprietary secrets. They are principles anyone can apply: start with the answer, use one slide per idea, make headers actionable, choose the right chart for the insight, and eliminate everything that does not serve the recommendation.
The difference between a good presentation and a great one is often clarity and structure. McKinsey has spent 60 years refining both.
A good deck is slow for one reason: turning a pile of notes and numbers into a clear line of argument takes time. That's the part Supernormal helps with. It takes notes in the background of your meetings, with no bot on the call, then builds a first draft of your slides from that context when you ask for it. You get the structure and the starting content in your own words, ready to shape into the deck you're after. You keep every call on the story. It gets you past the blank slide and into the work that needs your expertise and judgment.
", "url": "https://www.supernormal.com/blog/mckinsey-presentation-playbook", "title": "Inside McKinsey's presentation playbook: real examples", "summary": "McKinsey writes the answer on slide one, then proves it. Inside the playbook: four principles, SCQA, MECE, and 44 real deck examples.", "date_modified": "2026-09-14T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "Client work" ] }, { "id": "urn:sha256:870d6919b215636c033c46beaa9142a2ca48088507ca2ab64ffbfb66b267d539", "content_html": "Ask an account manager about the weekly client report and you'll get a sigh. It eats an afternoon, and you're never quite sure the client reads it.
Part of the problem is what goes in them. Most reports answer activity: here's everything we did this week. That's not what the client is scanning for. They want to know one thing, are we on track and is there anything I need to worry about, and if the report doesn't answer that in the first few lines, it gets skimmed and filed.
So the report does two jobs badly at once. Too long for the client to read, too much work for you to write. Both get better with the same move: write for reassurance and decisions, keep it to a page, and stop building it from scratch every week. Most of the update already exists in the check-in you just had.
A status report isn't a work log. The client isn't grading your effort, they're managing risk on their side. The job of the report is to tell them where things stand, flag anything that needs them, and let them get back to their day reassured.
Use that as the test for every line: does this help the client make a decision or feel confident? If not, it belongs in your internal notes, not the report.
A version that does the job, top to bottom:
Status at a glance: green, amber, or red, in one line
What moved this week
What's at risk, and what you're doing about it
What you need from the client, with a date
What's next
Cut the play-by-play, the vanity metrics, and anything the client can't act on. If a number needs a paragraph to explain, either add the one line of context that makes it mean something or leave it out. Raw figures with no read just make the client do the analysis, and they'll often get it wrong.
The format matters less than whether the report answers the question, but it's worth matching it to the situation. For most weekly updates, an email is the right call. It's the lowest friction to write, it gets read in the inbox where the client already lives, and it's easy to reply to. That's your default.
Move to a shared doc or page when there's more detail to house, or when a few people on the client side dip in across the week and you want one link that stays current. Save slides for the bigger moments, a monthly or quarterly review, or a walk-through with senior client-side people. A deck for a routine weekly update is usually more work for you and more to wade through for them.
Whatever you pick, keep it consistent, and you stop deciding the format every week.
The delivery tracker your team runs on is not the client's report. Forwarding it feels like one less thing to write, but it buries the answer the client wants under task-level noise.
Keep two versions. The internal one tracks the work, close to how you manage the project day to day. The client one answers are we on track and do you need to do anything. Send the second.
The hardest weeks to report are the ones where nothing visible shipped. Backend work, research, waiting on a third party, a big task that's only half done. The instinct is to pad the report or go quiet, and both cost you trust.
Report the invisible work as momentum. Name what's happening, why it matters to the outcome, and what it sets up next. \"Spent the week on the data migration\" means little to a client. \"Rebuilt the tracking so next month's numbers are finally reliable, live Friday\" tells them the same work is moving them somewhere.
When a task is genuinely mid-flight, say so and give the next checkpoint. A client can sit with slow progress if they can see it's still progress. What erodes confidence is silence, or a week of activity with no line on where it's heading.
Don't bury a slip at the bottom or save it for the call. Put it near the top: what happened, what it affects, and what you're doing about it. A missed date with a clear plan reads as a team in control. The same date left to surface on its own reads as a team that got caught.
And don't try to spin it. Clients usually know when they're being managed, or they sense it, and the moment they do you lose more trust than the bad news would have cost you. If something's gone wrong, say what it was, be precise, and say how you'll put it right. If you don't know something yet, say you're looking into it, then go and do it. And skip the jargon and the distraction. Clients aren't dumb, and dressing up a thin week just invites the questions you were hoping to dodge, which leaves you more on the spot, not less.
Weekly is the default, and the cadence that's easiest to hold is the one tied to a recurring check-in you already run. Fast-moving projects or anxious clients might want more, a steady retainer might be fine with less. Set the rhythm at kickoff so it's an expectation, not a favour you're doing.
Most of what belongs in the report was already said in the check-in. You just have to dig it back out and rewrite it, which is where the afternoon goes.
Supernormal takes notes in the background of the call, no bot on the invite, then its AI agent turns that conversation plus your project context into a draft status update in your voice. You review it, sharpen the risk line, and send.
Take a look at our client status report template to get a feel for what that could look like.
The writing stops being a from-scratch job and becomes a quick edit of something that already knows what happened. It's the same idea behind turning any meeting into a ready-to-review report.
It still needs that review. A draft built from the call can still fill in a date or an owner nobody agreed, so run five quick checks before you send.
Keep the same shape each week so the client learns where to look:
Green, amber, or red, with a one-line summary of where things stand
Moved this week: two to four bullets
At risk: the issue, and what you're doing about it
Needs you: the decision or input, with a date
Next: what's happening before the next update
Same skeleton every time, a few minutes to fill once the draft is in front of you.
You don't need a deck. A five-line email does it: where things stand, what you did, what's next, anything you need from them, and when you'll check in again. Send it the same day each week. For a solo client relationship, showing up on schedule matters more than the format, and it's the cheapest way to look reliable.
A good status report answers the question the client is asking and takes you minutes, not an afternoon. Start from the meeting you already had, not a blank page. Try the Supernormal notetaker to turn a check-in into a draft you can send, and if the weekly update has become a fixture, it's worth writing the whole thing up as an SOP so anyone on the team can run it.
", "url": "https://www.supernormal.com/blog/client-status-report", "title": "Client status reports: what to include and how to write them", "summary": "A weekly client status report should answer one question: are we on track. What to include, what to cut, and how to write it from your meeting instead of a blank page.", "date_modified": "2026-09-01T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "Client work" ] }, { "id": "urn:sha256:55d8ee640724b740c300638ef69d9c9446740a8b0fe49f7f7ba558952994a083", "content_html": "Key takeaways
SOPs fail because they're written once from memory and go stale in months. That's a systems problem, not a discipline one.
Start with the few high-cost processes, onboarding first, not everything at once.
Pull SOPs from the meetings where the work happens, so the doc matches reality and people trust it.
Give each one an owner, update it on the event not the calendar, and let decisions flow from the call into the doc so they reach the team undistorted.
The endgame: one place where context comes together, docs stay current on their own, and an agent flags conflicts before they hit the client.
Most agencies have SOPs. Most of them are out of date.
An agency SOP is just a standard operating procedure: the documented steps for how you run a repeatable job, from onboarding a client to sending a weekly report, so it comes out the same way whoever picks it up. That's the promise, anyway.
The reality is often a little more like this: someone senior loses a week to writing up how onboarding, reporting, and delivery are meant to run, drops it all in a shared drive, and presents the new system at an all-hands. It holds for about a month. Then a client asks for something the doc doesn't cover, someone finds a quicker way, a tool changes, and the process on the ground drifts off from the written one. A quarter later the SOP describes an agency that doesn't exist, and everyone's running on memory again.
The doc's still sitting there. It's just fiction now, and everyone knows it, which is why nobody opens it.
The deeper problem is that the context never stops moving. It moves from the bottom up, when your ICs find a better way and quietly change what they do. And it moves from the top down, when leadership makes a call that has to reach the people doing the work. That call gets passed from manager to lead to IC, and it loses a little something at every step, like a game of telephone. By the time it reaches the person who needs it, it's fuzzy. The SOP that should've caught it? Never touched. Nobody owns that gap, so it just sits there and grows.
So the usual advice, write better SOPs and be more disciplined, misses the point. That's not on your team. You've asked them to keep a second, hand-written copy of their own job up to date forever, on top of doing the job. Of course it rots. If you want SOPs people use, stop writing them from memory and start pulling them from where the work already happens.
This isn't only our take. When we asked agency leaders what separates the agencies pulling ahead in the AI shakeout, the answer kept coming back to one word: systems. Peter Emad of SalesCaptain said he'd \"build systems from day one. Repeatable workflows for research, data, content, outreach, reporting, and client delivery,\" so the agency \"isn't dependent on doing everything manually.\" Hannah Swinkin of Saint Boswell named the payoff: productizing your processes makes \"output more predictable and standardized across teammates,\" which \"gives teams more space for the real heavy lifting: strategy and creative thinking.\"
SOPs are how you build those systems. Which is what makes the state of most agency SOPs a problem: a system you can't keep current isn't a system. It's a document.
SOPs start out with such enthusiasm: \"We're going to note down our process and lock it in so it'll be so much easier going forward.\" The intention is always good, and for a while they're checked and followed.
But, SOP rot is inevitable. It's too long to read in the moment, so it gets skimmed. It lives in a drive nobody opens, miles from the tools where the work happens. The people following it didn't write it, so they've no stake in it. And nothing keeps it fresh, so every little change stretches the gap until the whole thing's make-believe.
Not one of those is a discipline problem. They all come back to the same thing: an SOP is a one-time snapshot of something that never stops moving. The snapshot ages. The work carries on.
You don't need to document everything, and trying to is half the reason these projects die in a spreadsheet. Start where not having a system costs you the most.
For most agencies that's the short list where a missed detail turns into a client problem: onboarding, weekly reporting and status, campaign or project delivery, and the handoffs in between. Onboarding is usually the one to start with. It sets the tone for the whole relationship, and it's where new-client chaos does the most visible damage. Nail that SOP and you've covered the moment clients are watching hardest.
The once-a-year, low-stakes stuff can wait. A living SOP for the ten things you do every week beats a dead binder covering a hundred you barely touch.
The best record of how you run a process already exists, in the transcript of the meeting where you run it.
Your kickoffs, delivery reviews, and handovers already hold the process, in plain language, with the exceptions and judgment calls that a from-memory doc always omits. So take it from there. With Supernormal taking notes in the background of those calls, no bot on the invite, its AI agent can turn that onboarding call into a first-draft onboarding SOP, one prompt away and ready for you to shape. You're editing something grounded in what happened, not sweating a blank template trying to remember step seven.
One version describes the ideal. The other describes the job. People follow the one they recognize.
If it's too long to read while you work, it won't get read. Aim for the least you need to let a capable colleague run the process without pinging you.
Give it a title that names the process, a one-line note on when to use it, an owner, and the date it was last updated. Then the steps in order, with the decision points and handoffs flagged, since that's where things go wrong. Link out to the templates, folders, and tools instead of pasting them in. Cut the padding. If a step needs a paragraph of why, that's a training note, not a step.
Don't underrate the last-updated date. It's the quickest read on whether an SOP can be trusted, and the first thing anyone checks before they'll follow it.
This is where most SOP systems fall over, so say it out loud instead of hoping. Four questions decide whether an SOP stays true.
Every SOP gets a named owner, usually whoever runs the process. But owning isn't writing. Their job is to review and sign off a change, not to remember to write it up from scratch, because that's the bit that never happens.
On the event, not the calendar. It should change the moment the process changes, which is nearly always a specific conversation: the leadership call where a decision lands, the retro where you agree a new step, the client call where scope moves. A quarterly review is a safety net, not the plan.
This is the Telephone problem, and it's the one that matters. Instead of a decision getting watered down as it's passed along from memory, catch it at the source. If the leadership call where it's decided is already being turned into notes, one prompt turns that decision into an updated draft of the SOP, in the words it was said, ready for the owner to approve. The doc becomes the channel, so the message stops getting mangled on the way down.
You can't enforce your way to people following an SOP, which is why \"be more disciplined\" never works. What you can do is kill every excuse to ignore it: keep it up to date, keep it short, keep it where people already work. They'll follow it once it's worth following. Accuracy is the enforcement.
Every agency runs on knowledge that lives in people's heads and never lands in a doc. Shared knowledge, institutional knowledge, the how-we-do-it-here a new hire soaks up over months and a leaver takes with them. And it doesn't only leak when someone resigns. A client pauses and comes back to a different team. An account moves from one consultant to the next. Someone writes a process up the week before they go, in a hurry, badly. Each of those is a moment the agency loses a piece of what it knows, and it shows up in revenue: AgencyAnalytics' 2026 benchmark ties 24% of client churn to misaligned expectations at onboarding, the kind of gap that opens when the knowledge didn't carry over.
The classic fix, a panicked documentation sprint in someone's last two weeks, produces exactly the rushed, idealized SOPs that rot fastest. Better to capture as you go, so it's written down long before anyone hands in their notice. When the meetings where work happens are already becoming living docs, an exit is a handover of context you already have, not a dig through someone's inbox. The knowledge stays with the agency instead of walking out with the person.
Fresh SOPs are table stakes, and a better template won't get you there. A template still leaves the updating to you, and updating by hand is the part that breaks. What holds up is documentation that stays current from the work: one place where context from every stream lands together, client calls, internal decisions, project updates, the threads where decisions get made. Once it's in one place, updating the docs that depend on it is a prompt, not a project: you refresh the SOP from the meeting where the change happened, instead of waiting on someone to remember to reconcile it. That's a single source of truth in the plainest sense: the agency's shared knowledge, its IP, held in one place instead of scattered across inboxes, drives, and people's memory, building up over time instead of leaking away every time someone leaves.
The bigger prize is where this is heading. With the context in one place, an agent can watch for conflict: a decision in this morning's leadership call that cuts against a documented process, a new client promise that collides with a timeline you already agreed, two workstreams heading for the same person in the same week. It flags the clash while it's still cheap to fix, so you deal with it before the client ever feels it. That's the jump from documentation that records what happened to a system that guards the work while it's happening.
There's a bigger shift forming behind that. As agents get more capable, the goal moves from a doc a person can follow to an instruction an agent can run the same way every time: no lost links, no skipped steps, no retraining a new hire, no one leaving with the process in their head. That's still the direction, not where the product is today, but the groundwork is identical: get the process out of people's heads and into one place that stays current.
Writing up how your agency works shouldn't mean losing a week to write fiction. Supernormal takes notes in the background of the calls where your processes run, then its AI agent turns them into drafts you review and send, from onboarding SOPs to status updates to project docs. You keep the judgment. You skip the blank page. Start with the Supernormal notetaker, and have a look at how agencies manage projects with an AI agent and what a strong client kickoff looks like.
", "url": "https://www.supernormal.com/blog/the-trouble-with-agency-sops", "title": "Agency SOPs: why they fail and what works", "summary": "Most agency SOPs go stale within months. Why that happens, which to build first, and how to pull SOPs from the meetings where the work happens.", "date_modified": "2026-08-26T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "AI and agents" ] }, { "id": "urn:sha256:241db205c1ac23481c54bb42ec4a1a54c50a9b8e1de94094ff2a6c2abc791989", "content_html": "Having microphone issues in Google Meet is frustrating, especially when you need to actively participate in an important video call. But don't worry - with some simple troubleshooting, you can resolve the problem and get your microphone up and running again quickly.
In this post, we'll walk through the potential causes of and fixes for a non-working mic in Google Meet on both desktop and mobile. Follow the step-by-step instructions below to identify and resolve your particular microphone problem.
The first thing to check is your microphone settings within Google Meet. Make sure your mic isn't muted and the correct input device is selected.
To check this on desktop:
Click on the microphone icon in the bottom left corner of the meeting window.
Make sure there's no red slash through the mic icon, indicating you're not muted.
Click the ^ arrow next to the mic icon to select your input device. Make sure the correct mic is highlighted.
On mobile:
Tap the three dot \"more\" menu in the top right of the meeting screen.
Tap \"Settings\" then \"Audio and Video\".
Make sure microphone isn't turned off.
Select the microphone you want to use.
If the incorrect input device is selected, switch to the proper mic and test it out. This simple fix solves many mic issues.
If the microphone settings look correct but your mic still doesn't work, the problem may be your physical hardware setup. For a USB microphone or headset, check that the USB cable is fully plugged into your computer. Try unplugging it and reconnect it securely.
If you're using your computer's built-in microphone, make sure there's nothing physically blocking it. The mic is usually a tiny hole located near the webcam.
Finally, inspect your cables for any damage or kinks. A faulty cable can cause microphone connectivity issues. Consider replacing the cable if it looks worn.
Google Meet requires your permission to access your microphone, especially when using the app for the first time. Make sure you've allowed Google Meet to use your microphone.
On desktop, look for a browser permission prompt in the URL bar or on the left side of the Omnibox that asks for permission to use your mic and camera. Click \"Allow\".
On mobile, go to your phone's app permissions settings and make sure Google Meet is allowed access to the microphone. You may need to toggle the permission off and on again to refresh it.
If access was previously blocked, granting permission should resolve Google Meet microphone problems.
Even when Meet and your browser look set up correctly, macOS can quietly block microphone access at the system level. Turn it back on there first.
Open the Apple menu and go to System Settings, then Privacy and Security, then Microphone. On older versions it's System Preferences, then Security and Privacy, then the Privacy tab, then Microphone.
Turn on the toggle next to the browser you use for Meet, such as Chrome or Safari.
Quit the browser fully and reopen it, then rejoin your meeting.
If the browser wasn't listed, open Meet once and allow the microphone when prompted, and it'll appear in the list.
If you blocked the microphone earlier, Chrome remembers that choice and Meet can't ask again. Clear it in site settings.
On the Meet tab, click the tune or lock icon at the left of the address bar.
Find Microphone and set it to Allow.
Reload the page.
If it's still blocked, type chrome://settings/content/microphone in the address bar, remove meet.google.com from the Not allowed list, and reload Meet. For a deeper audio walkthrough, see our guide to fixing Zoom audio, which covers the same browser and device checks.
Windows has a buried privacy setting that blocks browsers from the microphone even when every other permission looks correct. It's the most common Windows cause.
Open Settings, then Privacy and security, then Microphone.
Turn on Microphone access.
Turn on Let apps access your microphone.
Turn on Let desktop apps access your microphone. Your browser counts as a desktop app, so Meet stays blocked until this is on.
Reload Meet and check your mic.
One simple fix for any technology glitch - restart it! Restarting your computer or mobile device essentially refreshes the software, clearing out any errors.
Follow these steps:
For a PC, go to the Start menu and select \"Restart\".
For a Mac, go to the Apple menu and click \"Restart\".
For mobile, power the device off completely and turn it back on.
Once restarted, launch Google Meet again and check if your microphone works. A restart cleans up temporary glitches.
If you use the Google Meet web app in Chrome, a misbehaving browser extension could be the culprit. Open your list of Chrome extensions and disable any that could affect microphone access, namely ad blocker extensions.
Then, try Google Meet again. If the mic starts working, turn extensions back on one-by-one until you identify the problematic add-on. Remove that extension for good.
Note your Supernormal Chrome extension will never cause mic issues, so you don't need to worry about that one!
Outdated audio drivers can cause microphone malfunctions. Updating them is an easy fix:
On Windows:
Open Device Manager > Sound, video and game controllers.
Right click your audio device and select Update driver.
Search automatically for updated driver software and install it.
With updated drivers, check Google Meet again. The mic should work properly now.
If your primary microphone isn't working, switching to another input device is worth trying for a quick fix:
Plug in another USB mic or headset if available. Or,
Use your phone's mic: join the Google Meet on your phone and use Call In or Hand Raise features.
This workaround allows you to join the meeting until you can troubleshoot the primary device issues.
Meet often defaults to your computer's built-in microphone even while your AirPods or Bluetooth headset play the audio, so it looks connected but no one hears you.
In the meeting, click the arrow next to the microphone icon and select your headset as the input device.
If the headset isn't listed, confirm it's connected in your device's Bluetooth settings, then reconnect it.
Set the headset as the default input in your sound settings: on Mac, System Settings, then Sound, then Input; on Windows, Settings, then System, then Sound.
If audio cuts out mid-call, switch the output to your built-in speakers and back, which resets the connection.
Mobile mic problems are almost always an app permission. Grant it directly, then refresh it.
On Android: open Settings, tap Apps, tap Meet, tap Permissions, tap Microphone, and choose Allow. If it's already on, toggle it off and on again to refresh.
On iPhone: open Settings, scroll to Meet, and turn on Microphone. Toggle it off and on if it was already enabled.
Then reopen Meet, rejoin, and make sure you're unmuted. For more on mobile meetings, see our guide to joining Google Meet on your phone.
As a last resort, resetting Google Meet can force the app to reestablish permissions and settings required for microphone access.
On desktop:
Go to meet.google.com
Click on your profile icon > Help & Feedback > Reset Google Meet
On mobile:
Tap your profile icon > Settings > Reset Google Meet
After resetting, sign into your Google account again, rejoin your meeting, and check if the microphone works properly. Resetting may clear up stubborn glitches.
Meet lets you check your microphone before you join, so you never start a call unsure.
On the green preview screen before you join, look for Check your audio and video.
Speak and watch the input meter move, which confirms Meet is hearing your selected microphone.
To switch devices, use the settings there to pick a different microphone, then test again.
You can run the same check mid-call from the meeting settings under Audio.
And that covers the top ways to troubleshoot and fix a Google Meet microphone that isn't working! In most cases, the problem is caused by an incorrect setting that can be quickly resolved. But for hardware-related issues, you may need to replace a faulty mic or cable. Hopefully, these troubleshooting tips get your microphone up and running again swiftly.
Once your mic is working, you can focus on the meeting instead of your notes. Supernormal takes notes in the background without a bot on the call, then its AI agent turns them into follow-ups, summaries, and project docs you review and send. Try the Supernormal notetaker, no credit card required on our generous free tier.
", "url": "https://www.supernormal.com/blog/google-meet-microphone-not-working", "title": "Microphone troubleshooting for Google Meet", "summary": "Troubleshoot Google Meet microphone issues to fix audio problems. Check permissions, settings, and device compatibility for clear calls.", "date_modified": "2026-08-24T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "Meetings" ] }, { "id": "urn:sha256:8fcd7cc7baceaa88407826a94576925636d3a0a90b0e538bb78bae64da1fa9a7", "content_html": "There are two reasons people land on a page like this. Either you're tidying up after a trial and you want Fathom off your machine, or a client just asked why a notetaker was sitting in your meeting and you need it gone today.
Both are covered below. One thing to know before you start: dragging the Fathom app to the trash doesn't stop it turning up on your calls. Fathom runs off your connected calendar, so the app and the calendar link have to be dealt with separately. If you don't remove the calendar connection, the notetaker keeps showing up, which is why Zoom's community forum is full of people asking why it won't go away.
Everything below reflects Fathom's own help documentation as of August 2026. Where Fathom's instructions no longer match the current Zoom or Google interface, we've flagged the path that actually works.
Quick check before you go further: this guide covers Fathom the meeting notetaker, at fathom.video. There are two other companies called Fathom, one doing web analytics and one doing accounting software. If your billing emails don't come from fathom.video, you're in the wrong place.
If you have a meeting soon and you just need Fathom to stay out of it, do this first. It takes a minute and it works no matter which video app you use.
Open your Fathom settings at fathom.video/customize
Find the auto-record dropdown and set it to No meetings
Fathom will then only record when you start it manually. Fathom's help center also confirms the notetaker can't attend on its own: it can only record a Zoom, Microsoft Teams, or Google Meet call if you're on the call too, and it exits when the meeting ends. So it won't sit in a client call you didn't attend.
The catch - Turning off auto-record leaves your account, your recordings, and the app itself exactly where they are. Keep going for a full removal.
One more setting worth checking if you use Slack: Fathom's newer releases can also pick up Slack huddles, controlled by its own separate toggle in settings. Turning off calendar auto-record doesn't cover that.
Quit Fathom completely. Right-click the icon in your dock and choose Quit, or use Activity Monitor to force quit if it won't close
Open your Applications folder and drag Fathom to the trash
Empty the trash
Open System Settings, go to General, then Login Items, and remove Fathom from the list so it stops opening at startup
Two leftovers worth clearing. If you installed from a disk image, Fathom may still be mounted under Locations in your Finder sidebar, so eject it. And Fathom writes log files to ~/Library/Logs/Fathom, which you can delete safely.
Quit Fathom, including from the system tray
Open Settings, go to Apps, then Installed apps. On Windows 10 this is labeled Apps and features instead
Find Fathom, open the menu next to it, and choose Uninstall
Fathom stores its app data in %APPDATA%\\FathomVideo, which you'll find at C:\\Users\\[YourUsername]\\AppData\\Roaming\\FathomVideo. Delete that folder to clear the leftovers.
Fathom's Chrome extension exists so it can intercept Google Meet links and sit in on unscheduled calls. Removing the app doesn't remove the extension.
Look for the extension icon to the right of your address bar, right-click it, and choose Remove from Chrome
If you can't see the icon, open the three-dot menu at the top right, go to Extensions, then Manage extensions, find Fathom, and click Remove
Check the publisher - The one you want is \"Fathom AI Note Taker for Google Meet\" by Fathom Video Inc. There's an unrelated extension listed simply as \"Fathom\" by a different developer, so read the publisher name before you remove anything.
This is where most guides are out of date, including Fathom's own. Fathom tells you to go to Manage, then Installed Apps. Zoom retired that path. Here's the current one, per Zoom's documentation:
Sign in to the Zoom App Marketplace
Click My Library in the bottom left corner
Find AI Notetaker by Fathom, click the More icon in the Actions column, then click Remove
Confirm by clicking Remove again
If you're on a Zoom account managed by someone else, you may not be able to do this yourself. Multi-user Zoom accounts have app pre-approval switched on by default, so removal may need your account owner or admin. Admins remove it for everyone through Manage, then Admin App Management, then Apps on Account.
If Fathom is still intercepting your Zoom calls after all that, Fathom's own advice is to update Zoom to the latest version, which clears the intercept from your account.
Fathom's Google Meet integration is a calendar connection plus the Chrome extension, so this is the step that actually cuts it off. There's no separate Fathom-to-Calendar permission to revoke, it's all one link.
Go to myaccount.google.com/linkedapps
Select Access to your Google Account, then select Fathom
Click See details, review the access, then click Remove access and confirm
Fathom's privacy policy is clear about what happens next: deauthorizing Fathom through your Google or Outlook account settings disables your Fathom account until you reconnect a calendar, because the calendar link is core to how the product works.
That makes this the single most effective step on this page. If you only do one thing, do this one.
Fathom doesn't appear to install as a Teams app. Its Teams support works by watching your connected Microsoft calendar for meetings with a Teams link, then joining through that link as an anonymous participant. Several guides tell you to block or uninstall Fathom in the Teams admin center under Manage apps. Admins report it isn't listed there, so that path won't work.
What does work is revoking its Microsoft 365 permissions:
Go to myapplications.microsoft.com
Hover over Fathom, open the menu, and select Manage your application
Click Revoke Permissions
Microsoft notes one limitation here: permissions your administrator consented to on your behalf can't be revoked by you. If Fathom was approved at the organization level, you'll need your IT admin to remove it in Azure AD under Enterprise applications.
If you're an admin trying to keep notetakers out of Teams meetings generally, the lever Microsoft points to is switching off \"Anonymous users can interact with apps in meetings\" in your meeting settings, alongside join verification checks.
Log in to Fathom and go to Settings
Scroll to Premium Features and click Downgrade to Free, or go straight to fathom.video/manage_subscription/billing
Click Cancel Plan
Your subscription then runs to the end of the current billing period with no further charges. If you need it canceled immediately, Fathom asks you to email help@fathom.video. Team plans can't be canceled this way at all, those go through success@fathom.video.
Canceling is not deleting - Downgrading to free leaves your account, transcripts, and recordings in place. Deleting them is a separate job.
Go to fathom.video/customize
Scroll to the bottom of the page and click Delete Account
Fathom's help center says the request takes around 12 hours to process, after which account data is permanently deleted and can't be recovered. Its privacy policy gives a longer window, committing to commercially reasonable efforts to delete recordings and personal information within 30 days of the request, without guaranteeing that timeframe. If deletion timing matters for a client confidentiality reason, plan around 30 days rather than 12 hours.
The privacy policy is also explicit that nothing happens by default: if you stop using Fathom, your account and the personal information in it stay put unless you actively delete the account.
To remove individual calls rather than everything, open the recording, hover over the three dots next to the Share button, and click Delete Call. Deleting a call also removes all its notes, and it can't be undone.
Individual users on a team plan don't get a Delete Account button. You'll need your team admin to do it. Admins work from Settings, then the Users tab, where there are two different actions worth understanding before you click:
Removing a member takes them out of the team immediately, and any recordings they shared are removed with them
Deactivating a member blocks their login but keeps all their recordings accessible to the team
Account-wide retention policies aren't self-serve. Fathom asks Enterprise customers to email help@fathom.video to set one up.
The reason this page gets so much traffic from frustrated people isn't really about uninstall steps. It's the bot.
When a notetaker joins a client call as a visible participant, your client sees it in the participant list. Fathom's help center confirms that hiding the recording banner doesn't hide the bot from that list. On an internal standup, nobody minds. On a first pitch with a new client, or a call where someone is about to say something candid about a competitor, it changes the room. And you're the one who has to explain it.
Supernormal takes a different approach. It transcribes your meetings in the background from your own machine, so there's no extra participant and no permission conversation at the top of the call. We've written more about that trade-off in bot versus no bot, and the Supernormal notetaker page covers how it works.
If you're switching rather than just switching off, the thing to look at is what happens after the call, not the transcript itself. Every notetaker gives you a transcript and a summary. The write-up is only the start of the job.
With Supernormal, your meeting notes become the context for the actual work. Ask for a campaign brief from the kickoff call, a set of Slides for the internal review, a project plan, or the follow-up email, and you get a draft built on what was really said, ready to polish and send. You're reviewing work rather than starting it from a blank page.
Getting started takes a couple of minutes, and you don't need to have finished the Fathom cleanup first. Just make sure you've turned Fathom's auto-record off, or you'll end up with two notetakers on the same call.
", "url": "https://www.supernormal.com/blog/uninstall-fathom", "title": "How to uninstall Fathom from Mac, Windows, Zoom, and Meet", "summary": "Uninstall Fathom from Mac, Windows, Zoom, Google Meet, and Teams. Plus the one step that actually stops the notetaker joining your calls.", "date_modified": "2026-08-18T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "Meetings" ] }, { "id": "urn:sha256:8091addf9fa71dadce3e58faa8ce667289617b3e88887ef1a223f23c2929d28c", "content_html": "You just finished a client scoping call. It went well, everyone agreed on the shape of the project, and now the real work starts: turning that conversation into a plan your team can actually run. Usually that lands on one person late in the evening, rebuilding a half-remembered call into tasks and owners, and hoping nothing important got lost.
With Supernormal, it takes a prompt. Here's how to turn a scoping call into an owned, phased delivery backlog, with no bot on the call and without losing an evening to it.
Before you can break a call down, you need a complete record of it, not the half you managed to type while also trying to listen.
Supernormal sits in the background and takes notes on the call for you, with no bot joining the meeting. Nobody on the client side sees a strange participant appear, and nobody on your side is stuck transcribing instead of paying attention. The moment the call ends you have the full transcript, a summary, and the action items. That is the raw material your backlog gets built from, and the cleaner it is, the better the plan.
This is the step that used to eat an evening. In the Supernormal web app, open the meeting and ask the agent to break the scope into a delivery plan: phases, tasks under each phase, an owner for each phase, and any open questions the call left unresolved. Because the agent is working from the transcript, it's context-aware. It knows what was agreed, what was flagged as a risk, and what was left hanging.
Here's what that looks like on a real kickoff, anonymized. A digital PR agency has just run a 30-minute call with a new client to scope a Wikipedia page for the client's founder. The conversation covered whether the founder is notable enough to qualify, what counts as success, the client's thin starting position on independent coverage, and who would do what. Asked to turn that into a backlog, the agent returns something like this:
Audit the founder's existing coverage and references to see whether they clear Wikipedia's notability bar
Flag gaps and, if the bar isn't met, outline what would close them
Confirm the realistic timeline for the assessment with the specialist
Owner: agency
Share a starter list of existing mentions, articles, and press the founder already has
Expand it into a verifiable list of independent, reliable sources
Draft the page against agreed claims and Wikipedia's guidelines
Owner: agency, with the client supplying the starter list
Client review of the draft for edits and additions
Final revisions and submission for review
Owner: agency, with the client reviewing
Does the founder actually clear the notability bar, or is groundwork needed first
How do we handle the naming overlap with the other public figure of the same name
Who's the single point of contact on the client side for approvals
That's a working plan, drawn straight from the conversation, in moments. It even surfaces the awkward questions the room glossed over, which is usually where projects go wrong.
The agent gives you a strong first draft of the plan. Your judgment makes it right. This is the stop doing, start reviewing part, and it's where your experience as a delivery lead comes in.
Is anything sequenced wrong, where a later task actually has to happen first
Did anything land as \"the team\" rather than a person, and did the client pick up tasks they haven't actually agreed to
Does the plan assume client turnaround that never happens that fast, and are the phases sized to how your team really works
Did the call hint at work that isn't in the agreement, and does it need flagging before it quietly becomes your problem
You're not rebuilding the plan. You're correcting a good draft, which is a far smaller job than writing one from a blank page.
A backlog in a doc is still just notes. The last step is getting it into the place your team actually works, with owners assigned and a first check-in on the calendar.
Move the tasks into wherever your team tracks work, whether that's Linear, Asana, a Trello board, or a shared spreadsheet, assign each one, and set the date for the first status check while the plan is still warm. The client sees momentum in the first day rather than the first week, and your team starts from a shared, context-driven backlog.
The call ends, and instead of a transcript nobody has time to read, you have an owned, phased backlog ready to go, honest about what's still unresolved. Supernormal handles the reconstruction, so your time goes to the judgment that actually needs you: the sequencing, the risks, the client relationship.
Turn your next scoping call into client work, in a flash.
", "url": "https://www.supernormal.com/blog/scoping-call-to-backlog-ai", "title": "Turn a scoping call into a delivery backlog with Supernormal", "summary": "How to turn a client scoping call into an owned, phased delivery backlog with Supernormal. Capture without a bot, then let the AI agent build the plan.", "date_modified": "2026-08-11T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "AI and agents" ] }, { "id": "urn:sha256:e77f757912928e900b10de6c29ed1fc09589446af60f39d09a5edb9d31fe79ff", "content_html": "Picture the scene: You sit down to draft a proposal for a client. Or a pitch deck. Or a strategic framework. You know how your agency prefers to do this type of work. There may even be a template. But you can't shake the feeling that you've made something very similar to this before. Something great that you were proud to put in front of the client. But you can't remember which client it was for, or what project it was part of. Or maybe you can, but you still can't find the files. So you slog through the hours to get the work across the line again, suspecting that the last time you did this work it was even better, and the time spent starting from scratch is a huge waste.
This is what knowledge management actually looks like at an agency. It’s not an abstract strategy, but the daily cost of not making the most of what you already have. Making the most of past work isn't a new problem for agencies, but AI is making it one that is both more important and much easier to solve. Everyone has access to the same AI tools, but those tools are only as good as the context you feed them, so a repository of more, better reference work creates a sustainable competitive advantage in a market that's never been more competitive. Previously, building a database of past work relied on everyone at the agency diligently filing work in the correct way. It became a to-do item that often slipped off the list when more urgent priorities came along. This article is a practical guide to unlocking the value of your previous work for current clients, so you and your clients save time, and you maximize the value of every AI tool credit you spend.
If knowledge management used to mean folders, naming conventions, and a wiki nobody updated, in the AI era it means something more useful: context. \"Context\" and \"taste\" are hot buzzwords these days as everyone races to find ways to use AI to do distinctive work without starting from scratch every time, or prompting for longer than it would have taken to do the work yourself. Practically, there are three types of context most AI tools need to create outputs:
The prompt itself. The instructions, examples, and framing you give in the moment. \"Write a LinkedIn post\" versus \"Write a LinkedIn post for agency leaders, in my voice, arguing X\". The second provides more context.
Supplied materials. Documents, data, brand guidelines, past work, meeting transcripts. Anything you feed in so the AI has background on your situation rather than the average background it can access (usually from the internet).
Ambient/system context. Things the AI has access to without you pasting them: connected tools (your CRM, docs, calendar), memory of past conversations, knowledge of your role and preferences.
Writing a great prompt is a real skill, and even if you and your agency colleagues happen to be very good at it, it's almost impossible to provide all the nuanced information needed to deliver a high-value, relevant output from a prompt alone. Supplied materials make it more efficient to provide a lot of rich information, quickly. However, it takes work to gather all the relevant documentation and feed it to an agent. Doing this every time you prompt is time-consuming, and repetitive if what you're supplying is relevant to every task for that client.
The real unlock is in the third type of context: ambient/system context. By giving AI access to connected tools, reference documentation about specific clients and projects, and agency-wide guidelines and frameworks, you can keep output high-quality and on-brand without supplying new context every time.
So how do you get your agency consistently working from ambient/system context in your tools? Lots of individuals invest in creating a \"personal knowledge management system\" to help them with daily tasks, but creating one for an entire organization can be a little more complex. Here are the steps to take to lay a foundation for more effective use of context:
Where does your agency's work live? These are the typical categories of tools where your context already exists:
i) Document repositories, such as Google Drive, Notion, or Dropbox. Hopefully, a lot of the documentation of your past work is there. Don't worry if the data is in multiple formats like video files, PDFs, documents, and spreadsheets. AI can usually read all of these.
ii) Written client comms. The agency probably has a standard email platform, often Gmail or Outlook. Usually you'll also have a messaging tool, such as Slack or Microsoft Teams. These are excellent sources of more qualitative information, like feedback on past projects that can inform current work.
iii) Meeting notes. If you're reading this article, you hopefully already use an AI notetaker tool, like Supernormal's Notetaker. If not, perhaps you have an alternative way of storing meeting records? Meeting notes are often an even richer source of qualitative context than email, as they contain information both about the delivery of the work, and the reaction clients had to it.
iv) CRM and/or accounts. This may be no more complex than a spreadsheet, or maybe you have a full Salesforce instance. Whichever suits your scale, you almost certainly have tools to manage relationships and track revenue and costs. This context allows you to overlay business context onto your work, so AI can help you prioritize.
v) Design tools and brand guidelines. Maybe you use Figma, Canva, or specialist presentation tools like Pitch. Tone-of-voice guidelines for your agency and clients may live in a standard document, but images and design standards are often in dedicated design tools.
vi) Project management tools. Systems like Asana, ClickUp, Monday.com, or something you built yourself in Notion hold all the information about how your agency's work actually gets done. Who works on what, how you break down projects into tasks, and which deadlines are hit and missed.
As you audit your tools, it's likely you'll find opportunities to consolidate where data lives, or realize that certain types of information haven't been consistently captured. Now is a good time to do a one-off cleanup. Don't worry too much about perfectly structuring where data lives. AI is often better than humans at working with unstructured data. However, you will likely want a clear way of differentiating which context relates to which client, and which relates specifically to your agency. That way you can ensure that you're never using inappropriate context from one client to inform work for another.
After Step 2 you should know both where your context is, and roughly how it's structured.
As you clean up your different \"buckets\" of context, it's normal to feel a bit frustrated about how messy everything is, or where there may be gaps because some things weren't consistently captured. There are diminishing returns in trying to fix this, so focus instead on how and where information gets captured from now on. The rule that matters: knowledge you have to file by hand never gets filed, but knowledge captured automatically as you work does. Make sure everyone at the agency is using the same meeting notetaker and storing meeting notes consistently for example.
Now that you know where your context lives and have a plan in place to keep it fresh, you can start putting it to work. Every AI model or tool will have a different name and way to set up the connection between the tool and your context, on both an individual and an organizational level, but broadly speaking there are three ways to feed AI context:
How to do something. Think of this as a kind of permanent prompt. You give the AI context on how your agency prefers to do things, for example tone-of-voice guidelines, or a strategic framework you always use. Claude calls these \"Skills\", but other tools may use other wording, for example \"Design Guidelines\" in a design tool. Think of this type of context as a way of avoiding having to write the same thing in the prompt box over and over again.
Examples to inform outputs. When you want the tool to reference past work, you can connect a broad set of context to inform the output. You aren't telling the AI how to do something, but giving it examples to help guide its work, just like you would a colleague you're briefing on a new project. This is where finished work earns a second life. Treat your best briefs, proposals, and recaps as templates and reference material, not one-offs.
Permission to execute in tools. Beyond supplying guidelines and background on the work, you can also give AI permission to edit things in other tools. For example, you can tell the AI to update your CRM based on a recent customer call. With this kind of connection you close the loop, and agents can continue building your context as well as referencing it to produce better work.
For each AI tool your agency is working with, decide what type of connector you want in place, and which context should be referenced where. Think through which connectors should be in place for all team members, and which will be the responsibility of individual users to set up. The heading illustration for this blog post may help you think this through.
Most AI tools have a way of keeping certain workstreams, and therefore work context, separate. In both ChatGPT and Claude, this is called a \"Project\". For agencies, it is often extremely important to keep context from one client separate from another's. Savvier clients may even have clauses about this in their contracts with you. Determine who at the agency is responsible for maintaining and monitoring Project setup in your tools, and ensure the walls between client contexts are as immune as possible to natural human error.
As with any change in workflow, best practice is easier to design than it is to implement. You and your colleagues have existing habits, and these may have to change for you to start reaping the benefits of referencing agency context with AI. Agency leaders we've spoken to had the following advice for anyone setting up new ways of working with AI:
Start small. Run a pilot project with one client or team to test what works before rolling new processes out agency-wide.
Work with, not against, team preferences. If team members love using a certain tool, forcing them to adopt a new one is unlikely to be successful. Help people use context more effectively where they already like working.
Make things visible, and social. Create team rituals that demonstrate how work was made with agency context, not just the end result. Encourage people to show the failures as well as the successes so the whole team can learn.
Leaders have to walk the walk. Make sure leadership is also demoing their workflows, and following new ways of working that integrate context-powered AI.
Be careful what you measure. Billable hours are no longer the prime measure of value in the agency world, and the whole point of using AI more effectively is to improve agency margins by decoupling client value from time spent. Tracking credit usage can also be counterproductive. Think about what you're celebrating at your agency, and the practical implications for team progression and financial rewards.
AI is disrupting the agency business model, and creating a lot of uncertainty. When margins are squeezed and growth is hard to find, it is natural to fear new ways of working. But AI offers agencies a huge opportunity: to turn past work into a sustainable competitive advantage. Case studies of previous campaigns may have won you pitches in the past; now that work can help you deliver higher-quality, more distinctive work in the future as well. There's a reason Y Combinator, the world's most prestigious startup accelerator, is now requesting applications from AI-native agencies. The asset is already there, sitting in your Drive, your inbox, and your meeting notes. Now you can turn those files into fuel.
Looking for the easiest possible way to get started? Try Supernormal's free meeting notetaker, then use an agent with access to all that meeting context to complete your next steps for you.
", "url": "https://www.supernormal.com/blog/agency-knowledge-management", "title": "Agency knowledge management with AI: a practical guide", "summary": "Your agency already owns its most valuable asset: past work and decisions. Here is how an AI agent turns scattered knowledge into reusable, searchable IP.", "date_modified": "2026-07-16T00:00:00.000Z", "author": { "name": "sarah-kiefer" }, "tags": [ "Client work" ] }, { "id": "urn:sha256:23da34134fec7a09a383c0caf9547c8b6639be7e4884d91fc066adfe24544032", "content_html": "Reports of the death of the billable hour have long been exaggerated, but AI will finally make it a thing of the past. AI has broken the correlation between time and value, and every agency leader still pricing services by the hour now has no choice but to act. Clients expect agencies to deliver unprecedented, AI-powered speed and efficiency, yet hourly billing remains the standard pricing strategy for 28% of agencies.
We are now in the era of hourly billing actively working against you. If your team delivers faster thanks to clever use of AI, you earn less, not more. And this is happening while agency margins have already declined to an average of 10% from a golden-era high of 30%, and agency headcount is forecast to fall 15% in 2026, after dropping 8% last year.
Perhaps you know you need to stop billing by the hour, but you're struggling to change things. In this article we'll look at how we got here, what AI changes, and an actionable plan for adapting your pricing and operations so AI improves rather than erodes your margins.
Billing by the hour became standard practice for the pioneers of the agency business model in the mid-20th century. Understanding why a cost-plus approach became the standard helps when thinking through how and why to change things now.
Measurable: The value an agency delivers was hard to quantify accurately, and often arrived over a long period. It made sense to price on something easy to understand and count immediately: hours worked.
Comparable: The agency business has low barriers to entry, so the market is highly competitive and clients like to compare pricing from several providers. A standardized unit of value helped them compare apples to apples.
Protective: When scope is unclear or the client is new, hourly billing protects the agency from underestimating effort. Fixed fees carry more financial risk for a business whose biggest overhead is staff salaries.
Professional services precedent: Many features of the agency business model were copied from legal services, already large and established when agencies started to spring up.
Agencies are under pressure from clients to use AI to deliver more for less, and to use agents to complete creative and operational tasks faster and cheaper. If an agent can mock up 7 campaign ideas in minutes, why would a client pay for hours of a senior creative's time? In reality, the value lies in the past work and references that give the agent context, the prompt a senior creative writes, and the tweaking, filtering, and taste applied once the agent has done its work. Clients are paying for years of experience, institutional expertise, and taste. That value is just harder to quantify in minutes spent on this specific project.
New ways of working with AI undermine all 4 of the original premises for choosing hourly billing. Agencies' impact is now easier to measure and predict, in large part thanks to AI-powered marketing and brand measurement tools. All agencies have equal access to the same models, so everyone is equally incentivized to set a new pricing standard that still makes comparison easy for clients. AI helps agencies learn about clients faster, estimate effort and margin accurately, and prevent scope creep. And other professional services industries, such as Law, are moving away from the billable hour fast as they face the same disruption.
AI is also disrupting agencies' cost bases. Powerful models can be very expensive: Uber blew through its annual budget for Claude Code tokens in 4 months when it incentivized engineers to use the tool as much as possible, and Bryan Catanzaro, VP at Nvidia, has said AI often costs more than employees right now. Where salaries used to be agencies' biggest overhead, you may soon spend more on credits than on payroll.
In the end, most clients don't care whether a task was completed by a human or an agent. They care about the end result, and the price they pay to get there. Your new challenge is to price and package your work in a way that is tied to outcomes, easy for clients to understand and compare, protects your margin from unexpected costs, and won't break your internal operations. Simple, right?
![\"\"](\"https://framerusercontent.com/images/rYGttLNlqm5WQ0nPZQqrBPLF2UI.webp\")
Ignition, a revenue operations platform for agencies, surveyed 273 managers and executives at branding, creative, digital, marketing, PR, social media, and web agencies, and found 4 main pricing models in use: hourly billing, productized (incorporating subscriptions and tiered bundles), project-based, and retainer-based. Here are the pros and cons of the three main alternatives to hourly billing, so you can pick the right one for your agency.
Packaging up a range of services that achieve a defined outcome for a client, one-off or ongoing, e.g. social media campaign delivery, website maintenance, visual asset design.
Pros: Lends itself to subscription payments, making cashflow more predictable. Clear deliverables and tiered options make it easy for clients to understand what they're getting and flex up or down.
Cons: Unless outcomes and the path to them are tightly defined, scope creep risk is high. You need to know exactly how each offering is delivered, or every \"product\" ends up looking different and becomes hard to manage consistently.
You deliver a one-off, clearly defined project, such as a campaign or digital product build.
Pros: A good path into longer-term work with the client, and often where the most creative, award-winning projects happen because you get to think big.
Cons: Hard to forecast how many you will sell, and because every project differs, hard to price competitively without risking a thin margin if unexpected costs arise.
A client pays you to take on a pre-defined chunk of their operations on an ongoing basis.
Pros: Predictable cash flow gives you confidence to hire and retain staff. You build deep client relationships and expertise you can sell more services against.
Cons: Successful ongoing work eventually leads to a conversation about bringing it in-house, and retainer work can be repetitive and unlikely to win your agency attention.
So you know you need to move away from billing by the hour, and you have a good idea of which alternative best suits your agency's work. Now the hard part: changing how you sell and deliver. Here are the 4 crucial steps to making the shift stick, and improving your margin in the process.
To see where your most profitable work happens, where margin erodes, and where new AI processes could help, track how the packages you sell line up against the work you deliver. Track staff hours as an input, but also track AI credit usage by project, and gather qualitative feedback from staff and clients as work progresses. Just as importantly, this record becomes your proof of work: a clear account of what was decided, made, and delivered is what lets you charge for results with confidence.
This doesn't have to be complicated. Whatever you already use for logging hours can be repurposed to track credit usage too. File your AI meeting notes by project, then periodically prompt an agent to summarize feedback and flag where pricing and value are misaligned, or where operational issues are brewing. For more on using AI to manage agency projects, check out this playbook.
If you're a small agency or freelancer, you may not think you have a quote-to-cash process, but this is just business speak for packaging your services, quoting, managing the contract, invoicing, and getting paid. If you price in billable hours, the entire chain is probably built around them: contract templates structured around hours, a CRM capturing billable hours by project, invoices built on hourly rates.
You can hack around this for a while by converting hours into your new unit of value, but it won't be sustainable. Set up fresh contracts and invoicing templates that reflect your new pricing and packaging, and make sure everyone involved in quote-to-cash understands the change and why it's happening.
To benefit from selling value rather than hours, you need to understand what your agency is uniquely good at and translate that into repeatable processes and packages, rather than selling whatever you can to whoever you can reach. Hours flex to anything a client asks for; value requires you to think harder about the work you should take on.
Promethean Research found that 84% of digital agencies now position themselves as specialists, with specialist agencies growing 2–3x faster than the industry average. This means saying no to some projects, and perhaps dropping services your team loves. It may be uncomfortable, but specialization and standardization are proven to improve agency performance long-term.
\"Repeatable solutions enable outcome-based pricing. They build a body of proof, reducing risk for agencies and increasing credibility for clients.\"
Brian Kessman, Lodestar Agency Consulting
If bonuses and promotions are still tied to hours billed, everyone will keep optimizing for them, and any pricing change won't stick. Moving away from the billable hour is a cultural change as much as a strategic one.
Think through AI incentives too. You may want the team using AI as much as possible, but credit leaderboards can drive unintended behavior, as Meta recently discovered. Instead, align team incentives with the same units of value you now sell: a top performer might ship the most deliverables, win repeat work because clients love them, or build a new AI process that lifts margin on every project. Decide which successes you will now track and celebrate.
You may have read this far thinking: \"All sounds great, but I still have no idea how to implement AI at my agency to deliver the efficiencies everyone seems to be expecting.\"
First, read this excellent article from Lovable's Head of Growth, Elena Verna. It will help you feel calmer about the pressure. No agency anywhere has totally cracked the AI code (yet).
Next, try these specific and immediately actionable ideas for common agency tasks:
Moving away from billable hours refreshes your agency's business model from top to bottom. That complexity is why so many agencies have avoided the leap, even though clients have been calling for value-based pricing for years. AI is both the spur to push the change through and the tool that eases it, tracking projects and acting as a consulting partner while you land on the right packaging for your agency.
Still have questions? Join our Reddit community to swap tips with other agency leaders steering their companies through the age of the agent.
", "url": "https://www.supernormal.com/blog/billable-hour-pricing-ai-agencies", "title": "AI is killing the billable hour: agency pricing in 2026", "summary": "AI does in minutes what used to take hours, so billing by the hour punishes your best work. Here is how agencies are repricing for outcomes in 2026.", "date_modified": "2026-07-08T00:00:00.000Z", "author": { "name": "sarah-kiefer" }, "tags": [ "Client work" ] }, { "id": "urn:sha256:17419e8d6be7199eb5cd6e900f33280be16e58ef3f58f3140f2785e41192d69c", "content_html": "Agency life can feel like one battle after another. AI is revolutionizing the professional services industry, and wider economic uncertainty makes finding and retaining new customers more challenging than ever. According to Promethean Research, the average agency registered 7.5% revenue growth in 2025, but also cut staff by 2%, and saw margins slip to 13% from 14% the previous year. So what can agencies do to proactively steady their ship?
Managing scope creep is a great place to start. According to research by Ignition, a pricing and payments application used by agencies, 78% of agencies say they rarely or only sometimes charge for scope creep. This means 57% of agencies lose between $1-5k each month doing out-of-scope work, and 30% of agencies are losing over $5k. Almost all agencies (87%) are therefore losing anywhere from $12k to over $60k each year by failing to address scope creep.
The good news is that AI agents can now help combat agency scope creep in ways that would previously have been impractical, or unjustifiably expensive. In this article we'll explain how to use AI agents to manage scope creep at every step in your project workflow. If, like 28% of those surveyed by Ignition, you're selling subscription-based rather than project-based services, these tips are equally relevant, and can be implemented in an ongoing manner.
Scope creep conversations are some of the most delicate you will ever have with partners, and AI will never replace the long-term, trusting relationship you build with your clients. But AI can help you spot details you or your team might miss, and give you flexibility, suggested strategies, and operational capabilities you've probably missed before.
Here at Supernormal, we make agents designed to help agencies thrive, and the recommendations made here are informed by that work. However, these guidelines are designed to be useful whichever tools and agents you choose.
Your agency likely has a proprietary workflow developed from experience working with your specific clients and area of specialism. Workzone's workflow map is broadly applicable to all types of agency however, and in this article we'll breakdown where agents can help at each stage of this high-level framework:
![\"Circular](\"https://framerusercontent.com/images/WEaQJoT1daycF4Oi0c7BX40wf4.webp\")
Intake and brief
Planning, prioritization, and task breakdown
Pre-production alignment (strategy lock)
Production (parallel execution)
Internal review
Client communication and review
Revisions and change management
Approval and delivery
Post-delivery review and improvement
If you're working with clients on long-term retainer, this model is still relevant, repeating regularly and with some steps occasionally skipped.
When you first engage with a client, their expectations, and therefore the scope of the project, are typically communicated via multiple channels and in varying formats: Email, documents, meetings, briefing decks, budget spreadsheets, Slack messages. The opportunities for mismatched expectations and scope creep start here. Agents excel at collating and summarizing information from multiple sources, so at this stage it's important to feed the rich context you receive to a client-specific agent, even if you don't need the agent to take action (yet). The agent can then reference this information throughout the project, checking for scope creep, as well as upselling opportunities, and contradictions in what the client is telling you, or mismatches between ongoing work and client expectations.
Most crucially, all meeting context should be provided to your agent at this stage. Discovery and briefing calls, or even networking chats or catch-up coffees from last year, are a primary source of information about what the client really wants and needs from the engagement, beyond what they provide in official briefing documents. Make sure transcripts and meeting notes are also captured.
Not all clients provide sufficient information on their expectations at this stage. Great agencies excel at helping document client expectations and checking they have understood these. Agents are also well suited to creating the first drafts of this documentation. Clients feel heard and understood, you save time on document formatting, and no detail slips through the cracks. Try using Supernormal's Campaign Brief and Creative Brief presentation templates for example, to turn a discovery or briefing call into a beautifully-formatted deck with the help of our agents.
Once objectives and high-level deliverables are clear, the next step is to plan how the work will happen. Again, AI agents are ideal for getting this work started. Whatever project management tool you use, and whatever visualization best helps your team, be it Kanban, timelines, dashboards, or tickets, agents help you speed up this step, turning the context you've provided into drafted plans for you to review, tweak, and feed into your project management tools.
Later deviation from these plans will be your first hint that scope creep may be happening, and using an agent at this stage ensures alignment with all the information you inputted in stage 1.
For more information on how to use an agent to manage a project, read our article on managing projects with an AI agent.
Most clients aren't intentionally trying to increase scope, they're just excited about the work and reiterating their needs. It's natural for circumstances to change, exact needs to become clearer once they see initial drafts, or just to always want a little more. Generally, requests for scope creep are a good sign. The client likes what you're doing, and wants more of it. At this stage, frame a clear scope as the client's insurance policy, to ensure their project comes in on time and on budget.
The document signed off by the client at this stage will be the yardstick against which future scope creep will be measured, so if changes are requested at this stage, make sure they are documented. This step is often marked by a final alignment meeting, so ensure the meeting transcript and any supporting documents are again supplied to your agent, as well as the client and team. Often, your AI agent will be better than you are at spotting deviations from this document later down the road.
This is also a great moment to ask your agent to check for misalignment between the final alignment documentations, client conversations, and earlier project details. You may have missed these in the normal back-and-forth and high-level strategy conversations, but your agent won't have. For more information on turning client calls into a clear record of what was agreed, see AI and meetings: How context changes everything.
As you work on deliverables, scope creep may happen because of team ambition, and well as client requests. At this stage, use your agent to check outputs against agreed scope and client objectives. Sometimes the agent may spot misalignment better than the people who are heads-down and absorbed in actually doing the work.
Once the work actually starts, plans may also need to be constantly adjusted. This can create a lot of manual work, adjusting plans, tickets, and documentation. Agents can speed up a lot of this admin work, freeing up time for the creativity and strategic excellence the client is paying for.
This is the stage at which some of the most challenging scope creep conversations occur. The client sees the deliverables, even if they’re still just in draft, and starts requesting something more, or something different. The challenge shifts from spotting scope creep to managing it.
The human relationship between you and your client can never be outsourced to an agent. But the delicate business of reinforcing boundaries, suggesting possible solutions, or even upselling, can be supported by an agent who should be well-informed on the details of the project by now. When you or your agent identify project scope creep, brainstorm a variety of solutions with the agent. There may be creative approaches to rethinking the project structure that you haven’t considered. Have the agent draft a variety of options for adjustments to existing plans, like reducing the scope of one workstream to expand the scope of another. Or even use the agent to structure optional additional services that the client may now be interested in.
Any project adjustments at this stage can be laborious, and an AI agent can re-draft budgets, craft slides explaining changed, or even adjust the wording in a contract, so you can focus on review and client comms. Remember that scope creep at this stage is often a good sign that the client is fully engaged with the work, and may even be open to upselling or extending the scope of the project.
Congrats! You've reached the finish line of the project. You've likely been holding your breath until that final delivery meeting is completed, and the client is totally satisfied. If the client is broadly happy, this is the time to proactively look for opportunities to expand the work further, and start again at stage 1, this time with an agent by your side that has all the context from the successful first project. As you and your agent structure these new project options, ensure your agent has access to emails, Slack threads, and the notes from final client handover meetings. Opportunities may be hiding in a throw-away comment you missed while trying to get the project over the line.
What if this client is less than happy? If their dissatisfaction is related to perceived underdelivery, all that documentation from the early stages of the project will be crucial to grounding conversations in facts and specifics, and looking for mutually acceptable solutions. Again, an agent may see options you might not have considered, so treat them as a thought-partner to support you at this stressful time. The agent will also be able to pull specific details and exact quotes from meetings, acting as an important line of defense if you need it. For advice on spotting early warning signs so this situation never comes as a surprise, and a basic protocol for communicating with unhappy customers, we like this guide to handling unhappy clients from The Admin Bar, a community for agency owners and freelancers.
Now the project is over the line, it's tempting to move on with the next piece of work, but outstanding agencies always take a pause to learn from what just happened. Agents make excellent coaches at this stage, including reflecting on where scope creep did happen, and how this might be prevented in future. Try asking your agent the following questions:
Where did our work deviate from the originally agreed plan?
How might I have better managed the project?
Looking specifically at the emails/Slack messages/meeting transcripts associated with this project, how can I improve my client communication skills?
Practically, you can also ask the agent to draft team de-brief exercises, or retro surveys.
Project packaging and structure can also be at the root of sub-optimal project outcomes, and now is the time to analyze learnings about that from this particular project. Work with your agent at this stage to consider alternative service bundles, more efficient or standardized scopes across projects, or making a move to subscription packages that can make cash flow and staffing more predictable. Work with an agent to consider wider agency strategy, like pricing, client value, and ways to improve margin. An agent with access to context from multiple projects can assess the wider opportunities for your business, and help with long-term strategy and growth.
Most people working at an agency these days have experience using AI to draft emails, take meeting notes, or even write basic code. Few have tried working with an agent at every stage of their project workflow. Effectively deploying agents to address scope creep requires an awareness of the context agents need to be useful at every project stage, as well as an idea of where that agent can then help. By mapping the opportunities and practical steps explained in this article to your specific workflows, your agency can steal an edge by proactively managing scope creep from the first discovery call, without adding additional headcount.
Many agents out there can help fight scope creep in the ways explained in this article, but Supernormal is built specifically for agencies. Our agents can draft client briefs, decks, project plans, and recap emails, all based on the meeting notes written by our bot-free notetaker. Try Supernormal for free, and chat with our agent to generate your next brief, project plan, or proposal.
", "url": "https://www.supernormal.com/blog/managing-scope-creep-with-ai", "title": "Managing agency scope creep with AI agents", "summary": "Scope creep quietly drains agency margins. Learn how to use AI agents to spot, manage, and recover it at every stage of your project workflow.", "date_modified": "2026-07-02T00:00:00.000Z", "author": { "name": "sarah-kiefer" }, "tags": [ "Client work" ] }, { "id": "urn:sha256:04e77660873d6b950a01839a43141e0238c0ba878be3787c775e9ace477b8349", "content_html": "The fastest way to get a transcript from a YouTube video is free and already built in. Most videos have an auto-generated transcript you can access in seconds, no tools required.
This guide covers how to get that transcript, what to do when a video does not have one, and what to do with your transcript once you have it.
Transcribing a YouTube video is useful for more than just accessibility. Common reasons include:
Research and note-taking — extract quotes, data, and key points from long videos without rewatching
Content repurposing — turn a video into a blog post, newsletter section, or social posts
Accessibility — make content readable for deaf or hard-of-hearing viewers, or anyone who prefers reading over watching
SEO — text is indexable; video alone is not
Meeting prep — pull highlights from a recorded presentation, webinar, or interview before a client call
YouTube auto-generates transcripts for most videos. The accuracy varies but is usually good enough for research, content repurposing, or feeding into another tool, and you do not need to download anything.
Open the video in your browser
Click the three-dot menu (...) directly below the video, to the right of the Share button
Click Show transcript
A panel opens on the right with the full transcript, timestamped line by line
You can click any line to jump to that moment in the video. To copy the whole transcript cleanly, click the three-dot icon at the top of the transcript panel, select Toggle timestamps to remove them, then select all and copy.
Tap the video to bring up controls
Tap the three-dot menu in the top right
Tap Show transcript
If the transcript panel does not appear, the creator has either disabled it or YouTube has not generated one for that video. Skip to Method 2.
If a video has no auto-generated transcript, you can still get one in a couple of minutes using Supernormal's desktop app.
Open the Supernormal desktop app
Trigger a manual capture
Play the YouTube video
Once the video finishes, stop the capture
Open Supernormal and your full transcript is ready, no upload or import required
This also works for any video you cannot get a transcript for elsewhere, including private or unlisted videos you have access to, or videos with disabled captions.
If you pulled a transcript using Method 1, the next step is doing something with it. Paste it straight into Supernormal's AI agent and turn it into a summary, blog post, brief, or other client deliverable in a flash.
Supernormal also fits this workflow a third way: if you are recording a meeting that involves watching or screen-sharing a YouTube video (a team review, a training session, a client walkthrough), the desktop app captures the audio in the background and transcribes it automatically once the meeting ends. No separate transcription step needed.
A raw transcript is a starting point. Here is what you can do with it:
Long-form video often contains more than enough material for a written article. Paste the transcript into your editor, remove filler words, and restructure with headings. A 20-minute video can easily become a 1,500-word post.
Scan for strong standalone quotes or insights that work as LinkedIn posts or Twitter threads with minimal editing.
If you are working through multiple videos on a topic, paste the transcripts into an AI tool and ask it to extract key points or compare positions across sources.
Take your transcript, paste it into Supernormal's AI agent, and turn it into a brief, proposal, or project summary in moments.
", "url": "https://www.supernormal.com/blog/how-to-transcribe-a-youtube-video", "title": "How to transcribe a YouTube video for free", "summary": "Get a transcript from any YouTube video, with or without YouTube captions, then turn it into a blog post, summary, or client deliverable with Supernormal.", "date_modified": "2026-06-30T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "Meetings" ] }, { "id": "urn:sha256:1bf4a165a1856dc964b7b25a142299cc2dad362b8c658f691c5dca024068a305", "content_html": "Running an agency means running a lot of projects at once. A rebrand for one client, a campaign launch for another, a website build that slipped two weeks, and three discovery calls booked before Friday. Each project lives across email, briefing decks, Slack threads, a project management tool, and a dozen meetings. The work itself is rarely the hard part. Keeping everyone aligned on what the work is, who owns it, and when it lands is where agencies quietly lose hours, margin, and sleep.
This is the gap an AI agent can close. AI project management for agencies isn't about replacing your Project Managers or handing client relationships to a machine. It's about giving your delivery team a context-aware partner that turns scattered information into clear plans, keeps tasks moving, and catches the details people miss when they're heads-down on five accounts at once. In this playbook we'll walk through how to put an AI agent to work at every stage of an agency project, from the first kickoff to the final retro.
To keep this concrete, we'll follow one project the whole way through. A twelve-person creative agency wins a six-week brief from Totally Teas (what can I say, I'm British), a D2C brand launching a subscription. The project will cover a positioning refresh, a launch landing page, a three-email sequence, and a set of social cuts. Watch how the agent earns its place at each step.
Most project management tools are filing cabinets. They store tasks, dates, and documents, but they wait for a human to put everything in and keep it current. That admin is exactly what overloaded agency teams skip when things get busy, which is how plans drift out of date and details fall through the cracks.
An AI agent works differently. It sits on top of the context your team already creates: meeting notes, briefs, emails, and chat. Instead of asking you to type everything in twice, it reads what was said and agreed, then drafts the plans, status updates, and follow-ups for you to review. The shift is from documenting work to reviewing work. You stop doing the manual admin, and start reviewing what the agent prepared. For a fuller picture of how this fits into a connected way of working, see our explainer on AI workspaces explained.
It's a gap agency leaders feel keenly. In our recent survey of agency leaders, Ila Bandhiya of Middleware said she wishes AI could \"act more like a true collaborative partner, remembering context across projects, understanding intent beyond prompts, and proactively helping solve complex multi-step problems.\" A context-aware agent is how you close that gap.
Before fixing project management, it helps to name where agency projects actually break. The problem's rarely a lack of talent or effort. It's the coordination tax that builds up when work is spread across many clients and many tools.
Context is scattered. The real brief lives half in a kickoff call, half in a follow-up email, and half in a Slack message someone sent at 9pm
Decisions get lost. A client agrees to something verbally on a call, and three weeks later nobody can find a record of it
Plans go stale. The project plan made on day one stops matching reality by week two, and updating it feels like a chore
Status chasing eats the week. Account Managers spend Monday morning asking the team for updates instead of doing client work
Handovers are thin. When a project moves between people, context doesn't move with it
Each of these is a context problem, not a talent problem. An AI agent that holds the full context of a project can take most of this weight off your team.
Agency leaders feel this firsthand. Shraddhanvita Tiwari of Lean Summits names \"workflow fragmentation across tools and teams\" as her biggest operational challenge: work spread across separate platforms for SEO, analytics, project management, and content, with no easy way to connect it into a single execution loop.
An agent is only as useful as the context it holds. The first move on any project is to feed it everything that shapes the work: the brief, the proposal, the budget, the kickoff deck, and the relevant email and chat threads. The goal is a client-specific agent that knows the engagement as well as the person leading it.
Meeting context matters most of all. Discovery calls, kickoffs, and weekly check-ins are where the real requirements surface, often well beyond what makes it into the official brief. Capture those conversations as notes and transcripts and give your agent access to them. When Supernormal captures a meeting without a bot on the call, those notes become the source material your agent draws on for everything that follows. We go deeper on this in AI and meetings: how context changes everything.
In practice: at the Totally Teas kickoff, Supernormal captures the discovery call and the internal kickoff without a bot on either one, so the Account Manager stays in the conversation instead of typing notes. Afterward she gives the agent access to the signed proposal, the budget, and the client's old brand deck, to be used alongside both sets of meeting notes.
Once your agent holds the context, the next step is a plan. This is one of the highest-value things an AI agent does for an agency. Ask it to turn the brief and kickoff notes into a first-draft project plan: objectives, deliverables, workstreams, owners, and a rough timeline. What used to take a PM a day now arrives in moments, ready to polish.
You stay in control of the plan. The agent gives you a structured starting point that captures what was actually agreed, and you tweak, reprioritize, and approve before anything reaches the client. Because the plan's built from the same context as the brief, it's far less likely to miss a deliverable that was mentioned once and never written down.
In practice: asked for a first draft, the agent returns objectives, four workstreams, suggested owners, and a six-week timeline for Totally Teas. It also flags something the team hadn't clocked: the client mentioned a retail pop-up on the discovery call that never made it into the written brief. That single catch is the difference between a clean launch and a scramble in week five.
A plan is only useful when it becomes work people can pick up. Ask your agent to break each workstream into concrete tasks, suggest dependencies, and map them onto a timeline. Whatever shape suits your team, whether that's a Kanban board, a Gantt timeline, or a simple task list, the agent can draft the breakdown for you to drop into your project management tool.
This is also where an agent earns its keep on estimation. By pulling from how similar past projects ran, it can flag where a timeline looks optimistic or where two workstreams are about to collide for the same designer. You still make the call, but you make it with the details surfaced rather than buried.
In practice: breaking the Totally Teas plan into tasks, the agent surfaces a clash. Design and the launch video both need the same designer in week three. It flags the collision before it becomes a late night, suggests a sequence that protects the Friday deadline, and redrafts the timeline for the team to approve.
The day-to-day grind of agency project management is keeping work moving and keeping everyone informed. This is where the status-chasing tax hits hardest, and where an agent gives the most time back.
As meetings happen and work progresses, your agent can draft the running record: what changed since last week, which tasks are at risk, and what needs a decision. Instead of an Account Manager pinging five people for updates every Monday, the agent assembles a status update from the project context that the team simply reviews and corrects. When a plan needs adjusting, the agent can redraft the affected tasks, timelines, and documentation in a flash, so a mid-project change doesn't turn into an afternoon of admin.
In practice: through the Totally Teas build, the agent drafts the Monday status update from what actually happened in the week's meetings, so nobody chases anyone for it.
The point isn't to remove people from the loop. It's to move them from doing the busywork to reviewing it. Stop doing, start reviewing.
Clients don't see your task board. They see the updates, recaps, and deliverables you send them, and alignment lives or dies on those. After every client call, your agent can draft a recap email that captures decisions, next steps, and owners, so nothing agreed on the call gets lost. Before a milestone, it can pull together a progress summary in the client's language rather than internal shorthand.
This is also your early-warning system for scope. When a client request drifts beyond what was agreed, an agent that holds the original plan can flag the gap and help you draft options for handling it.
In practice: midway through the build, the client asks for an extra TikTok cut. The agent flags that it sits outside the agreed scope and drafts two options: absorb it, or add it as a small change order. The Account Manager makes the call and sends a recap that keeps the decision on the record.
When a project lands, the temptation is to move straight on to the next one. The best agencies pause to learn first, and an agent makes that pause cheap. Ask it to look back across the project and answer the questions a good retro asks: where did the work deviate from the plan, where did time leak, and what would you do differently next time.
Because the agent can read the full record of emails, notes, and task history, its review is grounded in what actually happened rather than what people remember. You can also ask it to draft a team debrief, a client satisfaction survey, or a short internal write-up of lessons learned. The repeatable parts of how you run projects can live as SOPs the agent keeps current, updated from how the work runs. Over many projects, an agent with this context becomes a kind of institutional memory, spotting patterns in where your projects tend to slip.
In practice: at delivery, the agent looks back across the whole Totally Teas project and produces an honest retro: where the timeline slipped, which revision round ran long, and what to scope differently next time. That write-up becomes the starting point for the next project with the client, this time with an agent that already knows the account.
Not every AI tool is built for the way agencies work. As you weigh up options, a few things separate a genuine project partner from a novelty.
Context first. The agent should draw on your real meetings, briefs, and threads, not just a chat box with no memory of the project
Capture without friction. If logging context is hard work, your team won't do it. Look for a notetaker that captures meetings without a bot joining the call
Deliverables, not just answers. The best agents produce finished client work: plans, recaps, briefs, and updates that are ready to polish and send
You stay in control. Output should sound like you and arrive as a draft for review, never auto-sent on your behalf
If you're choosing tools for a delivery team, our roundup of the best AI notetakers is a useful place to start, since capture is the foundation everything else is built on.
The most common mistake is treating an AI agent like a search box. Teams that only ask it one-off questions get one-off answers. The agencies that get real value give the agent context at the start of a project and keep working with it at every stage, so it grows more useful as the project goes on.
The second mistake is expecting finished work with no review. An agent gets you most of the way there in a fraction of the time, but the last mile, the judgment, the relationship, and the creative spark, stays human. Treat the agent as the partner that clears the admin so your people can focus on creativity and the client.
Most people at an agency have used AI to draft an email or take a quick note. Far fewer have put an agent to work across a whole project. The agencies that do can manage more clients, shifting scopes, and fast-moving workstreams without adding headcount, because the coordination tax that used to eat their week now lands as drafts to review.
Supernormal is built for exactly this. Our bot-free notetaker captures your meetings, and our AI agent turns those notes into the project plans, task breakdowns, recaps, and status updates that move a project from kickoff to delivery. More than 700,000 organisations already trust Supernormal, including teams at BBDO, Haymaker, and Thrive Digital. See how agencies use Supernormal, or try it free and ask the agent to draft your next project plan.
", "url": "https://www.supernormal.com/blog/how-to-manage-projects-ai", "title": "AI project management for agencies: a practical playbook", "summary": "Learn how to run agency projects with an AI agent, from kickoff to retro. A practical playbook for planning, task breakdown, coordination, and status updates.", "date_modified": "2026-06-26T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "AI and agents" ] }, { "id": "urn:sha256:c1c7a1c4ab576f2a5c8b014778b454a766c668650754316fb797c71f2ebb6661", "content_html": "Meetings move fast. Whether you're training new employees, capturing interviews, or documenting high-stakes decisions, sometimes a simple summary isn’t enough - you need the full record. That’s where meeting recording tools come in.
These tools do more than just hit \"record.\" They capture full video and audio, provide searchable transcripts, and let you rewatch (or reuse) key moments on demand. Think of them as your core memory or second brain: reliable, reviewable, and built to keep everything in one place.
In this guide, we’ve rounded up the best meeting recording tools for 2026 - spotlighting what each one does best, who it’s for, and how to choose the right fit for your workflow.
Best for multilingual meetings and EU data privacy: HappyScribe
Best for phone heavy workflows: Dialpad
Best for in person recording: Otter
Best for AI summaries and takeaways: Supernormal notetaker
Best for noise removal with recording: Krisp
Best for sales teams and coaching: Gong
Best for turning meetings into finished work: Supernormal
When creating this list, we focused on practical, real-world utility. Our goal is to help you find the best meeting recording tools for your needs, whether you’re looking for full-video recordings, meeting insights, or the ability to autoshare meeting summaries. Here’s how we made our selections:
We analyzed third-party ratings and reviews
We looked at reviews from trusted sources like G2 to understand what real users value (and critique) about each tool. This helped us spot common pain points and standout strengths.
We highlight each tool’s best use case, not just who’s “#1”
Instead of ranking tools from first to last, we chose to spotlight what each product does best. Some are great for integrating with your CRM and productivity systems, while others shine at generating instant summaries or serving async teams. We believe the best tool depends on how you run your meetings.
By combining user feedback, real-world experience, and tailored evaluation, we’ve curated a list that helps you make an informed choice faster.
HappyScribe is an AI meeting notetaker and transcription platform that records, transcribes, and summarizes calls in 150+ languages. It auto-joins your Zoom, Google Meet, and Microsoft Teams meetings through calendar sync, identifies speakers, and emails structured summaries and action items minutes after the call ends, with GDPR and SOC 2 Type II compliance built in.
Records, transcribes, and summarizes meetings in 150+ languages and dialects
Auto-joins Zoom, Google Meet, and Microsoft Teams via Google or Outlook calendar sync
Speaker identification, AI summaries, and action items after every call
Browser recorder and iOS/Android app for bot-free and in-person capture
Widest language coverage on this list, with 150+ languages at over 95% accuracy
EU-built with GDPR and SOC 2 Type II compliance and AES-256 encryption
Three capture modes: calendar bot, send-to-URL, and a bot-free browser recorder
Generous free plan with unlimited meeting recordings
Notes are generated after the meeting, so there is no real-time live captioning
Free plan caps each meeting recording at 45 minutes
Pro plan starts at 3 seats, which is more than some solo users need
Top-up minutes cost $0.20/min, which can add up for heavy users
Free: 10-minute AI trial plus unlimited meeting recordings at 45 minutes each, 1 seat
Pro: $19/month (billed annually). Includes 600 AI minutes per month, 3 seats, and unlimited AI Chat
Business: $59/month (billed annually). Adds 6,000 AI minutes per month, 5 seats, usage analytics, and discounted human proofreading
Enterprise: Custom pricing. Includes SSO/SAML, dedicated account management, and premium support
\"This platform is really useful in keeping records of online meetings. The AI is powerful in analyzing the recording, it can easily summarize contents and even answer questions on the content. I love how it integrates with Google Drive, Dropbox, and Zapier, and the user-friendly interface.\" — Konjengbam M., G2
\"Frequently, when I transcribe interviews or videos where there is more than one speaker, it often doesn't accurately capture who is speaking, so I often have to manually modify the speakers.\" — Borja O., G2
![\"Recording](\"https://framerusercontent.com/images/CWyHxiPcnnpltNXDHVPPZDorEo.webp\")
Dialpad Connect, Dialpad’s business communications plan, is ideal for customer support, customer communications, and phone heavy workflows. It offers high quality call recording, real time transcription, and AI summaries for teams who rely on frequent inbound and outbound calls.
Unlimited HD voice calls, AI-powered meetings, and messaging
Real-time transcription and instant call summaries
CRM integrations, admin controls, and analytics
24/7 support on Pro and Enterprise plans
Real-time transcription is highly accurate
Seamless integration with CRM and helpdesk platforms
Easy to set up and manage across devices and office locations
AI-powered call summaries reduce manual note-taking
Strong mobile and desktop app support for hybrid work environments
Clean, intuitive interface praised by users
Limited support for advanced video or screen sharing features
AI summaries can be inconsistent depending on audio quality
Transcription struggles with heavy accents or low-quality calls
Customer support response time varies by tier
Reporting features could be more customizable
Standard: $15/user/month billed annually ($27 month to month)
Unlimited calling, messaging, and meetings, AI transcription and AI Recaps, real-time analytics, Google Workspace and Microsoft 365 integrations, and 24/5 support
Pro: $25/user/month billed annually ($35 month to month)
Adds CRM integrations (Salesforce, HubSpot, Zoho), 24/7 support, and multiple office locations
Enterprise: Contact sales
Adds SSO, unlimited locations, advanced controls, and priority support
\"It's easy to make calls from your phone, computer, and Bluetooth headsets. It's a tool that we use often (every day) to communicate internally and externally. The support team is great at resolving technical issues.\" - Verified User, G2
“The only \"con\" I've run into is the fact that I'm bilingual and sometimes have to speak Spanish during an English call and that scrambles up the algorithm and the AI goes bananas.” - Alex F, G2
![\"Otter](\"https://framerusercontent.com/images/cV8y7pOLcZTF368Qd2o7uklMJB0.webp\")
Otter.ai is a versatile meeting transcription tool that shines in live, in-person contexts. Its mobile app can record audio while transcribing conversations in real time. With speaker tagging, search functionality, and offline support, it's a strong option for anyone capturing meetings on the go.
Take a look at Otter’s in-person meeting guide to get started!
Mobile app for live recording and transcription
Speaker identification and tagging
Records straight from a phone, no conferencing platform needed
Searchable transcripts with real-time syncing
Perfect for in-person use: start recording instantly from a phone without special equipment
Intuitive interface with strong mobile and desktop usability
Real-time transcription with searchable transcripts
Automatically generates summaries and action items
Transcription accuracy can drop with background noise or strong accents
Supports six languages (English, Spanish, French, German, Japanese, and Chinese), fewer than multilingual-focused tools
Speaker labeling can be inconsistent in group settings
Subscription model can be costly for frequent users
Users have reported delays in customer support and privacy controls
Basic (Free)
300 monthly transcription minutes, 30 minutes per conversation, and up to 3 lifetime file imports
Pro: $8.33/user/month (billed annually)
1,200 monthly transcription minutes, 90 minutes per conversation, advanced AI meeting templates, and 10 file imports per month
Business: $19.99/user/month (billed annually)
Unlimited meetings, 4-hour conversation limits, 3 concurrent meetings, analytics, and admin tools (imported files capped at 6,000 minutes per month)
Enterprise
Custom pricing with SSO, domain capture, organization-wide deployment, and HIPAA security as a paid add-on (video replay coming soon)
\"I use Otter to create transcripts of all my Zoom meetings. They are better than the transcripts that Zoom is producing. Also, the summary is fairly good in Otter.\" - Brenda K, G2
\"For free features - it is very limited and sometimes in noisy environments or in slow networks it struggles a bit.\" - Shivam S, G2
![\"__wf_reserved_inherit\"](\"https://framerusercontent.com/images/Wd0o5LMQMayK11UFj3lN7JdA.webp\")
Krisp is a lightweight audio enhancement tool designed to improve the quality of your meetings by removing background noise, echo, and voice distractions in real time. In addition to its core noise cancellation features, Krisp also offers meeting recording, call summaries, and meeting insights, making it ideal for professionals who want crisp recordings with built-in insights.
Real-time noise, echo, and background voice cancellation
Real-time meeting insights, summaries, and talk-time analytics
Bot-free recording
Integrates with Slack, Microsoft Teams, and others
Effective noise, echo, and background voice removal, even in busy environments
Seamless experience with minimal CPU impact
Simple setup with an intuitive user interface
Reliable for improving call clarity and user experience
Occasional issues with call summary accuracy
Less suitable for teams needing collaborative meeting documentation
Noise cancellation may degrade audio for some voice tones
No permanent free plan, only a 7-day trial
Most features gated behind paid tiers
Free trial: 7 days, no credit card required
Full access to try Krisp, there is no permanent free plan
Core: $8/user/month billed annually ($16 month to month)
Video recording, unlimited noise cancellation, 16+ languages, AI chat, in-person and hybrid meeting support, bot-free note taker, and 10GB storage
Advanced: $15/user/month billed annually ($30 month to month)
Adds 60GB storage, meeting link sharing, admin controls, and priority support with a dedicated Slack channel
Enterprise: Custom pricing
Adds a dedicated account manager, organization-wide controls, and unlimited storage
Watch Krisp’s noise cancellation capabilities as they are put to the test, below.
“In order to have any kind of success in my role, it's imperative that when speaking with individuals/offices, there are no background distractions. That being said, I am also a full time Mommy to 6 young children… However I found the Krisp App and it was a GAMECHANGER! It blocks out EVERYTHING and EVERYONE else's voice/crying while I'm on the phone, so well in fact that my baby can be throwing a fit 2ft away from me, and the person on the other end of the phone never hears a peep.” - Brandi W, G2
“Some meetings that apparently were recorded do not show in my meeting history and seem to have been lost, and the Krisp customer support team haven't been able to help me.” - Verified User, G2
Gong is a conversation intelligence platform designed for go-to-market teams. It records, transcribes, and analyzes sales calls to deliver insights on deal progression, customer objections, and rep performance. It integrates with popular CRMs and supports multi-channel capture, including Zoom and phone calls.
Records and analyzes video and voice calls
Conversation analytics tied to deal stages and pipeline
Smart search across calls, topics, and participants
Deep CRM integration with Salesforce, HubSpot, and others
Powerful analytics for sales performance and deal health
Identifies talk ratios, follow-ups, and next steps
Helps uncover common objections and winning behaviors
Centralized view of customer interactions across reps and accounts
High learning curve for new users or non-sales teams
Can surface too much data without clear prioritization
Setup and implementation require sales ops involvement
Pricing not transparent; requires custom proposal
Less relevant for teams outside of GTM or revenue functions
Gong’s pricing is custom based on team size and needs
Licenses are priced per user, with a platform fee based on the number of supported users
CRM and tech stack integrations are included at no additional cost
Interested teams must contact sales via a form to receive a tailored proposal
\"Using Gong has been a game-changer for my growth. Being able to rewatch my calls and review them with my manager has helped me sharpen my messaging and spot areas to improve. I also love having searchable transcripts—it makes it easy to update my notes and keep track of deal momentum. It’s made me more thoughtful, more prepared, and ultimately more effective in my role.\" - Emma L, G2
“The only thing I dislike about Gong is that it sometimes cannot show up on the meeting, and I just wish I would receive a notification if the person I am meeting with has declined the Gong recording so that I can prepare otherwise for the meeting (notebook, pen etc).” - Madison K, G2
While most tools on this list focus on full video recording and playback, we also included one standout option for teams who care more about follow through than replay. This is where the Supernormal app comes in.
Supernormal is an AI agent that helps you move quickly from discussion to execution. It captured audio via the Desktop App using your system audio to generate transcripts, then turns your meetings into clear summaries, next steps, and drafted work. Supernormal is ideal when you want more than a traditional notetaker and prefer a tool that helps you complete follow ups in seconds.
Automatic summaries with next steps
Conversation intelligence that lets you chat with AI using your meeting context
Shortcuts that automate repetitive follow ups and workflows
Artifact generation that produces drafted emails, Slack updates, Linear issues, Notion docs, and other post-meeting work
No bots join your calls
Designed for users who want meeting capture and workflow execution in one place
High accuracy for transcription, summaries, and drafted content
Works quietly in the background
Gives you ready-to-refine drafts that help you move quickly after meetings
English, Spanish, French, German, Italian, Swedish, and Portuguese supported only at the moment
Not built for heavy team level collaboration workflows
Doesn't record video, which may be a drawback for users who need full video playback
Free: $0/month, no credit card required. 15 monthly credits (5 per day), meeting notetaker with no bots, unlimited projects, and MCP to connect Supernormal to other tools
Team: $20/month. 50 credits per month, no daily limits, credit rollover for up to two years, and unlimited seats with no per-seat pricing
Business: $40/month. Adds SSO, audit logs, data retention, and admin controls
\"I love using Supernormal, especially because I usually spend way too much time writing emails after meetings. Since Supernormal automatically drafts post-meeting emails without me having to prompt it, it’s saved me hours every week. It’s honestly been a total game changer.” - Verified User, G2
\"Supernormal doesn’t always start automatically when a call begins. Every so often, I need to trigger it manually, which interrupts the flow a little.\" - James W, G2
Recording tools are powerful, but they are not always the right fit for every conversation. A recent discussion on Reddit highlighted how divided people are on the topic.
“Call recorders, have allowed me to stop taking every note, and have much more fluid conversations, all while knowing I can revisit the call later.” - Commenter, /salesengineers
“I’d say 99.9% of the time no one rewatches them.” - Commenter, r/salesengineers
Some professionals said they could not imagine working without meeting recordings. They rely on them to rewatch key moments, prepare for demos, protect themselves when details are disputed, and quickly ramp up on deals or projects they joined late. Others said full recording can make conversations feel performative, especially when leaders replay calls to micromanage. Some customers also become more cautious when they know a meeting is being archived.
A lot of people fell somewhere in the middle. They record discovery, onboarding, and complex cross-regional calls, but leave sensitive or early-stage conversations off the record to preserve candor.
The Reddit thread revealed something important:
Most people do not want video archives. They want clarity, recall, and follow-through.
That leads to three practical options depending on your workflow:
Useful when you need full fidelity, replay, training, or compliance. Great for complex sales cycles, onboarding, and multi-team coordination.
Ideal when you care about recall and searchability more than rewatching video. Many people in the thread said they almost never play back the full recording and rely entirely on transcripts or AI summaries.
Best when your priority is post-meeting execution rather than documentation. Tools like Supernormal app focus on turning your discussion into summaries, next steps, and ready-to-use drafts so you can move forward without digging through long recordings.
The takeaway is simple.
You don't need to record every meeting. The right choice depends on whether you care most about replay, recall, or results. The best teams are intentional, choosing the level of capture that supports better work rather than defaulting to recording everything.
Meeting recording tools are built for thoroughness. They help you revisit conversations exactly as they happened - whether for compliance, training, or simply to double-check a key point. In contrast to AI notetakers, which are built for speed and summary, recorders give you total fidelity. Every word. Every voice. All searchable and replayable.
That makes these tools essential for:
Remote or hybrid teams who need shared context across time zones
Legal, finance, and healthcare professionals with documentation requirements
Ops leaders creating searchable libraries of internal knowledge
While recorders prioritize completeness, they can be even more powerful when paired with AI tools that help extract insights. Platforms like Supernormal bridge that gap, offering full recordings along with structured, actionable summaries you can instantly put to work.
In short: whether you’re building a record, a knowledge base, or a better workflow, the tools on this list are designed to help you capture more and forget less.
", "url": "https://www.supernormal.com/blog/best-meeting-recording-tools", "title": "Best meeting recording tools 2026 | pros, cons, and more", "summary": "Never wonder what was decided again. Discover the best meeting recording tools of 2026 with transcription, search, and sharing features.", "date_modified": "2026-06-25T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "Meetings" ] }, { "id": "urn:sha256:642f4958e8a5cfb82e2817d125de93d9db7b688e542a44b1c497a0973acc8a31", "content_html": "For our State of Meetings report, we looked at 50.9 million hours of real meetings captured between 2023 and 2025, and a handful of formats turned out to make up most of how teams meet.
So here are the six most common types of meetings by real volume, what each one's for, and how to run it so people leave with a clear next step. They're ordered by how often they actually happen.
![\"Horizontal](\"https://framerusercontent.com/images/OXNt7tnmlLZeuG64KdH0q3gBe0.webp\")
Sync and check-in meetings are the single most common type in our data, with 6.2 million captured. They're recurring conversations where a team or a few collaborators align on progress, dependencies, and what's next.
When to use it: when a group needs shared visibility and coordination on an ongoing basis, not when one person just needs a quick answer.
How to run it well: keep it tight and predictable, give it a consistent structure, and capture decisions and blockers as you go so it produces a record rather than just talk. For recurring formats specifically, see our guide to effective recurring team meetings.
Common pitfall: letting a check-in sprawl into problem-solving. If something needs real discussion, take it offline and keep the sync moving.
Standups are second by volume at 3.0 million meetings, and together with syncs they account for more meetings than every other named type combined. A standup is a short, daily cadence where a team shares quick updates and surfaces blockers. They're also the most likely meeting to draw a crowd: 45% of standups in our data have 10 or more attendees.
When to use it: when a team is moving fast day to day and needs a brief, regular pulse check.
How to run it well: keep it to a strict time box and a simple format, for example what you did, what's next, and what's blocking you. Hold deeper discussions for a separate session.
Common pitfall: letting it run long. A 30-minute standup isn't a standup anymore.
All-hands and town halls (1.3 million meetings) are how leaders share company or org-wide updates. They consistently draw the largest crowds in our data, often well past 10 attendees, because the whole point is reaching everyone at once.
When to use it: when the same message needs to land with a whole team, department, or company, and a live moment adds something a written update can't.
How to run it well: lead with the headline, prepare your materials in advance, and protect real time for questions so it isn't purely one-directional. Our guide to running impactful all-staff meetings covers this in detail.
Common pitfall: reading slides aloud. If there's no discussion or Q&A, a recording often respects everyone's time more.
One-on-ones (1.2 million meetings) are among the fastest-growing formats we track, and direct conversations now make up nearly a quarter of all meetings. They're a focused conversation between two people, usually a manager and a report, built for check-ins, mentorship, and decisions that don't need a wider audience. Unlike all-hands, they stay lean by design, typically just two people.
When to use it: when a single working relationship needs dedicated attention, whether that's coaching, feedback, or unblocking someone.
How to run it well: let the other person set part of the agenda, keep it consistent rather than canceling it when things get busy, and follow up on what you agreed last time. Our guide to successful one-on-one meetings has a full playbook.
Common pitfall: turning it into a status update. A one-on-one is for the conversation and connection a status meeting can't hold.
Sprint and agile ceremonies (885,000 meetings) are the recurring rituals development teams run to plan, review, and improve: sprint planning, retrospectives, and backlog grooming. They blend several purposes, from deciding what to build next to working through what went wrong.
When to use it: when a team works in cycles and needs structured moments to plan the work and learn from the last round.
How to run it well: give each ceremony a single clear job, so planning doesn't bleed into a retro. For the retrospective in particular, separate diagnosing the cause from choosing the fix, and leave with owners and dates. See our guide to agile meetings.
Common pitfall: running ceremonies out of habit after they've stopped being useful. If a ritual isn't earning its place, change it.
Interviews (661,000 meetings) round out the six. They're focused conversations to evaluate a candidate, and like one-on-ones they stay small, usually two to four people centered on a single decision.
When to use it: any time you're hiring, of course, but the same structure helps for vendor evaluations and other assessment conversations.
How to run it well: prepare consistent questions so you can compare candidates fairly, and capture notes during the conversation so your evaluation isn't running on memory afterward. Our guide to crafting the perfect interview script is a good place to start.
Common pitfall: winging the questions. Inconsistent interviews make fair comparison almost impossible.
Meeting type | What it's for | Volume in our data | Typical size | Cadence |
|---|---|---|---|---|
Sync / check-in | Align on progress and dependencies | 6.2M | Small to mid team | Recurring |
Standup | Quick daily pulse and blockers | 3.0M | Often 10+ | Daily |
All-hands / town hall | Org-wide updates | 1.3M | Largest crowds | Monthly or as needed |
One-on-one | Coaching, feedback, focused decisions | 1.2M | 2 people | Weekly or biweekly |
Sprint / agile | Plan, review, and improve the work | 885K | Dev team | Per sprint |
Interview | Evaluate a candidate | 661K | 2 to 4 | As needed |
The six above are the most common by volume, but a few other formats come up often enough to recognize, usually as variations on the types above:
Decision-making meetings exist to reach and commit to a choice. Board meetings and strategy sessions are decision-making meetings at their core.
Problem-solving meetings bring the right people together to work through a specific issue and agree on an action plan. Retrospectives and incident reviews are common examples.
Brainstorming meetings generate new ideas and options before anything gets judged or refined. Our roundup of tools for running brainstorming meetings is a good starting point.
Planning meetings set direction for a period, such as quarterly or annual planning.
Team-building meetings strengthen relationships and trust rather than produce a deliverable, from a structured offsite to a light recurring ritual.
You'll often see meetings split into two broad buckets: formal and informal. Formal meetings follow a set agenda and produce a record. Informal meetings are looser and conversational. That split cuts across all six types above. A one-on-one can be a casual coffee or a structured review, and a sync can be a quick huddle or a minuted status meeting. The format you choose depends on the stakes.
Before you send the invite, ask three questions: what outcome do you need, who genuinely needs to be there, and could this be handled without a live meeting at all. Our data shows teams are already getting more deliberate here, with average meeting length down from 51 to 47 minutes and longer meetings on the decline. The meetings that remain are the ones that earn their place.
Whatever type of meeting you run, the value lives in what happens afterward: the decisions, the action items, and who owns what. Supernormal captures your meetings without a bot on the call, then turns them into clear notes and action items in moments, so you can stay present in the conversation instead of scribbling. From there you can turn the notes into follow-up messages and other deliverables that are ready to send.
You can see how it works on the meeting notetaker page.
These six types are what teams actually run most. Match the format to the outcome, keep a clear record of what gets decided, and a meeting earns its place.
Want the full picture of how teams meet today? Read the State of Meetings report.
", "url": "https://www.supernormal.com/blog/6-most-common-types-of-meetings", "title": "The 6 most common types of meetings (based on 50 million hours of data)", "summary": "We analyzed 50.9 million hours of meetings to find the six most common types of business meetings, what each one's for, and how to run it well.", "date_modified": "2026-06-23T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "Meetings" ] }, { "id": "urn:sha256:a4796182e20a7dad9761cc259e6bf6ea61e0a2e81b4e959f637a018c7070cb10", "content_html": "An AI notetaker you didn't invite just turned up in your Zoom call. Maybe it's Otter, quietly recording while everyone wonders who pressed record. The good news: you can remove it in a few seconds, stop it joining future meetings, and disconnect it from Zoom entirely. Here's how to do all three, plus a calmer way to take meeting notes that never puts a bot on the call.
💡 What Otter Notetaker, OtterPilot, and the Otter bot all mean
They're the same thing. Otter renamed OtterPilot to Otter Notetaker, so older guides and even your own settings may use either name. Both refer to the assistant that joins your calls to record and transcribe them.
If Otter is recording a call right now, you have three quick ways to stop it.
Type a command in the chat - Send \"stop otter\" in the Zoom meeting chat. Any participant can do this, even people without an Otter account, and it removes the notetaker right away. If more than one Otter notetaker is in the call, \"stop otter\" removes all of them at once
Stop it from the Otter conversation - Open the live conversation in Otter, from the home page calendar or the recording page, click Stop Notetaker, then confirm with Yes, turn it off
Remove it as the host - In Zoom's participants list, find the Otter Notetaker, open the More menu next to its name, and choose Remove. Only the host or co-host can do this, and a removed participant can't rejoin unless you allow it
If Otter won't stop recording, the chat command is usually the quickest fix, since any participant can send it and it doesn't depend on finding the right browser tab mid-call.
To keep Otter from auto-joining every call, change its auto-join setting once. On desktop or web:
Sign in to Otter and open Integrations
Open the Meetings tab, and find Default auto-join settings
Select Meetings I manually select
On mobile, tap Account, then Account Settings, then Meeting settings, then set Auto-join meetings to Manual.
Otter saves the change automatically. One note: any individual calendar events you previously toggled on or off keep their own setting, so it's worth reviewing upcoming events after you change this.
If you'd rather cut the connection entirely:
In Otter, open Settings, and disconnect the calendar Otter is using. This removes Otter from all your future calendar events in one step
For a clean break, open the Zoom App Marketplace, go to Manage, then Added Apps, and remove Otter there too
That revokes Otter's access, so it can't rejoin your meetings until you connect it again.
Want Otter to stop taking notes across Zoom, Google Meet, and Microsoft Teams, not just Zoom? Setting auto-join to Meetings I manually select applies everywhere, so one change covers all your calls. To pause Otter without deleting your account, leave auto-join on manual and start a notetaker by hand only when you actually want one.
Most people remove Otter for one of two reasons: a bot on the call feels intrusive in front of clients, or the notes never turn into real work afterward. Supernormal was built for both.
Supernormal takes notes in the background, without a bot ever joining your call. Nobody sees an extra participant, and your clients see only your team. After the meeting, Supernormal uses the context of the conversation to draft the things you'd otherwise build by hand: recaps, follow-up emails, briefs, and slides, ready the moment you ask. You stay in control, reviewing and refining before anything goes out.
No bot on the call - Supernormal takes notes in the background, so client calls stay professional
From conversation to client work - Turn the call into a recap, follow-up email, or brief in moments, not hours
You stay in control - Every output is ready to review and refine before you send it
More than 700,000 organizations already use Supernormal for meeting notes and client work. You can try Supernormal for free, no credit card needed. See how bot-free notes work on the meeting notetaker page, or read why we made the case for no bot on the call.
Removing a different notetaker? Here's how to remove Read AI from Zoom and how to remove Fathom from your Zoom calls.
", "url": "https://www.supernormal.com/blog/remove-otter-from-zoom", "title": "How to remove Otter AI from Zoom: The 2026 guide", "summary": "Otter AI joining your Zoom calls uninvited? Here is how to remove Otter from a meeting, stop it recording, and keep it out of your future calls.", "date_modified": "2026-06-16T00:00:00.000Z", "author": { "name": "sarah-kiefer" }, "tags": [ "Meetings" ] }, { "id": "urn:sha256:8d3410df16deb0eaaffb90b1974c826fe1423344a7f290344061c62bcc939264", "content_html": "MEDDIC, MEDDICC, and MEDDPICC are three versions of the same sales qualification framework. They give you a checklist for judging whether a deal is real, winnable, and worth your team's time, so you can spot the gaps before you commit hours to an opportunity that was never going to close.
They differ only in how many checks they include. MEDDIC is the original six-part version. MEDDICC adds a seventh check, Competition. MEDDPICC adds an eighth, the Paper process. You'll also see MEDDPICC misspelled as medpicc or medpick. They're the same framework, and Google treats them as the same search.
Why it matters: research by Matthew Dixon and Ted McKenna, drawn from 2.5 million sales calls, found that between 40 percent and 60 percent of deals end in no decision rather than a loss to a competitor. Most deals don't die because a rival wins. They stall because the buyer is unsure. A qualification framework exists to catch those stalls early, while you can still do something about them.
This guide covers all three. We start with the six MEDDIC components, then show what MEDDICC and MEDDPICC add, and how agencies use the framework to qualify and win new business.
MEDDIC was created in the 1990s by Jack Napoli and Dick Dunkel at Parametric Technology Corporation (PTC), where it helped drive a long run of rapid sales growth. It became one of the most widely adopted qualification frameworks in enterprise sales, and it's still maintained today by training companies like MEDDICC. MEDDICC and MEDDPICC came later as extensions, adding checks for the parts of a complex deal that the original six didn't cover.
The numbers the client wants to move. Revenue, cost, pipeline, retention, brand awareness. If you can't name the metric your work will improve, you can't prove your value later.
The person with final authority over the budget. In an agency pitch this is often not the person in the room. Your day-to-day contact may love you, but if the CMO or a procurement lead signs the contract, you need access to them.
The factors the client uses to choose between options. Creative quality, price, sector experience, chemistry, speed. Knowing the criteria lets you shape your pitch around what actually wins the account, and it helps you judge whether the prospect even fits your ideal client profile before you invest in pitching.
The steps and timeline the client follows to reach a decision. Who's on the panel, how many rounds, when they want to start. This tells you whether a deal will close this quarter or slip.
The real reason the client is looking. A campaign that underdelivered, an incumbent agency that went quiet, a launch with no plan. Pain drives urgency, and urgency is what gets deals signed. The trick is to listen for what the client actually means, not just what they say, because miscommunication with clients is where pitches quietly go wrong.
Your internal advocate. The person who wants you to win and will sell for you when you're not in the room. A champion has influence and a personal stake in the outcome. A friendly contact with neither isn't a champion.
MEDDICC keeps all six MEDDIC components and adds one more.
Everyone else in the running: other agencies on the shortlist, the incumbent, and the option of keeping the work in-house. Understanding the alternatives lets you position against them honestly, and it's where a sharp, well-prepared sales pitch earns its keep, rather than finding out at the final round that you were never the front-runner.
MEDDPICC is the most complete version. It keeps the seven MEDDICC components and adds an eighth.
The admin required to sign: master service agreement, procurement, legal, security review, onboarding. Surfacing this early stops a won pitch from stalling for six weeks in a contracts queue.
So the full eight letters of MEDDPICC are: Metrics, Economic buyer, Decision criteria, Decision process, Paper process, Identify pain, Champion, and Competition.
MEDDIC (6 checks): straightforward deals where you mainly need to qualify the opportunity and find your champion
MEDDICC (7 checks): competitive situations where you're up against other vendors or agencies
MEDDPICC (8 checks): complex deals with formal procurement, legal review, or several competitors, which is most agency pitches
For agency new business, MEDDPICC is usually the right fit. Procurement and contracting (the Paper process) and the agencies you're up against (Competition) are too important to leave out.
MEDDPICC isn't the only way to qualify a deal. The best known alternative is BANT, which stands for Budget, Authority, Need, and Timeline. Popularised by IBM, it's the lightweight, four-question version: does the prospect have the money, the authority to spend it, a real need, and a timeline to act.
The difference is depth. BANT is quick, which makes it useful for an early, high-volume check on inbound leads. MEDDPICC is slower and far more thorough, which makes it the better fit once a deal is real and competitive. BANT can wave through an opportunity that MEDDPICC would flag. A prospect can have budget and a deadline but still have no internal champion, no clear decision process, and three rival agencies you haven't accounted for.
You'll also come across frameworks like CHAMP and SPICED, which reorder similar ideas. For agency new business, a sensible approach is to use BANT as a fast first-call filter, then switch to MEDDPICC once a prospect becomes a serious pitch.
Most teams turn the letters into a simple scorecard and rate each one:
Green: you know it and have it confirmed
Yellow: you have a partial answer or an assumption
Red: you don't know
Any red or yellow is an open risk. The point isn't a perfect score. It's an honest picture of where the deal is weak, so you know what to chase before the next conversation. Run the scorecard in pipeline reviews and you'll spend less time on deals that were never going to land.
A pitch is a complex sale: several stakeholders, a formal process, real money, and competitors. That's the exact situation this framework was built for.
Most of the answers come straight out of your discovery and pitch calls. Run your client meetings well and the information surfaces naturally: the client tells you their pain, names the panel, hints at the budget holder, and lists who else they're talking to. The hard part is capturing all of it accurately while you're also running the conversation, then making sure your whole team can see it. And once you win, a strong kickoff meeting carries that same context into delivery.
Sales reps spend just 28 percent of their time actually selling, according to Salesforce's State of Sales report. The rest goes on admin, data entry, and writing things up. That's the gap Supernormal closes.
Supernormal captures your discovery and pitch calls without a bot joining the meeting, so the conversation stays professional and you stay present in it. After the call, its AI agents turn the discussion into your notes, follow-ups, and next steps, surfacing the qualification detail that came up: the metrics the client cares about, who signs off, the pain behind the search, and the competitors in the mix.
That means your scorecard fills itself from real conversations instead of half-remembered notes, and everyone working the account sees the same picture. You stop doing the write-up and start reviewing what matters. From the same call, Supernormal can also draft the follow-up email after a discovery call or the proposal, ready for you to polish and send. It's the kind of context-aware help AI agents built for agencies are made for.
", "url": "https://www.supernormal.com/blog/medpicc", "title": "MEDDIC, MEDDICC, and MEDDPICC: sales qualification framework explained", "summary": "MEDDIC, MEDDICC, and MEDDPICC are versions of one sales qualification framework. Learn what the letters mean, how they differ, and how agencies use them.", "date_modified": "2026-06-15T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "Client work" ] }, { "id": "urn:sha256:f40cd796c1a4fa1de70faba77a50be0e7c324074299ab908404ac750ad8fa383", "content_html": "It's easy to cringe just thinking about it. A misread brief, a vague line in an email, a kickoff call where everyone walked away with a different version of the plan. The wires cross, the work stalls, and someone ends up redoing it on their own time.
In an agency, the stakes are higher than in most workplaces. You are not just coordinating internally, you are managing several demanding clients at once, on deadlines, against a margin. When communication breaks down on client work, it costs billable hours, eats into that margin, and chips away at the client trust you worked hard to earn.
So what does miscommunication look like in an agency, why does it hit client-facing teams so hard, and what can you actually do about it? Here is a practical guide, with real examples and fixes you can put to work this week.
At its core, miscommunication happens when the message someone means to send is not the message that lands. The person writing or speaking and the person reading or listening end up on different pages. Details get lost, intent gets garbled, and assumptions fill the gaps.
In an agency, where context moves fast between account leads, creatives, strategists, and clients, it shows up in a few familiar forms:
A brief or message that is not clear in the first place
Important details from a client call left out of the write-up
Words or phrases that the client and the team read differently
Different communication styles between departments or with the client
Assuming knowledge the other person, or the client, does not have
The handoff between people that drops context along the way
Cultural and tonal differences across clients and markets
The result is always the same. The original intent does not get through, and people end up working from different understandings rather than a shared one. On client work, that gap turns into rework, missed scope, and awkward conversations you would rather avoid.
When account teams, creatives, and clients are not aligned, the fallout adds up fast.
When a brief is unclear or a key detail slips through, you spend billable time going back and forth to get clarity. That is time you cannot bill twice. As SHRM reports, a survey of 400 companies with 100,000 employees each cited an average loss per company of $62.4 million per year because of inadequate communication to and between employees.
Without a shared understanding of goals, timelines, and who owns what, work drifts off course. The classic cautionary tale is NASA's Mars Climate Orbiter, launched in 1998 and lost in 1999 when one team worked in metric units and another in US customary units, and no one caught the mismatch. Agency timelines rarely involve spacecraft, but the pattern is the same: a small unspoken assumption snowballs into a big, expensive miss.
Miscommunication erodes trust on both sides. A client who feels misheard, or a team that feels jerked around by shifting direction, starts to lose confidence in the partnership. The same dynamic that creates long-standing tension between coworkers plays out across the agency-client line.
Being out of the loop and constantly cleaning up avoidable confusion is draining. Gallup research has found that about one in two U.S. employees have left a job at some point to get away from their manager. Chronic miscommunication is often part of that story.
The good news: while you will never eliminate miscommunication entirely, you can sharply reduce how often it happens and how much it costs you when it does.
The patterns are easier to spot once you have names for them.
A brief lands missing the why behind it, or the must-haves the client only mentioned out loud on a call that no one wrote down. The team interprets it three different ways, and the first round comes back off-target. Worse is the \"quick question\" that was never a quick question: a small verbal ask that balloons into hours of work because the original scope was never documented clearly. Agencies routinely lose a chunk of margin to revisions that started life as an offhand comment on a call. When the conversation is not written down, there is nothing to point back to when the ask quietly triples in size.
A kickoff call starts, but it becomes clear no one shared the same picture of scope. Some people are talking about the campaign, others about a feature the client mentioned in passing, and the meeting ends with no decisions and no clear next steps. Everyone leaves busy and none the wiser.
A client deck goes out with figures that do not match, because two people pulled from different versions of the data. The error surfaces in front of the client, and suddenly you are defending your accuracy instead of presenting your thinking.
A client mentions a shifted deadline on a quick call with the account lead. The rest of the team never hears it, keeps working to the old timeline, and finds out too late. Duplicated effort, a scramble, and a client wondering why the left hand did not know what the right was doing.
When your team juggles several accounts, context bleeds. A tone or assumption from one client shows up in another's work, or a name gets crossed in an email. Small slips, but they read as careless to the client on the receiving end.
Different triggers, same root cause: people working from different information, or no shared record of what was actually said and agreed.
You cannot personality-fix your way out of miscommunication. What works is building systems that make clarity the default.
Agree on how briefs, agendas, status updates, and decisions get documented and shared, then stick to it. Shared templates and a consistent home for client information mean everyone knows where to look. The best client meetings already follow a rhythm: our guide to making every client meeting count walks through the structure that keeps calls on track.
In back-to-back days it is easy to plan your reply instead of absorbing what the client actually said. Good listening is active, not passive. As HBR's Amy Gallo explains in the video below, nodding along and repeating back what you heard is not enough: asking good questions is what signals to a client that you not only heard them but understood enough to want more detail. Slow down, ask clarifying questions, and reflect the request back to confirm you have it right.
After a client call, send a short recap: what was decided, who owns what, and by when. The same HBR guidance notes that people remember only a small fraction of what they hear, so when a client can read along as well as listen, far more of it sticks. A two-line confirmation also closes the gap between \"what I think you meant\" and \"what you actually meant\" before it becomes rework.
When people feel able to ask questions, flag concerns, and admit they are not sure without fear of looking foolish, problems surface early instead of festering. Google's research identified psychological safety as the top factor in its highest-performing teams.
Here is the trap most agencies fall into: people default to a call instead of writing things down, because documenting every conversation feels like work no one has time for. So the context lives in someone's head, their inbox, or a call no one wrote up, and the chaos compounds every week. The fix is not \"have fewer calls\" or \"force everyone to take better notes.\" It is making the written record a by-product of the conversation you were already going to have. When every call, decision, and action item lands in one place the whole team can reference, the \"wait, what did we say on that call?\" moments disappear, and there is no longer a trade-off between talking it through and having it documented. This is where AI and meetings change the math: instead of relying on memory or hurried notes, the record writes itself.
Supernormal is an AI meeting assistant and set of agents built for agencies. It takes notes on your client calls without a bot joining, transcribes the conversation, and logs the decisions and action items, so nothing important rides on someone remembering it later.
From there, it uses that context to do the work that usually causes the crossed wires. The follow-up recap, the client brief, the status update, the proposal: drafted in moments from what was actually said, in your team's voice, ready to polish and send. Because every call lives in one place you can organize by client or project, the whole team draws from the same record. Everyone stays on the same page, automatically.
The point is not to add another tool to check. It is to close the two gaps where miscommunication does the most damage on client work: important details slipping through, and different people working from different information. Your team spends less time documenting and chasing clarity, and more time on the creative and strategic work clients actually pay for. Stop doing, start reviewing.
Miscommunication in the workplace is one of those quiet costs that rarely shows up on an invoice but shows up everywhere else: in rework, in slipped deadlines, in client trust that takes a hit. For agencies, getting it under control is not a soft skill. It is margin.
Set clear protocols, listen actively, confirm in writing, and keep one shared record of what was said and agreed. Do that consistently and you cut both how often wires cross and how much it costs you when they do. Supernormal takes care of the record and the routine client work that comes out of it, so your team can focus on the thinking only people can do.
Ready to keep every client on the same page? Supernormal takes notes on your meetings without a bot, then turns them into the client work you need, in a flash. Try Supernormal, the bot-free notetaker and AI assistant for agencies.
", "url": "https://www.supernormal.com/blog/miscommunication-in-the-workplace", "title": "Miscommunication in the workplace: An agency guide", "summary": "Miscommunication in the workplace costs agencies time, margin, and client trust. Real examples and practical fixes to keep every client on the same page.", "date_modified": "2026-06-10T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "Client work" ] }, { "id": "urn:sha256:1677e2833134f158914905921582c987dccef23da118cbbb3e2ad69a0bf4aab3", "content_html": "The impact hit faster than anyone planned for. Forrester forecasts that agency headcount will fall by 15% in 2026, around 47,000 roles, after an 8% cut the year before. Job losses the same analysts once penciled in for 2030 are landing now, four years early.
Then came the structural re-engineering. In February 2026, WPP, the company that effectively invented the modern holding-company model, announced it was no longer a holding company. It folded dozens of famous agency names into four leaner divisions, targeted half a billion pounds in savings, and rebuilt itself around AI. When the biggest player in the industry rewrites its own definition to survive, the ground really has shifted.
It would be easy to read all this as the slow death of the agency. We think that reading is wrong. Agencies aren't disappearing. A particular way of working is. And the difference between the agencies that get hollowed out and the ones that come out stronger isn't whether they use AI. It's how.
We wanted to test that theory with the people actually living it, so we asked agency owners, founders, and leaders how AI is changing their work right now. Their answers, quoted throughout this piece, are remarkably consistent.
\"People might think I'm mad to say this, but I think there's never been a better time to found and run an agency. Marketers like me are crying out for original thinking, and external help with adapting their ways of working. The next generation agency can ditch everything about agency work that was annoying and painful, and focus on the creativity that makes it great.\"
– Sarah Kiefer, Head of Marketing, Supernormal
Look closely at the 15% headcount reduction and a pattern appears. Forrester's deepest cuts fall in clerical work (28% of the losses), sales and business development (22%), and market research (18%). The thread connecting them is low originality. This is the work that follows a template, the work AI does first and does cheaply.
The same research points to the flip side. The single biggest factor protecting a role from automation is originality. Everyone frames the divide as \"uses AI\" against \"avoids AI.\" The divide that actually decides who survives runs between \"sells execution\" and \"sells originality.\" One of those is getting commoditized to zero. The other is getting more valuable.
![\"Spectrum](\"https://framerusercontent.com/images/Wg4yzZPgl8QzK9S7w8lijdDE3U.webp\")
The agency leaders we spoke to feel this at the level of individual jobs. Jon Norris of We Are All Connected put it bluntly:
\"The junior layer is evaporating. The depressing truth right now is that Claude can do 90% of stuff a junior would have done two years ago. We need agencies, especially big agencies, to keep the talent pipeline going.\"
Viola Eva of Flow Agency sees the same force pricing out a classic agency product:
\"The cost and value of deliverables is falling almost to zero. There is no real value in a hand-made content audit anymore.\"
This is the part worth sitting with. What's being automated is the billable hour, the work a machine now does in seconds, not the agency itself. Any agency whose business rests on charging for those hours is exposed. Any agency that's quietly moved its value somewhere else is not.
Here is what makes this moment strange. Two agencies can adopt the exact same tools, the same models, the same subscriptions, and end up in opposite places. The software is identical. The strategy is not.
Anna Moragli of Search Magic drew the line cleanly:
\"The winners will be the agencies that position themselves as growth and visibility partners rather than execution vendors.\"
Everything that follows is two strategies running in reverse. One uses AI to do the same work cheaper. The other uses AI to do different work entirely. The first quietly disappears. The second thrives.
![\"Two](\"https://framerusercontent.com/images/bX3XuKzVHaJqdUI6tFskT5TURg.webp\")
The agencies in trouble are not the ones ignoring AI. Often they are the most enthusiastic adopters. They've just pointed the technology at the wrong goal: cutting cost instead of building value. Four moves show up again and again, and every one feels efficient on the way down.
More, faster, cheaper. The first instinct is to use AI to produce more. More content, more outreach, more reports. Peter Emad of SalesCaptain named exactly where that leads:
\"A lot of agencies will use AI to create more noise. More content, more outreach, more reports, more generic strategies. But not necessarily better work. The market becomes flooded with average AI-assisted output, and clients start trusting agencies less.\"
Settling for soulless work. Volume without judgment produces a particular kind of output, and clients can feel it. Paul Fowler of Moat Studio summed up the risk in five words: \"That people settle for AI-fueled mediocrity.\" Jessica Martinez of Mad Fish Digital described the broader spiral:
\"It seems to be a race to the bottom right now as far as pricing goes. In a world where anyone can be a vibe coder, 'AI expert', or pump out 1000 pages of content, don't take the human relationship out of the work.\"
Damaging trust for everyone. Low-effort AI work does not just hurt the agency that ships it. Heather Hamilton of Well Optimized SEO worries about the wider cost:
\"AI has made it easy to spin up an agency overnight, and a lot of those shops are focused on revenue instead of results.\"
Racing in-house clients to the bottom. When the pitch is just speed and price, clients reasonably wonder why they need an agency at all. Stephen Vernon of Base Hit Marketing expects a painful middle period:
\"Companies deciding to bring it in house. I believe there will be a period where work becomes sparse as companies try to figure it out themselves, then bounce back as they see the operational efficiencies of working with an agency that has processes for it.\"
The common tell across all four is the same. Each one treats AI as a way to spend less. None of them makes the work worth more.
The agencies pulling ahead are running the opposite plays. They use AI to remove the low-value work, then pour the reclaimed time into the things clients can't get anywhere else. Six moves came up repeatedly.
Build around systems, not headcount. The thriving agencies are designing themselves differently from the start, often replacing a dozen single-job tools with one AI agent. Peter Emad again:
\"I would build systems from day one. Repeatable workflows for research, data, content, outreach, reporting, and client delivery. I would still hire smart people, but make sure the agency isn't dependent on doing everything manually.\"
Hannah Swinkin of Saint Boswell, five months into building her agency after a decade in-house, sees the fresh start as an advantage:
\"I've been able to start with a fresh slate and weave in AI from the get-go. It's set us up for an incredible growth trajectory at an otherwise uncertain time.\"
Make taste the product. If AI levels content production, the differentiator moves to the thing it can't fake. Tom Livingstone of Measured Branding put the whole thesis in one sentence:
\"If AI is the great leveller for content production, then brand and taste will become the differentiator.\"
Steven de Brueys of WTF SEO is betting clients come back around to it:
\"I truly believe customers would rather have a human touch once they realize that most AI text and outputs lack soul and originality.\"
Specialize, and move up to strategic partner. Generalist execution is the most automatable position there is. The leaders we spoke to are narrowing and climbing at the same time. Rewati Khare of Pepper framed her approach as \"hyper-specialization, with the human-in-the-loop approach as the differentiator.\" Michelle Tansey of Red Queen Marketing sees specialist skillsets becoming more valuable, not less, as commodity work disappears.
Productize the process. Turning repeatable work into a defined product makes output consistent and frees people for the hard parts. Hannah Swinkin again:
\"Productizing their processes, so output is more predictable and standardized across teammates. It gives teams more space for the real heavy lifting: strategy and creative thinking.\"
Hire for the new shape of the team. The roles agencies are paying for have shifted. Michelle Tansey told us her most valuable hire this year was a dedicated automation and systems person. Viola Eva is hiring for \"marketing engineering\" alongside account management and strategy. Jon Norris wants \"AI fluency. Not necessarily experience, but an excitement about the possibilities and a willingness to learn.\"
Price the outcome, not the hour. This is where the whole shift becomes concrete. If deliverables cost almost nothing to produce, billing by the hour stops making sense. Rewati Khare is moving to \"charges based on ROI or business impact rather than hours.\" Forrester frames the same move at industry scale: agencies pivoting from selling services to selling solutions. The hour was never the value. It was just the easiest thing to measure.
There is an obvious objection to all of this. Is \"use AI well\" not the same advice everyone is already giving? It isn't, and the agency leaders we spoke to are sharper about it than most, because they know exactly where the technology breaks.
Mark Williams-Cook of Candour is clear about the limits: \"GenAI still can't be used on things you need to be correct. Multi-steps involving GenAI lead to unacceptably high hallucinations.\" Jessica Martinez sees the subtler danger:
\"Sometimes AI makes things up. It works off assumptions, and assumptions are just that. If you don't know it's an assumption, you take it as truth, which can be dangerous when a specialist doesn't know any better.\"
Stephen Vernon wants a tool that argues back: \"I wish it would play devil's advocate and provide citations for where the pushback comes from. I find answers too agreeable.\" Hannah Swinkin draws a hard boundary: \"We'd never rely on it for strategy, data analysis, or research.\"
Put those together and the real meaning of \"using AI well\" comes into focus. The thriving agencies aren't using AI to remove humans from the work. They're using it to move humans up the work, from doing to judging, from producing to deciding. Deploying an agent well turns out to be a management skill more than a technical one: giving it the context, instructions, and feedback to do the job properly. The machine handles the first draft and the grunt work. The human owns the taste, the strategy, and the call on whether it's actually right. That is the whole game, and it is the one thing the losing approach forgets.
We asked everyone a final question: if you were starting an agency today, what would you do differently? The answers are the clearest map we have of where this is heading.
Jon Norris would \"build the entire stack around connections, interoperable tools, and a data layer accessible from your AI of choice.\" Anna Moragli would \"build it as an AI-native business from day one, scaling through systems and proprietary processes rather than headcount, and niche down from day one.\" Heather Hamilton would \"get clear on our mission and values much earlier, because the agencies that stand out are the ones with a real point of view.\" Stephen Vernon would \"hire experts who create processes and pair them with AI automations.\" The harder question underneath all of it is which tools actually earn a place, something we work through in our guide on how to choose AI tools for your business.
Notice what none of them said. Not one wished they could produce more, faster, or cheaper. Every answer was about originality, point of view, systems, and human judgment. The agencies that disappear and the agencies that thrive are looking at the same technology. They're just asking it to do opposite jobs.
You can still choose which agency you become. The window for that choice is open right now, and it won't stay open forever.
The through-line of every thriving agency in our survey is the same: move your people from doing the work to reviewing it, and keep human judgment on top of the AI. That takes a tool built for client work rather than a generic chatbot you have to wrestle into shape prompt after prompt. Supernormal takes notes in your meetings without a bot on the call, then uses that context, along with your emails, documents, and projects, to turn a single prompt into client-ready briefs, decks, proposals, and emails. Your team reviews and refines instead of starting from a blank page. The grunt work goes to the machine. The taste, strategy, and final call stay with you.
", "url": "https://www.supernormal.com/blog/agencies-and-ai", "title": "The future of agencies: who survives the AI shakeout", "summary": "We asked agency leaders what divides the agencies that disappear from the ones that thrive.", "date_modified": "2026-06-08T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "AI and agents" ] }, { "id": "urn:sha256:68b6d6bbad06aa2db8f7ae1b40f89f839b18b8c898d744edff96783bf1ea4c50", "content_html": "💡 Agency/client kickoff meeting defined
The agency, or client, kickoff meeting is the first formal working session between an agency and a client at the start of a new engagement. It aligns both sides on goals, scope, timeline, roles, and how the work will happen. Done well, it prevents most of the scope creep, missed deadlines, and crossed wires that derail agency projects.
Three patterns show up again and again in client kick off calls, no matter how senior the team is.
The kickoff is too friendly to be useful - Everyone's excited, the client likes you, you all promise to make great work. Nobody asks the awkward questions about budget, decision rights, or what success actually looks like.
The agenda is too generic to surface real risk - A standard \"introductions, scope, timeline, next steps\" template covers the headings, but skips the conversation that flags whether the budget can support the timeline, or whether your client has actually got sign-off from their CMO.
The follow-up takes a week - By the time the project manager has written up the recap and circulated the brief, two more meetings have happened, the team is half-mobilized, and the document everyone needed for clarity arrives after the moment it would have helped.
\"The biggest opportunity is using AI to get back the hours we lose to tedious operations and set up, such as proposals and onboarding, and putting that time into our people.\"
— Heather Hamilton, Founder, Well Optimized SEO
According to the State of Meetings 2026, 46% of meetings now include someone outside your company. Kickoffs are the highest-stakes version of that: The conversation that defines the next three, six, or twelve months of client work. They deserve a much sharper approach than they usually get.
The kickoff goes well or badly based on what you do in the 48 hours before it. Use this checklist.
Three to five questions should it, things like goals, constraints, who needs to be added to the call. The answers help shape the agenda.
Client background, contract scope, known risks, the questions you want answered in the kickoff. Anyone joining the call should be able to read this and add to it.
Especially the deliverables list, the timeline, and the change-order clause. This will make sure you're more informed and ready to respond when the inevitable scope creep request comes.
If the client's product lead, legal contact, or budget holder needs to be there, make sure you know that.
Use our project kickoff presentation template to generate a nine-slide deck from your planning context. Covers objectives, scope, timeline, roles, and success criteria. Have it ready to share during the call.
If you want a structured walk-through of meeting prep, see our full guide to meeting prep.
Use this as a starting template. Time-box each section so the conversation moves. For a typical 60-minute kickoff, allocate the time roughly as suggested below. For a 90-minute kickoff, scale up the strategy sections.
Who is on the call, what they do on this project, and how they prefer to be reached. Skip the LinkedIn bios. Get to the working relationships.
What's this project actually trying to do for the client's business? What does success look like at the end of it? Push for specific, measurable answers. \"Increase brand awareness\" isn't a success metric. \"Hit 5,000 qualified signups by end of Q3\" is.
Walk through what's in the statement of work. Crucially, name what's out of scope. Most scope creep starts with an unsaid assumption. This is the conversation worth lingering on.
Key dates, review gates, and the non-negotiable launch window. Flag dependencies the client owns: Asset delivery, sign-off windows, legal review. Lock down who signs off on what, and how fast. If the client's CFO has approval rights and isn't on this call, you need to know now.
Ask the client what could go wrong. Share what concerns you. This is the section every kickoff skips and every project regrets skipping.
Reporting, Slack or email, where files live, who owns the shared workspace. Agree how you'll send a weekly client status report, then close with three to five action items, each with a name and a date.
A kickoff produces lots of information in a short window. Decisions, owners, dates, risks, scope boundaries, success metrics, stakeholder names. Trying to take it all down by hand means you stop listening. Asking a bot to join the call means your first impression with the client is a Zoom participant called \"Otter\".
This is where Supernormal earns its keep. Supernormal transcribes your meetings without a bot on the call, takes the notes for you, and makes the full context available the moment the call ends. You stay present in the conversation. The client only sees you, no notetaker bot.
What to actively listen for during the kickoff:
Specific success metrics, in the client's own words
Anything that contradicts the SOW
Decision-makers you didn't know existed
Risks the client raises, even casually
Anything the client says twice (it matters to them)
Supernormal preserves all of it as part of the project's shared context, so the whole team can refer back to it later.
Here's where most kickoffs lose their value. The conversation was great, the client felt heard, the team felt aligned. Good times. Then someone has to spend a few hours writing it all up and assigning actions. Bad times.
You may be wary of letting AI near client-facing work. It's a fair instinct. The worst thing an agency can do is send something that doesn't sound like you, or worse, gets a fact wrong.
With Supernormal, you're always in the driver's seat and nothing gets sent without your approval. The kickoff conversation can become the context for four separate outputs. Ask Supernormal for any of them, and each one's ready to review in moments.
A formal brief in your agency's voice, covering goals, scope, deliverables, timeline, and approval flow. Start from our campaign brief template and shape it to fit your house style. Ready to send to the client for written sign-off, which means the agreed scope is documented and dated before anyone has time to misremember it.
A short, client-friendly summary of what was agreed, with action items and owners pulled directly from the conversation. The kind of email that takes you forty minutes to write by hand, ready to polish and send in moments.
Ask Supernormal to turn the kickoff into a short set of Slides for the rest of the team: Account, creative, strategy, production, finance. The people who weren't on the call get fully up to speed without sitting through a recording. Drop the deck into Slack, present it at the next internal stand-up, or send it ahead of the first creative session. The wider team is on the same page before the day's out.
A draft project plan with milestones, owners, and dependencies. A starting point for your project manager, not a finished plan, but a draft that takes minutes to refine rather than hours to build from scratch.
All four outputs come from the same conversation context, so they tell the same story. It means no more situations where the recap email and the project plan say slightly different things because two different people wrote them.
Treating the kickoff as a relationship meeting, not a working session - Build rapport in the first five minutes. Spend the next fifty doing the actual work of aligning.
Skipping the out-of-scope conversation - If the client thinks you're delivering something you aren't, you'll find out at the worst possible moment. Say what's out of scope, in writing, in the kickoff.
Not naming the decision-makers - A project with unclear approval rights moves at the pace of the slowest stakeholder. Map approvals in the kickoff and confirm them in the brief.
Writing the recap a week later - The longer the gap between meeting and recap, the more both sides start filling in the blanks with their own assumptions. Get the recap out the same day. Supernormal makes that realistic.
No follow-up cadence agreed - If the kickoff ends without a clear \"we meet every Tuesday at 10\", you've left the most important variable of the engagement to chance.
The agency kickoff meeting is the highest-leverage hour of any new engagement. It sets the scope, the trust, and the working rhythm of the next several months. Run it with a clear agenda, prep your team in advance, and let Supernormal handle the write-up so you can focus on the conversation, the client, and the work. Run it enough times and it's worth capturing as an onboarding SOP, so every kickoff starts from the same playbook.
Supernormal is built for agencies: Client briefs, decks, project plans, and recap emails, all from the same conversation context. Try Supernormal for free, and ask for the brief at the end of your next kickoff. It's ready by the time you've packed up.
", "url": "https://www.supernormal.com/blog/client-kickoff-meeting", "title": "Cracking the client kickoff: setting expectations from day one", "summary": "The kickoff sets scope, trust, and rhythm for the whole project. Get the 6-point agenda, prep checklist, and post-call workflow.", "date_modified": "2026-05-28T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "Meetings" ] }, { "id": "urn:sha256:f5e713b6210bfcdacb9a563bb39e1f8babac89ebff83ecb27af391f484fdcb10", "content_html": "Walk into any agency and look at someone's browser tabs. Slack, your project management tool, the CRM, Google Drive or Dropbox, a time tracker, the SEO tool, the social scheduler, and the shiny new thing a client insisted on back in Q1 but hasn't mentioned since. Each new client brings the possibility of one more tool added to the stack, often because they use it and you agreed to meet them where they are.
This is not just your team. For a few years, agencies were finally getting their stack under control. The average company portfolio shrank between 2021 and 2023. Then came the AI wave, and the number of applications per organization jumped back up to 275 in 2024 and 305 in 2025, according to Zylo's 2026 SaaS Management Index. Agency-specific numbers tell the same story: Basis's 2026 Advertising Agency Report found that 36.8% of full-service and media agencies now manage ten or more tools, more than double the 17.3% from two years prior.
The temptation is to add another AI tool. The answer worth considering is consolidation.
Agencies don't buy fourteen project management apps on purpose. The stack grows because every new client tries a different tool, every freelancer has their own preference, and every AI startup pitches you a product that does one slice of your job slightly better than the last one. Marketing adopts a writing tool. Strategy adopts a research tool. Your PMs adopt a reporting dashboard. By year-end, no one can find anything.
This is the textbook definition of sprawl. Zylo's research shows the average organization now runs 9.9 project management apps and 9.5 team collaboration apps. That's not unique to agencies, but agencies feel it more acutely because their work product depends on those tools talking to each other and on context flowing cleanly from a client conversation to a deliverable.
Consolidation isn't a return to the old way of working. It's a new way of working. A single AI agent does what used to take several tools, all from the same source of context.
Often that context lives in the same place: your meetings. The kickoff call has the brief in it. The pitch conversation has the proposal in it. The weekly check-in has the status update in it. If one AI agent can capture those conversations accurately and turn them into the right deliverable, the work that used to take several tools becomes one workflow.
That's the consolidation case in one sentence.
Industry voices are starting to make the same call. Paul Dyer, CEO of /Prompt, told PR Week in its 2026 industry forecast:
\"Real differentiation between agencies will become apparent for the first time since the advent of social media. Only this time, it will be agencies that have adopted AI into their daily practices that will have clear differentiation from those relying on point solutions or buzzy tech with low internal usage rates.\"
Marketing-research firm GWI calls this \"the app trap\": the productivity drag of switching between tools, which Harvard Business Review estimates at around 9% of working time, eventually outweighs the value of adding any single tool to the stack.
The forecasters agree on the trajectory. Gartner predicts that 40% of enterprise applications will embed task-specific AI agents by the end of 2026, up from less than 5% in 2025. An eightfold expansion in twelve months.
The public markets have been pricing in the same shift. In February 2026, investors wiped roughly $285 billion off software stocks in a single 24-hour window after one AI agent demo, an event the financial press promptly named the SaaSpocalypse. Bain partners summarized the math in their post-selloff note: \"Instead of 500 seats, a customer might buy 450 and let an agent do the rest.\" For agencies running a stack of per-seat single-purpose tools, that compression is already underway. Just inside the tool budget rather than on a stock ticker.
Even teams without engineers are voting with their feet. Retool's 2026 Build vs. Buy Report found that 35% of enterprises have replaced at least one SaaS tool with custom-built software this year, and 78% plan to build more. I went through this exercise myself earlier this year: three weeks in Claude Code building until I swapped a $2,500-a-year Ahrefs subscription for a custom SEO dashboard at $1,440 a year. I'm not a developer, so the learning curve was steep, but being able to type what I wanted and learn as I went made the project worth more than the saving.
That's one route. Even when the tools stay, how you reach them is changing too:
\"Haven't changed any tools, but accessing them has moved heavily towards MCP through Claude. I use ahrefs every week but haven't actually been in the platform for months.\"
Jon Norris, Marketing Consultant at We Are All Connected
Both paths are harder for agencies. The build path needs engineering bandwidth most don't have on standby. The access path still takes setup work across every connector. Consolidating around an AI agent that already understands your client context is the agency version of both moves. Fewer tools, less spend, work that fits how you operate, no code required.
Context-aware AI agents don't wipe your stack clean. They absorb slices of it. Here's where those slices land for agencies right now.
Briefs, proposals, decks, status updates, follow-up emails. These are the outputs that come from client conversations. The agent captures the call and shapes the output. Standalone notetakers (see our comparison of the best AI notetakers), blank-page AI deck builders, and the AI writing assistant your strategy team trialled last year all overlap with this slice.
Auto-built client reports pulled directly from GA, ad platforms, social, and your CRM. The work that used to live in a screenshot ritual on the last Friday of every month is now one prompt. Overlaps with standalone dashboard tools and any reporting subscription priced per client.
Competitive scans, audience profiling, category landscape, content gaps. GWI's Agent Spark and similar tools are pulling this slice in-house. Overlaps with research subscriptions, point analyst services, and the freelance researcher you brief at the start of every pitch.
Blog drafts, social copy, ad copy, email sequences generated from a brief or strategy doc, not from a blank page. Overlaps with single-purpose AI writing tools, the seat-priced copy generators that piled up in 2024, and your team's content production capacity for low-stakes formats.
\"What did we promise that client in March.\" Today that question routes through Slack, Notion, Drive, and whoever's brain you can find. AI agents that hold context across calls, docs, and threads turn it into a five-second question, and remove one of the most common reasons agencies open three tools to answer one client email.
The biggest single slice for most agencies is the meeting-to-deliverable one. That's where the most context originates and the most documentation work happens. But the others matter, and the consolidation playbook works the same way for any of them. Pick the slice, pilot one workflow, measure, expand.
This isn't six tools getting deleted from your stack. It's one or two cancelled outright (the dormant AI writing tool, the dashboard subscription nobody opens) and several others doing less of the work they were doing badly. The bigger win is coherence: fewer places where work has to start, fewer handoffs to maintain.
You can't consolidate what you can't see. Most agencies underestimate their own stack by a significant margin because the dormant tools never come up in conversation. They're forgotten precisely because they're not used.
Run the audit in two passes.
Pass 1, the visible surface. Have the team share their actual tab bars and active subscriptions in a 30-minute sync. Everyone shows what they have open today. This captures the tools your team is actively using and the ones individual people have signed up for.
Pass 2, the invisible surface. Dormant tools rarely come up because no one remembers them. To find them, you need a paper trail. Pull together email receipts (search inboxes for \"invoice,\" \"subscription,\" or \"renewal\"), talk to whoever handles operations or vendor billing, pull SSO logs if you have them, scan the shared password manager, and check credit card statements for the past twelve months. This is where the savings usually hide.
A simple format works for what you find: tool name, annual cost, primary user, last login, job it does, candidate to replace.
Common findings from a thorough audit:
Three to five tools no one has logged into in thirty days
One or two used exclusively by one person who could share their workflow
Several tools without a clear owner
At least one duplicate of a tool the company is also paying for elsewhere
A handful of AI trials from 2024 that auto-renewed quietly
Bucket every tool by the workflow it serves: meeting capture, drafting, proposal building, reporting, search, specialist craft. Anything that ends up in the drafting bucket alongside an AI agent is the obvious overlap.
Red flag worth highlighting: any tool with \"AI\" in the name that was bought after 2024 without a documented job to be done. Those almost always made it onto the stack because someone wanted to test the technology, not because they were solving a workflow problem. They're prime consolidation candidates.
Mark each candidate one of three ways:
Cancel if no one has logged in for sixty days or more
Replace if it's doing a job another tool already does better, including a job an AI agent now handles
Keep if it's the specialist software your team actually relies on for craft
Don't try to consolidate everything at once. Pick a single use case where the value is obvious. Two filters help: high frequency (it happens weekly or daily) and a clear quality bar (you'll know good from bad immediately). Pick the workflow your team complains about most.
Chris O'Neal, CEO of Growth Loop, made the same call on the MarTech podcast:
\"It's fundamentally about picking one workflow or picking one subset of your work end to end to show and learn from that area and then start to propagate it to other teams, other areas, other workflows.\"
Track three things during the two-week pilot:
Time from meeting end to first-draft ready. Should drop sharply.
Quality of the first draft on a 1 to 5 scale. Scored by a senior reviewer, not the pilot participants.
Hours saved per week per pilot participant. Simple end-of-week survey is enough.
The bar for \"holds up\" is concrete: the AI agent's first draft requires under thirty minutes of edits to be client-ready. If your team is still rewriting from scratch, the pilot has failed, and that's worth knowing in two weeks rather than two months.
Keep two ledgers, honestly.
Money side:
Subscriptions you can cancel at renewal
Seat counts you can drop on tools you're keeping
Vendor leverage gained for next quarter's negotiations
Procurement and admin time saved on the cancelled accounts
Minutes side:
Weekly hours saved per pilot participant (the survey from Step 3)
Senior team time freed up from junior review cycles
Calendar time recovered from documentation work
Faster turnaround on client deliverables
For agencies, those minutes are margin. Hours your team claws back from documentation either become billable capacity on new scope or recovered margin on the scope you already sold. Either way, the line item moves.
The minutes side is usually larger than people expect. HBR's research on app switching (cited earlier) found employees spend about 9% of working time toggling between tools. Cut three or four tools and you start clawing that back.
Typical first-pilot result is five to ten hours back per person per week, plus one to three subscriptions identified as cancellable at the next renewal cycle. The hours back are usually what your team notices first. The license savings show up at renewal.
After the first pilot succeeds, pick the next workflow. Proposals after briefs. Follow-ups after proposals. Search after follow-ups. Each gets the same two-week pilot and the same \"holds up\" test.
Most agencies are six months into this before they see a meaningfully leaner stack at renewal time. That's not failure, that's the pace consolidation actually moves at. The teams that try to consolidate everything in the same month usually end up with a half-finished migration and a team that's lost faith in the process.
Slow expansion beats fast adoption. Each workflow is its own commitment.
For more on how to evaluate AI tools at the decision-making stage, see our guide on how to choose AI tools for your business.
So, do AI agents replace the agency stack? For a growing slice of it, yes. The work that comes out of meetings (briefs, proposals, decks, follow-ups, status updates) is exactly the work AI agents are getting fluent in. The work that doesn't (creative judgment, client relationships, craft) is exactly what should stay with you.
If you are interested in consolidating, or in seeing what tools you could replace in-house, our piece on AI workspaces: rethinking how and where we work covers the wider shift. And if you want to try a no-bot AI agent built for client work, Supernormal is free to use.
", "url": "https://www.supernormal.com/blog/tool-consolidation-agency-stack", "title": "How to consolidate your agency tech stack (2026 guide)", "summary": "How to consolidate your agency tech stack in 2026. What AI agents really replace, the market signals behind the shift, and a five-step audit playbook.", "date_modified": "2026-05-19T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "AI and agents" ] }, { "id": "urn:sha256:559eff91c577d35e2a4bed67ae5808ce68195c015a87454ff72f25aee7f2b9a1", "content_html": "Taking notes during meetings is distracting, inconsistent, and time-consuming. It splits your attention, reduces engagement in the conversation, and often produces incomplete records. Worse, important action items and decisions get missed or misremembered later. In 2026, that's a solved problem. AI does the note-taking, you stay in the conversation, and the notes show up cleaner than anything you'd have typed yourself.
Adoption of AI-powered meeting notetakers has grown rapidly. Tools like Supernormal, Otter, and Fireflies are now the default in most knowledge worker stacks.
\"Unlike 5 years ago, it feels totally normal for AI to handle daily tasks like note taking, task assignment, and meeting prep.\"
Colin Treseler, CEO at Supernormal
If you've ever:
Struggled to participate in a meeting and take accurate notes at the same time
Left a meeting unsure of who was doing what
Spent time after every call manually typing up summaries
Missed key decisions because you were multitasking or distracted
...AI notetaking is for you.
This guide walks through how AI notetakers actually work, what to look for, and how to set one up. The walkthrough uses Supernormal as the example, but the same steps apply across most tools in this space.
Make sure the tool supports the meeting apps you actually use: Zoom, Google Meet, Microsoft Teams. There are plenty of AI notetakers and meeting recording tools on the market. The right one depends on your stack, your workflow, and whether you want a bot in your meetings (more on that below).
Look for:
Support for your most-used meeting apps
Calendar integration so meetings get joined automatically
Document and email integrations for sharing notes
Privacy and sharing controls
A bot-free option, if you don't want extra participants on client calls
The rest of this walkthrough uses Supernormal as the example.
Setup steps vary slightly depending on where you meet. Once you've created an account:
For Google Meet: install the Supernormal Chrome extension from the Chrome Web Store and click Add to Chrome. Allow microphone and camera access. The extension shows up automatically when you open a Google Meet.
For Zoom: connect Supernormal to your Zoom account from Account Settings in your Supernormal dashboard. Open Integrations, toggle Zoom on, and log into your Zoom account. Supernormal joins meetings you host without manual approval. If you're not the host, the host still needs to approve recording.
For Microsoft Teams: open your Account Settings, find \"Automatically join calendar events\", and choose \"Calendar events with a meeting link\". Supernormal will then auto-join any Teams meeting on your calendar. You can also manually add norma@supernormal.com as a participant before or during a Teams call.
Once you're set up, Supernormal runs on autopilot. It syncs with your calendar and joins meetings based on the preferences you've selected:
Join all events with a meeting link
Join only meetings you've created
Join only when you explicitly invite Supernormal
During the meeting, Supernormal captures the conversation in real time. It identifies speakers, summarizes key points, and extracts action items.
After the meeting ends, you get a structured summary within seconds. Notes include clearly labeled sections for decisions, action items, and discussion summary. You can share automatically with internal attendees, via a public link, or with specific people only.
The biggest question when picking an AI notetaker is whether it should join the meeting as a visible participant (a bot) or work silently in the background.
Bot-based notetakers join your call as a virtual participant. Other attendees see them in the participant list, often labeled something like \"Otter Notetaker has joined.\" Bots are useful for transparency on external calls and for capturing video alongside the transcript.
Bot-free notetakers integrate with your calendar and your computer's audio to take notes silently. No new participant appears in the call. This is the better choice for internal meetings, client calls where a bot would feel unprofessional, or anytime you want notes without changing the room dynamic.
Trade-offs to keep in mind
Bots make it obvious that the meeting is being captured, which can be a feature or a bug depending on the call
Bots can feel intrusive to clients and external participants
Bot-free options need permission to access your computer's audio
Some apps (like Teams) work better with a bot than without
Supernormal supports both. The Supernormal meeting notetaker captures notes from your computer without a bot in most cases. Bots are used only when the meeting app requires them, like for capturing video on Zoom and Teams. For more on the trade-off, the bot-versus-no-bot post goes deeper.
The Supernormal meeting notetaker goes further than transcription. It captures meeting context directly from your computer, then AI agents turn that context into the work you'd otherwise spend hours writing yourself: follow-up emails, slides, briefs, project plans, and docs. In a flash.
Zero setup friction: no recording buttons or reminders. Once configured, it just runs
Structured, shareable summaries: clear highlights, action items, and context
Works across the stack: Google Meet, Zoom, Microsoft Teams, and in-person meetings via the desktop notetaker
Enterprise-grade security: SOC2 Type II compliant, trusted by 700,000+ organizations
Beyond notes, Supernormal turns meetings into completed client work. The same meeting can generate a follow-up email for the client, a brief for the team, a slide deck for the executive review, and a project plan for the next sprint. Polish and send.
AI meeting notes save hours of admin per week, but only if the tool fits your workflow. Pick one that supports your meeting apps, matches your bot tolerance, and meets your privacy standards. Then let it run.
", "url": "https://www.supernormal.com/blog/ai-meeting-notes", "title": "How to use AI for meeting notes (2026 guide)", "summary": "Use AI to take meeting notes automatically. A 2026 step-by-step guide to AI notetakers, with or without a bot in your call.", "date_modified": "2026-05-14T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "Meetings" ] }, { "id": "urn:sha256:26f4fbdb382749afb61daf22750962cbb11e4ffd73d3d3f3e30d1396da7259dc", "content_html": "You're about to join a Microsoft Teams meeting and you don't have the app installed. Maybe you're on a borrowed laptop, on your phone, or you just don't want one more app on your machine. Here's how to do it in under a minute, plus how to handle the note-taking without a bot in the call.
When the meeting invite shows up, the link wants to open the Teams app by default. To skip the app, right-click the link and copy it, then paste it into Chrome, Edge, Firefox, or Safari. Edge and Chrome are Microsoft's recommended browsers, but the others work fine for most meetings.
When the page loads, look for the option that says \"Continue on this browser\" or \"Join on the web instead\". Click it. The page will ask for microphone and camera permissions. Allow them if you want to participate, deny if you'd rather lurk. Sort out your audio and video settings, then click \"Join now\". You're in.
![\"Microsoft](\"https://framerusercontent.com/images/t4QOEVcTSzLW98NsoBNDYhXmbw.webp\")
You don't need a Microsoft account to join as a guest. The only thing you'll need to enter is your name, so make it a recognizable one.
Pro tip: bookmark teams.microsoft.com as your default Teams entry point. Faster than digging through emails for the meeting link every time.
Mobile is trickier. Teams for Web is not officially supported on iOS or Android, so when you tap a meeting link on your phone you'll usually see something like this:
![\"Microsoft](\"https://framerusercontent.com/images/s2olc4ePMuVYmR231YdgH0KnPGs.webp\")
The big purple Download Microsoft Teams button is Teams's preferred answer. But if you really don't want the app, switch your browser to \"Request Desktop Site\" mode:
Safari (iOS): tap the AA icon in the address bar, then \"Request Desktop Website\"
Chrome (iOS or Android): tap the three-dot menu, then \"Desktop site\"
Reload the page. The web app usually loads after that. Allow mic and camera access when prompted, and expect video quality to drop on slower connections.
Honest take: the Teams mobile app is more reliable for mobile meetings. You can delete it after the call if you don't want it cluttering your phone.
Most Teams meeting invites include a phone number and conference ID for audio dial-in. Scroll to the bottom of the invite. You'll miss the visuals (slides, screen shares, faces), but you'll be on the call.
The browser version of Teams covers most of what you'll need in a meeting:
Screen sharing: share your entire screen or a specific window. Close anything sensitive first
Chat: the meeting chat panel works the same as in the app
Background effects: blur or a limited set of background images
Breakout rooms: you're moved automatically when the organizer creates them, and you can return to the main room manually
Keyboard shortcuts: Ctrl + Shift + M (Cmd + Shift + M on Mac) to mute, Ctrl + Shift + O for camera, Ctrl + Shift + K to raise your hand
![\"Microsoft](\"https://framerusercontent.com/images/ieUpdnzizImF0g6kmmGuqT2fZuk.webp\")
📋 What's not available in the browser
A few features only work in the desktop app: whiteboard, full file collaboration, and the highest video quality settings. Live captioning is also more limited in some browsers. If you rely on any of those, the app is the better choice.
Joining without the app solves the install problem. It doesn't solve the note-taking problem. You still need someone or something to capture what was said, pull out the action items, and turn the conversation into the work that comes after.
The Supernormal meeting notetaker takes meeting notes directly from your computer without a bot joining the call. After the meeting, AI agents turn that context into the work you'd otherwise spend hours writing yourself: follow-up emails, slides, briefs, project plans, and docs. In a flash.
It works whether you joined Teams in the browser, on mobile, or in the app. No bot in the meeting, no awkward \"Supernormal has joined the call\" announcement, no manual write-up afterwards.
If you take notes from a lot of meetings, especially client calls with external attendees, the bot-versus-no-bot trade-off is worth a quick read before you settle on a tool. The AI meeting notes guide walks through the setup in more detail.
And that's how you join a Teams meeting without the app, take useful notes from it, and get on with the rest of your day.
", "url": "https://www.supernormal.com/blog/joining-microsoft-teams-meetings-without-the-app", "title": "How to join Microsoft Teams meetings without the app", "summary": "Join Microsoft Teams meetings from your browser or phone without installing the app. Step-by-step guide, plus how to take notes without a bot.", "date_modified": "2026-05-14T00:00:00.000Z", "author": { "name": "sarah-kiefer" }, "tags": [ "Meetings" ] }, { "id": "urn:sha256:d913c8ec0a10ff2b415c5455898c5b08d85feb86073a9a30c66de0bc8038ec28", "content_html": "Every agency knows the rhythm. The RFP lands on a Tuesday. The deadline is the following Friday. The account lead calls a kickoff. The team huddles, debates the approach, sketches the pricing, divides the questions. Then the real work starts: drafting, formatting, fact-checking, chasing past work, rewriting the same boilerplate for the fourth time this quarter.
The numbers are blunt. Sales teams spend 20 to 30 percent of their time on RFPs and questionnaires. 10 to 20 percent of RFPs go unanswered every year, and missing even one response per month can cost a team anywhere from $1.2 million to $60 million in annual revenue, depending on deal size. For agencies running on billable hours and tight margins, that loss plays out differently: it shows up as senior strategists writing until midnight, juniors rewriting the same case study for three different pitches, and the work you actually wanted to be doing pushed to next week.
The cost isn't just time. It's the strategic work you didn't start, the pitch you didn't enter, the creative idea that died because nobody had the bandwidth to develop it. RFPs aren't the work. They're the gate to the work. And the gate is consuming the team.
Most AI tools for RFPs are built for enterprise sales teams responding to vendor selection processes. Agencies have a different problem.
An agency RFP response is a strange document. Most clients ask for similar sections — agency overview, strategic approach, team, past work, methodology, pricing, timeline, references — but every RFP has its own twist. Half of what you need to include lives in your team's heads. The other half is scattered across Slack threads, last quarter's winning proposal, a Google Doc someone updated in March, and the discovery call you ran two weeks ago.
The IP problem compounds it. Agencies build the same answers over and over. The capability statement. The methodology overview. The relevant case study tailored to this specific industry. Every team has its version of the perfect answer to \"describe your approach to creative development.\" Most teams rewrite it from scratch each time, because finding the last good version takes longer than starting fresh.
Then there's the context problem. An RFP response isn't just a description of your services. It's a response to this client's specific needs, surfaced in the discovery call, refined in the review meeting, and shaped by the strategic conversation your senior team had on Friday. The best responses sound like you've already started the work. That voice is impossible to fake with a generic template.
If you've tried using ChatGPT or Claude for an RFP response, you know the shape of the problem. You paste in the RFP. You ask for a draft. You get back something competent but generic. It doesn't know your client. It doesn't know your past work. It doesn't know what your team agreed in the review meeting. It doesn't know your pricing logic, your team composition, or which case study is genuinely relevant for this prospect.
So you spend the time you \"saved\" rewriting the draft. You replace the generic language with your team's voice. You swap out the made-up case studies for real ones. You re-do the pricing because the AI guessed. You strip out the over-confident claims and add back the nuance your account lead would actually use.
The math stops working. Generic AI gives you a first draft in minutes and costs you a day to clean up. That's not a workflow. That's a different way of starting from scratch.
The AI agent that helps you draft an RFP well is the one that already has the context. Your meetings. Your past proposals. Your team's actual approach to the work.
This is the difference between retrieval-first AI tools (which search a knowledge base for pre-approved snippets) and context-aware AI (which uses real conversation, real documents, and real client signal to write something genuinely yours). Both have a place. But for agencies, where the unique IP is the way you think about client problems, the second one wins.
Here's what that workflow looks like.
The moment the RFP lands, you call a review. Whoever leads the meeting opens Supernormal's meeting notetaker and runs the call as normal. No bot joins the room. The notetaker captures your team's discussion: which questions you flagged as critical, the approach you agreed to lead with, the pricing logic, the team you want to put forward, the differentiators you want to emphasize.
When the call ends, the notes appear in Supernormal in your browser, ready to use. No exporting, no copy-paste. The substance of the response is already on screen.
Head to the RFP response template page. The page has its own prompt box with the RFP response structure already attached, so the agent knows to format the output as a proper proposal: agency overview, strategic approach, team, budget, timeline, measurement framework, references.
In the prompt box on the template page, describe what you need: something like \"Create an RFP response using this template based on the RFP review call.\" Hit enter and Supernormal opens a new task, finds the review meeting in your context, and starts drafting. Attach any extra context that matters: past winning proposals, the client's RFP brief, the discovery calls you ran with this prospect six weeks ago.
The draft is a full proposal: agency overview, strategic approach, team, real case studies pulled from your past work, budget breakdown, timeline, measurement framework, references. It isn't generic. It's grounded in your team's actual review meeting and the work your agency has already done. Your team reviews, tightens the language, signs off on the pricing, and submits.
This is the workflow agencies using Supernormal already run. Teams at BBDO, Pinterest, and Thrive Digital use the same meeting-context approach for proposals, briefs, and client communications. The principle is the same: the agent with the most context about your work does the best work.
Speed is the headline. Quality is the catch. A few practical guardrails:
Treat the pricing as a draft, not a decision. Supernormal can pull the pricing logic from your review meeting, but a human commercial lead should sign off before the response goes out. The same applies to legal terms and any custom commitments.
Make sure past responses are still accurate. The case study that won you a 2024 client might describe a service tier you've since changed. Agents work from the context you give them. Old context produces outdated answers.
Match the client's section structure exactly. RFP scoring is rigid. If the client asks for ten sections in a specific order, don't let the agent restructure them. Use the prompt to lock the structure.
Put one person in charge of final quality. The agent does the heavy lift. A named human owns the final read. This is true regardless of which AI you use, and it's especially true for high-stakes pitches.
If you're responding to your next RFP this month, the RFP response template is the fastest place to start. It's built specifically for agency RFPs: client-dictated structure, pricing breakdowns, team bios, methodology, references. Add your review meeting and your past work, and the first draft is on screen in moments. For other formats, the full proposal template collection covers project proposals, consulting proposals, and RFP responses in one place.
If you're not yet running your meetings with Supernormal taking notes, that's the step that unlocks the rest. The agent's quality depends on the context it has. Meetings are the context most agencies have most of, and the context they most often lose. Start by running your next RFP review meeting with the notetaker open. Everything else follows.
![\"Mac](\"https://framerusercontent.com/images/u12abTEzLBci8FXkyReL2baLiag.webp\")
The agencies that are pulling ahead on RFPs right now aren't the ones with the biggest sales teams or the most polished boilerplate. They're the ones who've figured out how to turn the work they already do, the meetings they already run, into the responses they need to send. That's the agency advantage. It's also what the right AI agent is for.
", "url": "https://www.supernormal.com/blog/drafting-rfp-responses-ai", "title": "How agencies draft RFP responses with AI | Supernormal", "summary": "Stop losing weekends to RFPs. Learn how agencies use AI to turn review meetings into ready-to-send responses, in a flash.", "date_modified": "2026-05-12T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "Client work" ] }, { "id": "urn:sha256:402eb623242f2511bdd14c9289dc99ae21f81b74fdab29bf3bd65732faadda2d", "content_html": "The best pitch decks don't follow a formula. They solve a specific problem: convincing skeptical investors that an unconventional idea isn't just viable, it's inevitable.
We analyzed the most famous pitch decks from companies like Airbnb, Uber, Facebook, and Stripe, plus proven frameworks from Y Combinator and Sequoia Capital. Some of these decks are legendary because they nailed one specific thing so well it became the new best practice. Others worked because the founders showed traction so strong that the deck almost didn't matter.
Here's what actually made them work.
Facebook's pitch meeting became legendary in Silicon Valley for its brevity. Mark Zuckerberg was an introverted 19-year-old between his sophomore and junior year at Harvard. Peter Thiel told him to leave for an hour. When he returned, they gave him a term sheet. The entire process took about two hours. Facebook raised $500,000 for a 10.2% stake, valuing the company at $4.9 million.
The deck worked because growth was so explosive that vision almost didn't matter. About 20 college campuses, 100,000 users, spreading virally across campuses without any marketing spend. But the real insight was in what they chose to emphasize: 75% of users returned daily.
That single metric told investors everything. This wasn't a novelty. Daily active usage would eventually translate to massive advertising potential, even if the monetization strategy wasn't fully formed yet.
Facebook's pitch deck was more media kit than traditional pitch, focusing relentlessly on user traffic, engagement, and growth metrics rather than vision or market size. When you have that kind of retention, the data speaks louder than any narrative.
The deck also showed how network effects were working. Each new user made the platform exponentially more valuable. As Facebook expanded to more schools, students weren't just accessing a tool, they were connecting to their entire social graph. The winner-take-all dynamics were obvious.
Growth rate matters, but engagement depth matters more. Facebook proved the product had become essential to users' daily lives. That's what converts a skeptical investor into a believer.
LinkedIn raised $4.7 million led by Sequoia Capital in November 2003 at a $10-15 million pre-money valuation. The Series A fundraise took four to five months in the post-dot-com bubble environment, when investors were still burned from social platform failures. Reid Hoffman later released the Series B deck from August 2004 publicly on his 10th anniversary.
What made LinkedIn's pitch work was showing three distinct revenue streams: subscriptions (premium accounts for power users), recruiting (job postings and candidate sourcing), and advertising. This diversification proved the platform could generate revenue multiple ways, reducing risk for investors.
While Friendster and MySpace were positioning as social networks, LinkedIn framed itself as business infrastructure. The B2B angle meant higher willingness to pay and enterprise sales potential. The recruiting revenue stream showed clear enterprise expansion potential - one recruiter using LinkedIn to find candidates could lead to company-wide adoption, then industry-wide network effects.
If you have multiple revenue streams, show them all. LinkedIn proved they weren't dependent on a single business model. This diversity gave investors confidence even if one stream underperformed.
Tesla raised $7.5 million in February 2004 (Elon Musk invested $6.5M and became Chairman), Series B raised $13 million in 2005, and Series C raised $40 million in 2006. By January 2009, Tesla had raised $187 million total, with Musk having contributed $70 million of his own money to keep the company alive.
On August 2, 2006, Elon Musk published his master plan: enter at the high end where customers pay a premium, then drive down market to higher volume and lower prices with each successive model. Roadster, then Model S, then affordable mass market. This stepped approach made an audacious vision feel achievable.
For a company attempting something most considered impossible (profitable electric vehicles), team credibility wasn't nice-to-have, it was essential. The deck put the team slide early, hitting hard with founder credentials upfront.
Tesla's deck was notable for backing every claim with charts, contracts, and concrete milestones. Supplier agreements, engineering specifications, production timelines. Specificity built credibility when the concept seemed far-fetched.
The Tesla Roadster was a sleek sports car built on a Lotus chassis that destroyed the perception that EVs were slow, ugly golf carts. By starting with a high-performance sports car, Tesla proved electric could be desirable, not just environmentally conscious.
Rather than asking investors to believe in a 20-year vision all at once, Tesla showed concrete milestones. Launch the Roadster by 2008, prove the technology, use those learnings and revenue to fund Model S development, then leverage that manufacturing expertise for mass market vehicles. Each vehicle wasn't just a product, it was proof of concept that de-risked the next, more ambitious model.
Uber's 2008 pitch deck helped raise $200,000 in early funding, eventually leading to an $11 million Series A in 2011. The deck worked because it distilled a massive market disruption into four words: everyone's private driver.
Not a taxi alternative. Not car service optimization. A private driver for everyone. The aspirational framing transformed a utilitarian service into a lifestyle upgrade that investors could immediately visualize.
The deck opened by establishing the painful status quo. Aging taxi fleets and inefficient technology - radio dispatch systems, physically flagging down cabs, calling dispatchers. These weren't abstract problems. They were daily frustrations every urban professional experienced.
But here's what made Uber's deck genuinely smart: they led with market size data showing the taxi and car service market was $4.2 billion in San Francisco alone. When your market is that big in a single city, you're not selling incremental improvement. You're showing category-creating potential.
The timing bet was less obvious in 2008. Smartphones were just emerging and BlackBerry led with 32% market share, iPhone had just 10%. Uber bet correctly that smartphone penetration would accelerate and location-based services would become standard, before either was obvious. That's the kind of insight that separates visionary decks from derivative ones.
Airbnb's original pitch deck raised $600,000 from Sequoia Capital in April 2009, after Y Combinator earlier that year. It's become one of the most studied fundraising presentations in startup history, and for good reason.
The problem slide is considered one of the best ever created. It stated three obvious truths: online bookings are too expensive, hotels lack local character, and there's no way to book rooms with locals instead of hotels. No jargon, no complexity. Just pain points any investor immediately recognized.
But the real genius was how they proved demand existed without having to ask investors to believe in the vision. Thousands of temporary housing listings already existed on CouchSurfing and Craigslist. People were already trying to solve this problem with inferior tools. This wasn't \"if you build it, they will come.\" This was \"they're already here, we're just giving them something better.\"
The business model explanation was equally direct: \"We take a 10% commission on each transaction.\" One sentence. No complex revenue streams, no confusing pricing tiers. Just a straightforward take rate that anyone could understand and calculate.
The financial slide showed that capturing just 15% of the market, with an average $25 transaction fee, could generate $200 million in revenue. This single slide validated both market size and business model. Investors could see the massive upside if the thesis proved correct.
What made Airbnb's deck work wasn't perfection. It was clarity. The competitive advantages slide was actually one of the busiest in the deck, but it clearly articulated six distinct advantages. The monetary host incentive differentiated them from CouchSurfing (which relied on altruism), while \"list once\" solved Craigslist's friction of posting to multiple cities manually.
The timing was perfect, and the deck made sure investors understood why. The 2008 recession created simultaneous demand for affordable travel options and income opportunities for homeowners with spare rooms. The timing wasn't coincidental, it was essential to the thesis. Your deck should answer \"why now?\" within the first three slides. Airbnb did.
Stripe raised $18 million led by General Catalyst and Sequoia in February 2012 at a $100 million valuation. The pitch positioning was fundamentally different from traditional payment processors: they framed the problem as \"payments is a problem rooted in code, not finance.\"
The hero slide showed \"7 lines of code vs 6-month enterprise integration\" compared to traditional processors. This single comparison communicated everything investors needed to understand. Developers could integrate Stripe in an afternoon instead of half a year.
While incumbents like PayPal and Authorize.net treated developers as an afterthought, Stripe made developer experience the core differentiator. Beautiful documentation, simple APIs, and no approval gatekeeping meant developers chose Stripe before procurement even got involved. That's a genuine competitive moat.
But the bigger insight was positioning payments as infrastructure, not product. Every business building online commerce was a potential customer. The TAM wasn't just current online merchants, it was every future internet business. Subscription billing, marketplace payments, international expansion - these were afterthoughts for incumbents but core Stripe features from day one.
If you're selling to technical buyers, prove you understand their pain better than anyone. Stripe's side-by-side code comparison showed not just that they were better, but that they fundamentally understood the problem differently than incumbents.
DoorDash's YC Demo Day pitch had just 3 minutes to convince investors. They raised $120,000 from Y Combinator in summer 2013, then $2.4 million seed round led by Khosla Ventures following Demo Day. Remarkably, they didn't make it onto the list of top Demo Day picks because the unit economics weren't clearly presented in the brief pitch.
The positioning was smart though. The deck showed three columns: Lead Gen (Seamless, Grubhub), Courier (TaskRabbit, Postmates), and Integrated. Lead generation platforms only showed menus without handling delivery. Courier apps did delivery but weren't built for food and didn't partner directly with restaurants. DoorDash positioned itself as the integrated solution bridging both sides.
The traction metrics were concrete: they cut average delivery times by 24 minutes in Palo Alto compared to similar cities, showed strong weekly order growth, and generated $1.5 million in annualized restaurant sales. The geographic expansion strategy was disciplined - start in a manageable market, prove the model, then replicate.
DoorDash's core insight was that most restaurants lacked delivery infrastructure. They enabled every restaurant to deliver, expanding the market beyond pizza and Chinese food to include the full range of dining options.
Beyond individual company examples, some frameworks have proven effective across hundreds of successful fundraises.
YC's framework was built for Demo Day, where founders have 2-3 minutes to convince a room full of investors to take a meeting. The structure reflects this time constraint: 10-12 slides maximum, one idea per slide, designed to be legible, simple, and obvious.
The 10 core slides:
Title slide - Company name and one-line description
Problem - What pain point are you solving, stated clearly
Solution - How your product solves it, in simple terms
Traction - The single metric that makes you look most impressive
Market - How big is the opportunity
Product - What it looks like and how it works
Business model - How you make money
Team - Why you're the right people to build this
Financials - Current status and projections
Ask - How much you're raising and what it unlocks
The traction slide is the most important element. YC companies are taught to choose the single metric that makes the company look most impressive and build the entire slide around it. DoorDash showed 31% week-over-week growth. Facebook showed 75% daily active users.
YC decks work because they answer a sequence of questions investors naturally ask when evaluating a startup. When you structure your deck around these questions, the story flows logically. The framework strips out everything that doesn't directly answer: Can I understand this in 3 minutes? Is the team capable? Is this a real business?
Want to build your own YC-style pitch deck? Check out our Y Combinator pitch deck template.
Sequoia's framework has become the global standard for structuring startup pitch decks. Unlike YC's Demo Day format, Sequoia's template isn't time-constrained. It's designed for the deck you send to investors before a meeting, the one they'll spend 3-5 minutes reviewing to decide if they want to talk.
The 10-12 core slides:
Title/cover slide - Company name and logo
Company purpose - Define your company in a single declarative sentence
Problem - What are the biggest problems you solve? Who has them?
Solution - How does your product solve these problems? What makes it unique?
Why now - What trends make now the perfect time for this product?
Market size - How big is your market? What's the revenue potential?
Competition - Who do you compete with? Why are you better?
Product - What does it look like and how does it work?
Business model - How do you make money? What are your unit economics?
Team - Who are the key people and their relevant experience?
Financials - Current metrics, projections, and what you're asking for
Appendix - Detailed financials, technical specs, additional team info
Two slides make Sequoia's framework distinct. The \"Company Purpose\" slide forces you to define what you do in one sentence before diving into details. This is harder than it sounds. Airbnb could have said \"we're a two-sided marketplace connecting hosts and travelers\" but instead went with \"book rooms with locals rather than hotels.\"
The \"Why Now\" slide is equally critical. It's not enough to show a big market. Sequoia wants to understand why this market is ready for disruption right now. Uber showed smartphone adoption accelerating. Airbnb showed the 2008 recession creating demand for affordable travel and extra income. LinkedIn showed professionals needed digital networking post-dot-com bubble.
Sequoia's framework has been used successfully by Airbnb, Brex, and Allbirds to secure funding from top-tier VCs. It works for growth-stage companies raising from investors who want depth, not just a quick pitch.
Want to build your own Sequoia-style pitch deck? Check out our Sequoia pitch deck template.
After analyzing these decks, a few patterns become clear.
Every winning deck established the pain point within the first 2-3 slides. Not the solution, the problem. Investors need to immediately understand what you're solving and why it matters now.
The best decks showed 10x improvement over existing solutions, not incremental gains. Uber wasn't 10% better than taxis. Stripe wasn't slightly easier than PayPal. They fundamentally changed how the category worked.
Notice the pattern in retention metrics. Facebook showed 75% daily return. The decks that worked didn't just show growth. They showed the product had become a daily habit. Growth rate matters, but engagement depth matters more.
Markets need to be large enough to build billion-dollar companies, but even niche plays need to demonstrate clear expansion paths. Uber showed the San Francisco taxi market alone was $4.2 billion. DoorDash showed restaurant delivery was vastly underpenetrated compared to other delivery categories.
The team narrative matters more at early stages. LinkedIn's deck worked partly because Reid Hoffman had credibility. Tesla's worked because the team credentials were front and center. Domain expertise, previous exits, or unique insights into the problem all work. Generic bios or missing relevant experience damage credibility.
Too much text on slides loses investors. The best decks use visuals, charts, and minimal text. If investors have to read paragraphs, you've lost them.
Avoid jargon and industry buzzwords. Your solution should be explainable to someone outside your industry.
Airbnb's \"we take a 10% commission\" is clearer than \"leveraging a platform-based marketplace model with dynamic pricing optimization.\"
Either show real traction or be honest about being pre-revenue. Hockey stick projections with no supporting logic hurt credibility.
Facebook showed actual user counts and retention.
The numbers need to be real.
Claiming you have no competition signals you don't understand your market. Every successful deck addressed competition directly.
Airbnb showed CouchSurfing and Craigslist.
DoorDash showed Seamless, Grubhub, TaskRabbit, and Postmates.
Then they explained why their approach was different.
The best pitch decks are built from real customer insights, market data, and team expertise. Supernormal helps you turn your actual conversations into compelling pitch materials.
Supernormal captures your customer calls, user research sessions, and team strategy meetings. When you need to create your pitch deck, pull insights directly from these conversations instead of trying to remember what was said.
Your investor calls, user feedback sessions, and market research discussions contain the traction metrics and validation points investors want to see. Supernormal helps you surface these insights and generate slides built from your actual meeting notes and customer conversations.
Ready to create your own pitch deck? Get started with Supernormal's AI slide templates and turn your meetings into compelling investor materials.
Studying successful pitch decks reveals that fundraising comes down to a few core things.
Make the pain point obvious in the first few slides. Show traction, market data, or behavior shifts proving demand exists. Demonstrate why you're uniquely positioned to win. Paint the picture of what success looks like and how you'll get there. Invest in design and storytelling that respects investors' time.
The best pitch decks tell stories backed by data. Start with your customer conversations, extract the insights that prove demand, and build your narrative around real proof points.
", "url": "https://www.supernormal.com/blog/pitch-deck-examples", "title": "Pitch deck examples that raised $200M+ | What worked", "summary": "Analysis of pitch decks from Airbnb, Uber, Facebook, and more. See what convinced investors, plus real frameworks you can use.", "date_modified": "2026-04-20T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "Client work" ] }, { "id": "urn:sha256:2c2cefc6566a5d7ef4b8d7caef30cb593bb8923c23588baba25e39903af9a9df", "content_html": "If you've ever used Claude to complete tasks after a meeting, you know the routine: find the transcript, copy a chunk of it, paste it into the conversation, add your prompt, and finally get to the actual work. Every single time.
It works, but the copy-pasting is the problem. It's friction before the thinking even starts, and it means you're only ever working with whatever you remembered to grab.
An MCP connection removes that step. Once it's set up, Claude can read your meeting history directly. No copying. No pasting. Just ask, and the context is always there.
This post covers how to connect the MCP and six ways to start using it today.
If you're using Claude on the web (Pro, Max, Team, or Enterprise), go to claude.ai/customize/connectors, add a custom connector, name it Supernormal, and paste in the server URL. Authenticate, and you're done.
If you're using Claude Code, run one command:
claude mcp add supernormal --transport http \nhttps://api.supernormal.com/mcpSupernormal MCP also works with Cursor, ChatGPT, and any other MCP-compatible tool. Full setup instructions for each are in the Help Center.
Once the MCP is connected, give it a quick test in Claude for the web or Claude Code by asking something like:
What was discussed in my last meeting?If Claude comes back with real details from your transcript, you're ready to go.
Every meeting ends the same way: action items get assigned, people head back to their desks, and those items exist only in a transcript nobody is going to re-read in full. Someone will follow up on the obvious ones. The rest will quietly fall through.
After a planning session or standup, the last thing you want to do is paste a transcript into Claude and ask it to find the action items. You shouldn't have to. Claude can go get them directly.
Try this:
Pull all the action items from yesterday's planning meeting \nand list them with owners and next steps.Because Claude is reading the full transcript, it catches the follow-ups that got mentioned in passing, not just the ones that came with a name attached. The offhand \"can you look into that?\" gets captured the same as the formally assigned task. You get a complete list, not a partial one.
From there, you can ask Claude to format it as a checklist, draft it into a follow-up email, or push it into whatever system your team uses to track work. Note that you can also run all these prompts in Supernormal itself after your meeting, but if you're working on other tasks it tools like Claude, it can be useful to have everything in one place.
When you're about to commit to a project, there's usually a gap between what the team thinks clients want and what clients actually said. Closing that gap normally means going back through call recordings, and nobody has time to do that thoroughly.
The feedback you need is spread across many conversations. One clients mentioned it in passing three weeks ago. Another brought it up as a blocker. A third had a workaround. None of that makes it into a summary doc. It just sits in transcripts.
Claude searches across all the meetings in a Supernormal project, and any other project context you have stored in a Claude project.
Try this:
Find every time [project workstream] is mentioned across \nour calls in the last 30 days, and the work we've \ngenerated in [Claude project name]. What are the common themes?You get the full picture across every relevant call and Claude output, not just the ones you happened to remember, and not filtered through whoever wrote up the notes. That's the difference between working from gut feel and working from real context.
Your meeting library grows fast. Discovery calls, client check-ins, internal planning sessions, stakeholder updates: they all pile up in one long list. Getting them organized into coherent projects means going through each one manually, figuring out which belong together, and naming the groupings in a way that actually makes sense.
It's tedious work. You know it needs to happen, but you also know it will take an hour you don't have, so it sits there unorganized.
Claude can do it in seconds.
Try this:
Organize all my recent Supernormal meetings into projects.\nCreate the projects and move the meetings into them.Claude reads across your meeting history, identifies the patterns, groups related conversations, and suggests project names that reflect what the work is actually about. Not generic labels like \"Client Meetings\" but specific ones like \"Q2 Brand Refresh\" or \"Onboarding Flow Redesign.\"
From there, you can ask Claude to create those projects in Supernormal and move the meetings into them. Or if you want to refine the structure first, ask it to suggest a different breakdown. The point is you're reviewing and approving, not starting from scratch and guessing which meetings go where.
This is especially useful when you're new to a team and inheriting a backlog of unstructured meeting notes. Instead of trying to piece together what each conversation was about, Claude does the first pass for you.
A good design kickoff or client discovery session produces a lot of raw material: goals, constraints, references, feedback, open questions. Then the meeting ends and someone has to shape all of that into a brief the team can actually work from.
That part is slow. Going back through the recording, finding the key moments, pulling them into a doc, and making it coherent is an hour of work that feels like it shouldn't exist.
Skip it. Ask Claude to write the brief directly from the meeting.
Try this:
Based on our kickoff with [client name], write up a design brief \ncovering objectives, audience, constraints, references mentioned, \nand open questions.The output won't be final and you'll edit it, but you're editing instead of drafting from scratch. More importantly, you're starting from what was actually said, not from your memory of it, which is already getting fuzzy.
If you want a head start on structure, we have a design brief template you can use as the basis for your prompt. You can also pull from multiple meetings: if the brief needs client requirements from one call and internal constraints from a separate team session, ask Claude to combine both. Then you can ask Claude to push the brief into whichever collaboration tools you use and that you have connected to Claude, like Notion, Google Slides, or others.
When a new person joins and documentation is thin, the knowledge they need isn't missing. It's scattered across past conversations. How a process works, why a decision was made a certain way, what was tried before: it all exists somewhere in your meeting history.
Normally getting to it means tracking down relevant meetings one by one and hoping you got them all. Claude reads across all of them at once.
Try this:
Summarize how we handle [process or system] based on team \ndiscussions from the past 60 days. Write it for someone \njoining the team for the first time.This works for onboarding, but it's just as useful any time you need to explain something that's never been written down. Instead of finding a quiet hour to write a doc from scratch, you ask Claude to pull together what your team has already discussed.
Client-facing teams know what comes after every call: write the follow-up email, update the project management tool, log the next steps, assign the action items. Done carefully, it takes 20 to 30 minutes. Done quickly, things get missed.
A customer success manager shared this workflow on Reddit recently: after every call, they drop the transcript into Claude, which drafts a follow-up email straight to their drafts folder and generates structured CRM notes with a summary, next steps, and action items. The whole thing takes two minutes. They review and approve everything before anything goes out. Nothing reaches a customer without them seeing it first.
With Supernormal MCP, the transcript step disappears. Claude already has it.
Try this:
Based on my last call with [client name], draft a follow-up email \nrecapping what we discussed and listing agreed next steps. \nThen give me CRM notes with: summary, next steps, and action items \nwith owners.One thing worth highlighting from that Reddit thread: the consistency gain matters as much as the time saving. When you write notes manually, the detail depends on how tired you are. When Claude does it, every record looks the same. Six months later, when you're reviewing a deal or handing off an account, that adds up.
Not everyone wants to add another tool. If that's you, everything above is also available directly inside the Supernormal web app. Ask questions, generate briefs, pull action items, without needing a separate AI subscription.
The MCP connection is for teams who are already in Claude, Cursor, or another AI tool and want their meeting context to follow them there.
", "url": "https://www.supernormal.com/blog/meeting-notes-in-claude-mcp", "title": "How to work with your meeting notes in Claude using an MCP", "summary": "Stop copy-pasting transcripts into Claude. Connect Supernormal with MCP and your meeting history is already there. Here are six ways to put it to work.", "date_modified": "2026-04-10T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "AI and agents" ] }, { "id": "urn:sha256:cd0f1f5ab3fe0528bf864e653ecd837bfc7c41491a668b0c52f4382655080afa", "content_html": "Most people do almost no meeting prep. They skim the invite, glance at the attendee list, and hope muscle memory carries them through. This is especially common in fast-paced, client-facing, or cross-functional roles where your day jumps from strategy to status to creative review with almost no pause in between.
And sometimes winging it works, we’ve all been there and gotten lucky. But when your day is stacked with back-to-back calls and constant context switching, momentum slips fast. Meetings drift. Decisions take longer. Follow-ups slip.
Good meeting prep really comes down to knowing why you are walking into the room, what you need from the conversation, and what the other person needs from you. When you get those basics right, the call feels lighter. You make faster decisions. You leave with clarity instead of follow-up chaos. Most people avoid prep because they assume it takes too long. In reality, a few minutes of direction transforms the entire call.
Here are practical ways to prep for the meetings you deal with every week, using steps that take minutes, not hours.
If you have only a few minutes before a call, this is the highest-ROI prep you can do. It sets direction, reduces drift, and gives you a grip on the conversation even if you’re jumping in cold.
Minute 1: Confirm the goal
Write a single sentence that starts with: “This meeting is successful if…”
Minute 2: Skim recent context
Search your email, calendar notes, Slack, or project docs for the latest conversation or update related to this meeting.
Minute 3: Identify your talking points
List three things you need to cover. Not ten. Not six. Three.
Minute 4: Anticipate what they’ll ask
Note the top question or objection you’re likely to hear and prepare a straightforward answer.
Minute 5: Prep the opener
A simple line that sets the direction: “Here’s what I’m hoping we get through today: X, Y, and Z. Does that work for you?”
Copy this checklist into a Google Doc, Notion page or whatever workspace you use, and keep reusing it. It works whether you’re meeting a new stakeholder, reviewing a project, or hashing out next steps with your team.
Add a little depth:
Purpose, People, Process
A lightweight version of the 7Ps framework.
Purpose: what we are trying to achieve.
People: who is here and why they matter.
Process: how we will run the time, in three or four bullets.
Context pack
One slide or one page with previous decisions, open risks, and links to source docs. Keep it scannable.
Two routes to yes
If your first proposal is blocked, what is the backup that still moves you forward?
Pre‑reads
If there are important docs or context people should review before a call, a pre read helps everyone come prepared, not just you. Keep it short, add a three bullet summary at the top, and label it clearly. You can attach it to the invite, drop it in Slack, or email it. Send it 24 hours ahead, if possible.
I recently dropped a Reddit thread which asked, “What tools do you use for meeting prep?” The answers covered the full spectrum. Some people use simple frameworks and one‑page notes. Others use pen and paper. A few rely on caffeine and instinct. And several called out specific tools.
“You can use (Microsoft) Teams to collate historical meetings and emails from each attendee and use it to prepare meeting notes and agendas ahead of time. I keep a running document of stakeholder profiles to feed into Copilot so I can think through their perspectives and wants or needs.” - Commenter, r/ProductManagement
Patterns across the thread, and across most teams:
Simple works. Many keep a running agenda in a Google Doc or Notion. One page per project.
Frameworks help under pressure. A stripped 7Ps or one line purpose keeps calls from drifting.
Tool choice follows habit. Some prep inside the task itself, for example inside ClickUp or Jira.
For tooling, use the simplest tool you will actually open. Then be consistent.
Most teams spend their week jumping between strategy reviews, stakeholder check-ins, creative discussions, and recurring rituals like stand-ups or 1:1s. Some meetings are easy to wing, but others fall apart fast without a little structure. These playbooks cover the moments where prep has the highest payoff.
Senior reviews move quickly and reward clarity over context. Whether you are presenting to a leadership team, an executive sponsor, or the person who signs off budgets, people decide fast and skim even faster. Most teams over prep detail and under prep direction.
Your aim: a clear decision, not a tour of your work.
Step | What to do |
Prep the day before | Two slides only: the decision you need and the three facts that support it. Make a short list of what you will do if the answer is no. Calendar a ten minute pre brief with an ally in the room. |
Run it | Open with your ask, then support it. Keep screen share on the decision slide. Leave a few minutes to confirm owners, dates, and next reporting point. |
Quick script | “Today I am asking for approval to expand the beta by 300 seats. The three reasons are adoption, cost per seat, and support load. If we cannot approve today, I am proposing a two week data extension. Here is the plan either way.” |
Write your ask in one sentence so you can deliver it without scrolling or caveats.
You feel comfortable explaining the core of the meeting without scrolling through notes.
Saving the ask until the end, over-explaining the background, or trying to cover every detail.
Send a short recap with the decision, owners, and dates. Keep it simple.
These are the conversations people procrastinate: misalignment, performance concerns, disagreements, or bad news. When emotions run high, even small misunderstandings land heavily. This is especially true in client relationships, team disputes, or tense project reviews where stakes feel personal.
Without a plan, the conversation spirals or stalls. Prep helps you stay calm, steady, and fair.
\"I am an over-prepper - I make lots of notes over a few days to get out what I want to say, and make sure that it is direct but with sensitivity. Lately, I also put it through ChatGPT to see if I can get feedback if it comes off too emotional and therefore takes away from my points I want to make.\" - CX Manager, B2B SaaS
Your aim: protect relationships, surface facts, agree on one next step.
Step | What to do |
Prep | Write a neutral sentence that describes the issue without blame. List three facts and two feelings you have seen on each side. Decide your boundary, for example, “We cannot ship without security sign off.” |
Run it | Start with the shared goal. Ask permission to suggest a process. Use short turns. Reflect back what you heard. End with one clear next step and a follow up time. |
Phrases that help | “Here is what I am observing and why it matters.” “What is the smallest step we can agree to today?” “Let me say back what I heard to check I have it right.” |
Write one neutral sentence, a statement of fact over emotion..
You can describe the issue in a way that feels fair to both sides.
Jumping into solutions too fast, debating interpretations, or reacting to tone instead of content.
Send a short, factual recap of what you both agreed.
These calls hinge on confidence and direction. Whether it is a pitch to a prospective client, a renewal, or an investor update, people want a clean narrative: where things stand, what is working, what is not, and what you need from them. Anything scattered reduces trust.
Your aim: build trust and secure a specific commitment.
Step | What to do |
Prep | One page with a timeline, three metrics, the story behind them, and the ask. Make a short list of likely questions and clear answers. |
Run it | Start with the outcome you want, then progress, then obstacles, then the ask. Share a draft email or next step they can approve or forward. |
Quick script | “We are on track for the milestone due on the 30th. Two risks remain. If you are comfortable with the plan, the next step is an introduction to the data team. I drafted a note you can send. Can I drop it in chat now.” |
Write your ask clearly enough that you do not have to improvise it.
You know the exact action or commitment you want before the call starts.
Diving straight into metrics, softening risks, or ending without a next step.
Send the draft you referenced. Make it easy for them to forward or approve.
These meetings get messy fast. Some stakeholders come without an agenda, and internal teams often over-explain or context dump. The result is a call where everyone talks, but nothing moves forward. Prep keeps the conversation focused and protects the relationship.
Your aim: leave with a shared decision or clear next step.
Step | What to do |
Prep | One page with the last milestone, what is in progress, risks or asks, and what you need from them. |
Run it | Start by stating the goal for the call. Share progress in three clear bullets. Confirm next steps with owners and dates before you close. |
Bonus tip | Keep updates short. Ask: “Before we jump in, is there anything specific you want to cover today?” |
Decide the one outcome you want them to leave with.
You can explain progress and the ask in under a minute.
Letting the call turn into a broad status update, assuming they remember last week’s decisions, or presenting too much detail too early.
Send a short recap the stakeholder can forward internally without edits.
These sessions can get messy, fast. Different priorities, different roles, and too many ideas competing for airtime. Without structure, the meeting expands instead of converging. Prep gives the group rails to run on so you leave with decisions, not a larger to-do list.
Your aim: leave with named owners and dates.
Step | What to do |
Prep | Create a simple outline with three sections: Decisions, Open Questions, and Parking Lot. Add rough times to each section. |
Run it | Assign a note taker at the start. Use the parking lot to keep the conversation on track. End with a one minute recap while everyone is still in the context. |
Bonus tip | Start by saying, “Here is what we need to decide today,” then stick to it. |
Pick the two decisions that must be made today. Everything else goes in the parking lot.
You can describe what “done” looks like for the meeting before it starts.
Trying to solve everything live, letting side conversations take over, or skipping the recap.
Send notes quickly with decisions, owners, dates, and anything moved to the parking lot.
If you don’t have time or energy to build your own structure right now, start with something simple you can reuse.
Paste the following into a document, or add your context to this template here.
Goal:
Outcome I want by the end:
Top two questions:
Attendees and roles:
Last decision and link:
Risks I see:
Close script:
For example, your goal might be ‘Align on the Q1 scope,’ and your close script might be, ‘Before we wrap, here’s the decision I’d like us to confirm today.’
Welcome and goal, 2 minutes
Topic A, 8 minutes
Topic B, 8 minutes
Decisions and owners, 5 minutes
Next steps and timing, 2 minutes
![\"A](\"https://framerusercontent.com/images/lcL7driEcsb0BOCYdxVza8WUM.webp\")
You do not need a heavy system to prepare well, but a few tools can remove busywork. Calendar add ons and AI assistants can pull attendee bios, grab recent email threads, find the last doc you used, and draft talking points from past notes.
When you join a recurring meeting, Supernormal shows three key points from your last call. That quick refresh means you walk in ready, not scrambling to remember what happened last week.
![\"Daily](\"https://framerusercontent.com/images/yk5wE9Z5PQLRdWlLvOTRyJfqIA.png\")
Those last few seconds before you join a call are where most meetings are won or lost. A quick reset helps you show up clear, steady, and ready to lead the conversation instead of reacting to it.
Read your one line goal out loud.
Check your ask, owner, and date are clear.
Open your recap script.
Put links in a single note so you can paste fast.
Mute notifications.
And when the meeting ends? If you’ve used Supernormal, you’ll have all the decisions, action items, and context to get the work done.
", "url": "https://www.supernormal.com/blog/meeting-prep", "title": "Stop winging it: meeting prep that actually helps | Supernormal", "summary": "Learn fast, effective meeting prep habits that reduce drift and speed up decisions. Includes checklists, playbooks, and tips for high-impact calls.", "date_modified": "2026-04-02T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "Meetings" ] }, { "id": "urn:sha256:97880c304cd09c215d564b364a551ecd42059685d943af8e9469f1a880f8fae1", "content_html": "If you're new to Zoom, joining a meeting can seem daunting. Have no fear! This guide covers everything you need to know about joining Zoom meetings, including troubleshooting common issues.
Before you can join a meeting, you'll need to get Zoom set up on your computer or mobile device. Here's how:
If you'll be using Zoom frequently, download the Zoom app. This will give you full access to all of Zoom's features. Go to zoom.us/download and click \"Download\" under \"Zoom Client for Meetings.\" Install the app on your device once the download is complete.
If this is your first time using Zoom, you can join a meeting without downloading the app. Just make sure you have the latest version of Google Chrome, Microsoft Edge, or Safari web browser on your computer.
On a mobile device, you can join a meeting via the Zoom mobile app or your web browser. The app can be downloaded from the App Store (iPhone and iPad) or Google Play Store (Android devices).
That's it for setup! As long as you have Zoom installed or a compatible web browser, you're ready to join meetings.
Now comes the fun part. Actually joining a meeting is simple. Follow these steps:
On a computer:
Open the Zoom desktop app or web browser.
Click \"Join a Meeting\" if prompted. Otherwise, go to zoom.us/join.
Enter the meeting ID provided by the meeting host/organizer. The meeting ID is a 9, 10, or 11-digit number.
Click \"Join.\"
On a mobile device:
Open the Zoom mobile app. Tap \"Join a Meeting.\"
Enter the meeting ID and tap \"Join Meeting.\"
If prompted, enter the meeting passcode provided by the host.
That's all there is to it! You should now be joining the meeting.
A few things to keep in mind:
You may need to enter your name so the host knows who is joining.
The host may require a passcode. This will be provided to you ahead of time.
If you join before the host, you may be placed in a waiting room. Don't worry. The host will admit you once the meeting begins.
Before the meeting starts, you'll need to set up your audio and video settings. Follow these tips for the best audio/video experience:
For audio:
If using a desktop/laptop, join computer/device audio by clicking \"Join Audio\" in the dialog box.
On mobile, tap \"Call via Device Audio.\" This uses your phone's mic and speakers.
If calling in, dial the number provided and enter the meeting ID when prompted.
For video:
On desktop/laptop: Click \"Start Video\" to turn on your webcam so others can see you.
On mobile: Tap the camera icon to turn on your device camera.
If you don't want others to see you, keep video off. But you'll still be able to see everyone else.
You can test your speakers/microphone before joining to ensure proper audio setup. Running a bandwidth test also helps avoid glitches.
Make sure you join in a quiet space without background noise so everyone can hear clearly. And adjust your video framing so your face and shoulders are visible on camera.
Once you're in the meeting, familiarize yourself with the Zoom controls and features:
Audio/Video buttons - Mute/unmute your microphone or turn video on/off.
Participants list - See who's currently in the meeting.
Chat - Message other participants or the group.
Share screen - Share your screen or a document with meeting attendees.
Record - Start/stop a local or cloud recording (if enabled by host).
Reactions - Provide visual feedback during screen sharing using icons like Clap or Thumbs Up.
Leave meeting - Leave the meeting while it continues for other participants.
Take advantage of tools like chat, reactions, and screen sharing to actively engage in the meeting! But keep your mic muted when you aren't speaking to minimize background noise.
If you're hosting a meeting, there are additional controls like managing participants, starting breakout rooms, and more. We don't need to get into those here, but just know the host has extra functionality.
Even with clear instructions, you might run into issues when joining a Zoom meeting. Here are the most common problems and how to fix them.
Problem: You see \"Invalid meeting ID\" or \"This meeting ID is not valid\"
How to fix it:
Check for typos - Meeting IDs are 9, 10, or 11 digits. Verify each number.
Remove spaces and dashes - Enter only the numbers (no spaces or special characters).
Confirm the meeting is active - Meetings can expire or be deleted. Ask the host for a current link.
Try the meeting link instead - If you have an email invitation, click the link rather than entering the ID manually.
Check if the meeting has ended - Past meetings can't be rejoined unless they're recurring.
If none of these work, ask the host to send a fresh meeting invitation.
Problem: You joined the meeting but can't hear other participants
How to fix it:
Click \"Join Audio\" - Look for this button in the bottom-left corner if you skipped audio setup.
Check if you're muted - Click the microphone icon to unmute (you'll hear others even while muted).
Increase your device volume - Use your computer or phone volume controls.
Test your speakers - Go to Settings → Audio → Test Speaker to verify sound is working.
Select the correct audio output - Click the arrow next to the microphone icon → Select a Speaker → Choose your headphones or speakers.
Close other apps using audio - Some apps block Zoom from accessing your speakers.
Leave and rejoin - Sometimes reconnecting fixes audio issues.
Use phone dial-in as backup - Dial the phone number from the meeting invitation and enter the meeting ID.
Problem: Your video won't turn on or appears frozen
How to fix it:
Click \"Start Video\" - Make sure the camera icon isn't crossed out.
Close other apps using your camera - Only one app can use your camera at a time. Close FaceTime, Photo Booth, or other video apps.
Check camera permissions:
Mac: System Preferences → Security and Privacy → Camera → Check the box next to Zoom
Windows: Settings → Privacy → Camera → Allow apps to access your camera → Turn on for Zoom
iPhone/Android: Settings → Zoom → Allow Camera access
Select the correct camera - Click the arrow next to the video icon → Choose a Camera → Select your webcam.
Update Zoom - Outdated versions can have camera bugs. Check for updates in Settings.
Restart your device - This resolves most camera connection issues.
Test your camera outside Zoom - Open Photo Booth (Mac) or Camera app (Windows) to verify your camera works.
If your camera still doesn't work, you can join audio-only and use chat to participate.
Problem: You see \"Please wait, the meeting host will let you in soon\" and you're not admitted
What this means:
The host has enabled the waiting room feature for security. This is normal and you need to wait.
What to do:
Wait patiently - The host will admit you when they're ready or when the meeting starts.
Check the meeting time - Make sure you haven't joined too early. Hosts may not admit people more than 5-10 minutes before the scheduled time.
Verify you joined the correct meeting - Check the meeting ID matches your invitation.
Contact the host - If you've waited more than 10 minutes past the scheduled start time, message or call the organizer.
Try leaving and rejoining - Sometimes this refreshes your request to join.
If the host is running late, there's nothing you can do except wait. Don't keep leaving and rejoining as this creates multiple requests the host has to manage.
Problem: Zoom is asking for a meeting passcode and you don't have it
How to fix it:
Check your email invitation - The passcode is usually near the meeting ID (typically 6 digits).
Look in your calendar event - If you added the meeting to your calendar, check the event details.
Try clicking the meeting link - Passcodes are often embedded in the meeting URL, so clicking the link bypasses the passcode prompt.
Check for case sensitivity - If the passcode includes letters, make sure you match uppercase and lowercase exactly.
Ask the meeting organizer - Contact the host for the current passcode.
Note: Passcodes are different from meeting IDs. The passcode is an additional security measure.
Problem: You see \"Connecting...\" indefinitely or \"Unable to connect\" error
How to fix it:
Check your internet connection - Try loading a website to verify you're online.
Switch networks - Try a different WiFi network or switch from WiFi to mobile data (on phone).
Restart your router - Unplug for 30 seconds, then plug back in.
Close bandwidth-heavy apps - Streaming video, large downloads, or cloud backups can slow your connection.
Disable VPN temporarily - Some VPNs interfere with Zoom. Try disconnecting.
Check your firewall - Corporate or school networks may block Zoom. Ask your IT department to whitelist zoom.us.
Try the browser version - Go to zoom.us/join in your browser instead of using the app.
Clear Zoom cache:
Open Zoom → Settings (gear icon) → Advanced (scroll down)
Click \"Clear\" next to cached data
Use a different device - If available, try joining from your phone or tablet.
If you're on a work or school network, Zoom might be blocked. Contact your IT administrator.
Problem: The Zoom app crashes immediately or won't launch
How to fix it:
Force quit and reopen - Close Zoom completely and try again.
Restart your device - This clears temporary issues.
Update Zoom - Download the latest version from zoom.us/download.
Check for operating system updates - An outdated OS can cause compatibility issues.
Free up storage space - If your device is full, apps may crash. Delete unused files or apps.
Uninstall and reinstall Zoom:
Mac: Drag Zoom to Trash → Empty Trash → Download fresh copy from zoom.us
Windows: Control Panel → Uninstall a Program → Select Zoom → Uninstall → Download fresh copy
Mobile: Delete app → Reinstall from App Store or Google Play
Join from browser - As a temporary solution, use zoom.us/join in your web browser.
Problem: You see \"Your browser does not support Zoom\" or features aren't working
How to fix it:
Update your browser - Zoom requires recent browser versions. Update to the latest:
Chrome: Click the three dots → Help → About Google Chrome
Edge: Click the three dots → Help and Feedback → About Microsoft Edge
Safari: Update through Mac App Store (Settings → Software Update)
Firefox: Click menu → Help → About Firefox
Try a different browser - Chrome works best with Zoom. Download it from google.com/chrome if you don't have it.
Clear browser cache:
Press Ctrl+Shift+Delete (Windows) or Cmd+Shift+Delete (Mac)
Select \"Cached images and files\"
Click \"Clear data\"
Disable browser extensions - Some extensions block Zoom. Try in an incognito or private window first.
Allow camera and microphone permissions:
Look for the camera/microphone icon in your browser's address bar
Click it and select \"Allow\"
Download the Zoom app - Browser version has limitations. The app provides full functionality and better performance.
Problem: You hear an echo or feedback noise during the meeting
How to fix it:
Mute yourself - If you're not speaking, click the mute button.
Use headphones - This prevents your speakers from feeding back into your microphone.
Check if multiple devices are connected - If you joined from both your computer and phone, mute one device.
Move away from other participants - If you're in the same room as someone else in the meeting, only one person should use audio.
Lower your speaker volume - This reduces the chance of feedback.
Select the correct audio device:
Click the arrow next to the microphone icon
Choose correct microphone and speakers
Test the setup
Problem: Video is pixelated, choppy, or constantly buffering
How to fix it:
Check your internet speed - Zoom requires at least 1.5 Mbps for video calls. Test at speedtest.net.
Turn off your video - This reduces bandwidth and improves audio quality.
Close other apps and browser tabs - Free up bandwidth and processing power.
Move closer to your WiFi router - Or connect via ethernet cable for best performance.
Ask others to turn off video - If the meeting is large, fewer videos = better performance.
Lower video quality in settings:
Settings → Video
Uncheck \"HD\" or \"Enable HD\"
Exit other video calls - Make sure you're not in multiple video conferences.
Restart your router - This can improve connection speed.
If you're having any Zoom joining issue, try these steps first:
Restart Zoom - Force quit and reopen the app
Restart your device - Fixes most temporary glitches
Update Zoom - Get the latest version from zoom.us/download
Check internet connection - Visit a website to verify you're online
Try the browser version - Go to zoom.us/join
Use a different device - Try joining from your phone or tablet
Contact the host - Ask for a fresh meeting link or meeting ID
If problems persist after trying these solutions, visit support.zoom.us for additional help or contact Zoom support directly.
Once you've joined your Zoom meeting, Supernormal can handle the work that comes after. Instead of spending time on meeting notes and follow-ups, you get the actual deliverables you need: campaign briefs, client emails, project plans, and meeting summaries formatted for your team.
Supernormal captures your meetings without a bot and creates these deliverables while you focus on the conversation. Download the desktop app and it detects when your meeting is starting. No need to take notes or remember to hit record.
Joining a Zoom meeting for the first time may seem intimidating, but it's quite straightforward once you learn the basics. With just a Zoom install or compatible web browser, entering a meeting ID, and configuring your audio/video, you'll be ready to meet and collaborate in no time.
Remember these tips:
Download the Zoom app or use a current web browser
Enter the 9, 10, or 11-digit meeting ID assigned by the host
Set up your preferred audio and video options before joining
Use meeting features like chat, screen sharing, and reactions
Leverage Supernormal to transcribe meetings and provide summaries
With this handy guide, you can now hop on a Zoom call like a pro. Here's to successful video conferencing!
", "url": "https://www.supernormal.com/blog/how-to-join-a-zoom-meeting", "title": "How to join a Zoom meeting: Complete guide (2026)", "summary": "Learn how to join Zoom meetings on any device in 2026. Step-by-step guide with troubleshooting for desktop, mobile, and browser. No account needed!", "date_modified": "2026-03-23T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "Meetings" ] }, { "id": "urn:sha256:13e8109dead2fca48689fc3e60a9d11ef42dc56b4dcea35cd8972b2a40a03c0b", "content_html": "On February 27, 2026, OpenAI signed a deal with the Pentagon to deploy ChatGPT for domestic surveillance and autonomous weapons systems. The deal came hours after Anthropic was blacklisted for refusing the same terms. Within 72 hours, ChatGPT uninstalls spiked while Claude hit #1 on the App Store. A #QuitGPT movement emerged.
On March 7, Caitlin Kalinowski, OpenAI's robotics chief, resigned, saying \"surveillance of Americans without judicial oversight and lethal autonomy without human authorization are lines that deserved more deliberation.\" Even OpenAI's own employees signed an open letter supporting Anthropic. Sam Altman admitted the deal \"was definitely rushed, and the optics don't look good.\"
If you're one of the hundreds of thousands deleting ChatGPT, here's how to move your work elsewhere and find an alternative.
Before you delete anything, try to save your conversation history. You've spent months building up prompts, refining outputs, and documenting your process.
Here's the problem: many people can't actually export their data.
Official export method:
Go to Settings → Data Controls
Click \"Export data\"
Click \"Confirm export\"
Wait for an email from noreply@openai.com (check spam/promotions)
Download within 24 hours before the link expires
Reality check: Many users report export emails never arriving, even after multiple attempts. Some exports take up to 7 days. If this happens to you, skip to the manual backup method below.
Bad news: You can't export workspace conversations. At all.
OpenAI doesn't allow Team or Enterprise users to export workspace data, only personal workspace chats. If your work conversations are in a Team workspace, they're locked in. This has raised GDPR concerns and left business users unable to retrieve their own work.
If export isn't working or isn't available, manually save your most important conversations:
Open each conversation you want to keep
Copy the entire conversation text
Paste into a Google Doc or text file
Save with a descriptive filename
Yes, this is tedious. Yes, it's ridiculous that a company making billions can't provide proper data portability. But it's better than losing everything.
What to save:
Your best prompts and templates
Conversations where you refined complex outputs
Custom instructions you created
Any GPTs you built (copy the configuration)
Leaving ChatGPT isn't only about saving your chats. It's also about what OpenAI keeps and what it does with it.
Training: On free and Plus accounts, OpenAI uses your conversations to train its models unless you opt out. You can turn that off before you go: open Settings, then Data Controls, then switch off \"Improve the model for everyone.\" That stops your future chats being used for training, though it doesn't pull back anything already used. Team, Enterprise, and API data isn't used for training by default.
Retention: Even with training off, OpenAI holds onto conversations for 30 days for abuse monitoring, and reviewers can read them during that window. So opting out limits future training, it doesn't make your chats private overnight.
Deletion: When you delete your OpenAI account, OpenAI says it removes your data within 30 days, subject to legal retention. That's permanent, so export or copy anything you want to keep first.
Not all alternatives are built the same. Before switching, figure out what you actually need:
Quick questions and research - Any AI chatbot works
Writing and editing - Look for models strong in language (Claude, Gemini)
Code and technical work - Consider coding-specific tools (Cursor, GitHub Copilot)
Client work and deliverables - You need something that remembers context across conversations
Meeting notes and documentation - Specialized tools handle this better than general chatbots
If you were using ChatGPT for more than one of these, you might need more than one replacement.
Here's what you lose when you leave ChatGPT: all the context you built up in custom instructions, pinned conversations, and GPTs you created.
Every AI chat tool has the same fundamental problem: conversations reset. You start fresh every time. That's fine for \"Explain quantum physics\" but terrible for \"Draft a status report for the Acme project using the format we discussed last week.\"
When evaluating alternatives, ask:
Can it pull context from my actual work (emails, docs, meetings)?
Does it remember previous conversations without me re-prompting?
Can I organize work by project or client?
Will I still be copy-pasting between tools, or does it integrate with my workflow?
Context matters for more than convenience. When a tool doesn't have the facts, it fills the gaps with a confident guess, and in client work that's where a lot of mistakes come from. More on why accuracy depends on context.
The alternatives at a glance:
Claude: Best for writing, analysis, and anyone leaving over ethics. Keeps your context with Projects and memory. Watch-out: full integrations need a paid plan.
Gemini: Best if you already live in Google Workspace. Pulls context from Gmail and Docs. Watch-out: it only shines if you're all-in on Google.
Perplexity: Best for research and current information. Doesn't use your files. Watch-out: not built for writing or deliverables.
Supernormal: Best for client work drafted from your meetings. Pulls context from your meetings, emails, and docs. Watch-out: it needs the meeting notetaker running to pull meeting context.
Most of these have a free tier that's plenty for everyday questions. The paid plans mainly buy you higher limits and the work integrations, so start free and upgrade only if you hit a wall.
Best for: Writing, analysis, and anyone who cares about the ethics
Claude is made by Anthropic, the company that refused the Pentagon deal and got blacklisted for it. If you're leaving ChatGPT over values, this is the obvious choice. They even provide a hacky workaround for getting data/memory out of ChatGPT and into their platform.
Pros:
Strong writing and reasoning capabilities
Longer context window (can handle more text at once)
Company has clear ethical stance
Projects feature helps organize conversations by client or topic
Automatic memory across conversations (launched March 2026)
Integrations with Slack, Google Drive, Gmail, Figma, and Canva via Claude Cowork
Cons:
Work tool integrations require paid plans for full access
Not as deeply integrated into one ecosystem as Gemini with Google Workspace
Learning curve for setting up Projects and integrations
Best for: People already in the Google ecosystem
If you live in Gmail, Google Docs, and Google Drive, Gemini pulls context from those tools automatically.
Pros:
Integrates with Google Workspace
Can search your emails and docs for context
Deep links to source material
Cons:
Only works well if you're all-in on Google
Writing quality varies
Google's AI ethics track record is mixed
Best for: Research and finding current information
Perplexity is a research tool, not a chatbot. It searches the web, cites sources, and gives you actual answers instead of generic AI responses.
Pros:
Always current (searches real-time web)
Cites sources
Good for fact-checking and research
Cons:
Not designed for writing or creative work
No context from your files or work history
Can't create deliverables
Best for: Doing work using project context
If you were using ChatGPT to draft client deliverables, status reports, or project documentation, Supernormal takes a different approach than traditional chatbots. It automatically captures audio (not video) from your meetings, emails, and documents, then uses AI agents to generate project-specific deliverables — no more starting conversations from scratch every time.
Pros:
Automatically pulls context from meetings without copy-pasting transcripts
Pre-built agents for common deliverables (slides, sheets, docs)
Organizes work by project or client, not scattered conversations
Creates finished outputs, not draft text that needs heavy editing
Bot-free meeting capture (no \"Supernormal Bot has joined\" notifications)
Powered by Claude, so it handles general questions too
Cons:
Requires using the desktop app for meeting capture
Primarily designed for work deliverables with meeting context rather than standalone chat
Pick your primary replacement based on what you actually use AI for. If you mostly write, go with Claude. If you mostly research, try Perplexity. If you need meeting notes, use a specialized tool. You might end up using more than one, that's fine.
Once you've chosen, set it up properly. Go through your ChatGPT export (or manually saved conversations) and find the prompts you used most. Most alternatives have a \"custom instructions\" or \"system prompt\" feature where you can set defaults — add your tone preferences, output format, and any context you were repeatedly feeding into ChatGPT. If you're using Claude, create Projects for different clients or work areas. If you're using Gemini, connect it to your Google Workspace. Do the setup work now so you're not constantly re-explaining yourself.
Then delete ChatGPT. Go to your OpenAI account settings and delete your account entirely. Remove the bookmark. Uninstall the app. If you were paying for ChatGPT Plus or Teams, cancel the subscription. Don't leave it \"just in case\", you already exported what you needed. Make the break clean.
Here's what most people realize after leaving ChatGPT: the chatbot model is fundamentally broken for real work.
You don't want to copy-paste between tools. You don't want to re-explain context every time. You don't want to write prompts, you want to get work done.
The future isn't better chatbots. It's AI that integrates into your actual workflow:
Meeting notes that automatically capture decisions and action items
Project briefs that pull from your emails, docs, and past conversations
Status reports that write themselves from your actual work
Deliverables that match your style because they learn from what you've already created
That's why we built Supernormal. It's not a chatbot replacement, it's AI that works alongside you, learning from your meetings, emails, and documents to create client-ready deliverables without the copy-paste workflow.
If you're leaving ChatGPT and looking for something built specifically for agencies and consultants doing client work, take a look.
", "url": "https://www.supernormal.com/blog/breaking-up-with-chatgpt", "title": "Breaking up with ChatGPT: How to export your data and find better alternatives", "summary": "ChatGPT uninstalls jumped 295% after the Pentagon deal. Here's how to export your data, evaluate alternatives, and actually make the switch.", "date_modified": "2026-03-12T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "AI and agents" ] }, { "id": "urn:sha256:70fbf3fe45f0e0ea80460c82b652c050e06cb5fc7364e6b961294a5d01dafa07", "content_html": "How can you tell which AI tools will deliver value inside real, mission-critical workflows? If you are looking for real, practical advice on AI tools that streamline your team’s input and help you realize the value AI can offer, this guide will be useful.
It points to where AI delivers the most value in day-to-day operations. You’ll also see which tools deliver the kind of needle-moving efficiency you need, and get a four-question framework to help you pick the right tools for your business stack.
AI delivers the most value when it is applied to work that happens daily and quietly consumes time across teams. Instead of focusing on experimental use cases, the most effective AI tools reduce manual effort, improve clarity, and support coordination at scale.
The following areas represent where AI is delivering the most consistent and measurable value for B2B teams today.
Beyond communication and collaboration tools, AI is starting to handle complete work deliverables. For knowledge workers, this means tools that can draft follow-up emails, create presentations, synthesize research, or build spreadsheets without extensive prompting or manual context-sharing.
Tools like Lindy and Supernormal represent different approaches to this challenge. Lindy positions itself as a platform for building \"AI employees\", agents you can create and customize for specific workflows. Supernormal takes a different path by automatically capturing context from meetings, emails, and documents, then using that context to feed pre-built AI agents that generate deliverables already tailored to specific projects and stakeholders. The benefit is speed: work that typically requires hours of drafting can be completed in minutes of review and refinement.
Meeting context has emerged as a key differentiator for AI work tools. Rather than requiring users to manually copy transcripts or re-explain project background, some tools capture this information automatically. Supernormal's desktop app, for example, records meeting audio directly from your computer without joining as a participant. This bot-free approach eliminates distracting join notifications during calls. The captured context then informs the AI agents when generating deliverables, reducing the gap between what was discussed and what gets created.
Beyond basic meeting transcription, Supernormal transforms captured conversations into work outputs. Capturing via the Desktop App using system audio, rather than joining meetings as a bot, it captures audio without appearing as a participant. After a call, users can ask Supernormal to draft the follow-up email, create a pitch deck incorporating discussion points, or synthesize research findings — all without manually copying transcript snippets or re-explaining project background.
The workflow relies on persistent context. Unlike generic AI tools that start fresh with each request, Supernormal maintains awareness of past meetings, email threads, and documents across projects. This means asking for \"the Q2 proposal for Acme Corp\" generates output that already references the January kickoff meeting, the client's stated budget constraints, and their preferred timeline without prompting.
And to make taking action simpler, Supernormal Desktop App workflow integrates with a suite of integrations you already use:
Meet in Zoom, Google Meet, Digital Samba, Slack Huddles and Microsoft Teams
The Desktop App records audio directly from your computer across all video conferencing platforms. This bot-free capture means no join notifications during calls, so meetings stay focused and distraction-free while Supernormal captures everything in the background as context for its AI agents.
Collaborate and communicate in Slack and Gmail
Pull context from Slack conversations and email threads to inform AI-generated deliverables, then deliver outputs directly where teams already work. This keeps project context connected and reduces time spent copying information between tools.
Manage projects in Asana, ClickUp, Linear, Monday.com, and Trello
Turn meeting decisions and next steps into structured tasks inside project management tools. This allows teams to move from discussion to execution without manually recreating work after meetings.
Store docs and knowledge in Notion, Google Docs, and Google Sheets
Add meeting summaries and key insights directly into documentation tools. This helps teams preserve context, keep knowledge up to date, and avoid rewriting information after every meeting.
Manage customer conversations in HubSpot, Salesforce, and Pipedrive
Capture customer conversations and surface relevant notes and follow-ups inside CRM systems. This keeps records accurate and up to date without requiring manual data entry from sales or customer-facing teams.
Connect to databases and build workflows in Airtable
Structure meeting outputs so they can be stored, routed, or reused in databases and workflows. This allows teams to treat conversations as operational inputs that support reporting, automation, and internal processes.
Scaling with Supernormal means work gets done faster, teams spend time reviewing outputs instead of creating them from scratch, with meeting context automatically feeding smarter AI agents.
Ask almost any knowledge worker what they most want out of an AI assistant, and you’ll probably hear about the need for a simpler way to get work done. Between email and any project management, CRM, CMS, or other applications, keeping up and staying ahead is often something most people either struggle with or leave for a Saturday or Sunday.
Lindy was built to give back your time by filtering through your email, tasks, and a suite of integrated applications like Slack, Google Workspace, and HubSpot. It uses iMessage to send proactive texts about what’s on your agenda for the day, and you can give it instructions to complete tasks too.
Onboarding and training are critical to effective company growth, yet they often become time-consuming and inefficient as organizations expand. Instead of repeating the same sessions across teams and locations, businesses are increasingly using AI training videos to deliver consistent, engaging onboarding and upskilling content on demand. This allows HR and team leads to standardize knowledge transfer, personalize training by role or region, and reduce the ongoing time investment required to bring new hires up to speed. AI tools like D-ID provide this ability.
As teams grow, operational complexity increases. Processes that once lived in people’s heads or spreadsheets begin to break down. AI is helping teams automate the connective tissue between systems and reduce operational drag. That’s where tools like Zapier and Make have evolved. They already worked efficiently, and new AI capabilities make them seem far more capable than ever.
Zapier connects the tools your team already uses and automates the work that normally happens between them. Its AI helps trigger actions based on events, such as creating tasks, syncing data, or sending notifications, without requiring manual steps.
Zapier integrates with thousands of applications, allowing information to move automatically across systems. Updates in one tool can reliably trigger actions in another, keeping workflows consistent as volume increases.
Growing your team with Zapier provides operational consistency. Workflows continue running in the background, helping teams maintain speed without adding coordination or headcount.
Make automates more complex workflows that require logic, conditions, and multi-step processes. It allows teams to design flows that adapt based on inputs rather than following a single linear path.
Make integrates with business tools to route requests, transform data, and trigger actions across systems. Workflows can branch based on priority, status, or data conditions, allowing teams to handle complexity without human intervention at each step.
As your team scales, Make helps manage operational complexity without increasing oversight. Once workflows are in place, they run predictably, reducing exceptions and manual follow-ups as systems grow.
Most organizations generate large amounts of knowledge through meetings, documentation, and internal discussions. The challenge is not creation, but capture and reuse.
Notion AI helps teams turn everyday work into usable documentation. It generates summaries, drafts internal content, and refines existing documents so information stays current without constant manual effort.
Because Notion AI works inside documents, databases, and project spaces, teams can capture knowledge as work happens. Information remains connected and accessible without requiring duplicate effort.
Notion AI helps prevent knowledge decay at scale. As teams and projects multiply, information remains usable without relying on individual memory or repeated explanations.
Guru surfaces accurate information where teams are already working. Its AI delivers answers inside tools like browsers and Slack, removing the need to search through documents or interrupt colleagues.
Guru integrates with existing systems to pull verified information into daily workflows. When team members need guidance, answers appear in context, helping them act quickly and consistently.
Guru reduces friction caused by knowledge gaps in growing organizations. Teams spend less time searching and more time executing, supporting faster onboarding and alignment.
As teams scale, internal data becomes harder to track, interpret, and reuse across departments. AI-driven data discovery platforms like Secoda help organizations centralize analytics knowledge, document metrics, and surface trusted information without manual digging. This approach is increasingly used by growing video platforms — including Uscreen — to keep internal reporting and decision-making aligned as operational complexity increases.
As project volume increases, visibility often decreases. AI is helping teams maintain momentum by automating updates and surfacing risks earlier.
Asana Intelligence analyzes project activity to surface risks, dependencies, and workload issues. It generates summaries that help teams understand what is happening without manual reporting.
These insights appear directly inside Asana, allowing teams to act within existing project views. Teams gain earlier visibility into issues without relying on frequent status meetings.
At scale, Asana Intelligence supports proactive execution. Teams can adjust before timelines slip, reducing reactive coordination and last-minute pressure.
ClickUp AI automates the administrative work that accumulates as projects grow. It generates task descriptions, summarizes activity, and drafts updates based on what is already happening.
Because these features are embedded directly into ClickUp workflows, teams benefit without changing how they work. Project spaces stay organized without constant manual upkeep.
ClickUp AI helps maintain momentum as your business grows. Teams spend less time managing tools and more time delivering outcomes as work increases.
As businesses scale, founders, leaders, and go-to-market teams increasingly rely on personal brand visibility to build trust, credibility, and inbound demand. Maintaining that presence, however, often becomes inconsistent as priorities shift and workloads increase.
Supergrow applies AI to the everyday work of LinkedIn content creation and distribution. It helps professionals turn ideas, experiences, and insights into regular, high-quality posts without adding daily overhead. By supporting ideation, writing, carousel creation, scheduling, and engagement in one LinkedIn-first workspace, Supergrow reduces the manual effort typically required to stay visible on the platform.
Instead of treating social presence as a separate or reactive task, Supergrow fits into existing workflows. Teams and individuals can plan content ahead of time, schedule posts and follow-up comments, and engage consistently with their audience—without relying on risky automation or generic AI output. Over time, this creates a more predictable system for maintaining visibility, nurturing relationships, and supporting long-term brand growth on LinkedIn. As AI-driven search increasingly surfaces LinkedIn content, optimising posts for AI search engines becomes a way to drive additional inbound demand.
There is a world of AI tools available today, but selection matters more than adoption. The most effective AI tools for businesses share a few defining traits.
The best AI tools eliminate steps teams previously handled manually. Automatically capturing outcomes removes the need for note-taking, follow-up emails, and task recreation, leading to immediate and sustained time savings.
AI delivers the most value when it fits into tools teams already use. Delivering outputs directly into Slack, project management platforms, or documentation tools avoids forcing behavior change and lowers adoption friction.
Effective AI tools operate in the background. Automated summaries, task creation, and data syncing reduce the need for additional meetings, updates, or check-ins.
By clearly surfacing decisions, owners, and next steps, AI reduces ambiguity and helps teams move forward faster, especially as organizations grow.
While the list of common characteristics for choosing the right AI tools is clear enough, the other side of the spectrum can be far murkier, especially when teams are forced to make hasty decisions under pressure.
For example, if a tool requires significant behavior change or adds another layer of management, it is unlikely to deliver long-term time savings. Behavior change is difficult to implement at scale, and additional oversight increases workload for already resource-constrained teams.
The most compelling reason to invest in AI is not speed for its own sake. It is the ability to scale without introducing strain into the organization.
As your team grows, the challenge shifts from execution to coordination. Meetings increase, projects overlap, and knowledge fragments. The friction that slows teams down is less about effort and more about maintaining clarity, alignment, and momentum as complexity compounds.
The AI tools that matter absorb complexity. They turn conversations into usable outputs, automate the connections between systems, preserve context over time, and reduce the coordination overhead that typically comes with growth. AI can even help at the earliest stages of a project: platforms such as Ultahost use AI to suggest relevant domain names based on brand ideas, keywords, and market positioning, helping teams move seamlessly from concept to launch. Instead of pushing teams to work faster, they quietly extend what teams are already capable of.
This is where AI becomes a true scalability multiplier. When tools integrate into existing workflows, remove manual effort, and operate in the background, they allow organizations to grow output and execution without growing friction, overhead, or headcount. As AI adoption expands across teams, many businesses also need to rethink how they charge for these capabilities so pricing reflects real usage and value. Choosing the right AI pricing models helps companies scale responsibly without locking themselves into rigid cost structures.
", "url": "https://www.supernormal.com/blog/ai-for-businesses", "title": "Best AI Tools for Business 2026 - Practical Selection Guide", "summary": " Discover AI tools that remove work instead of creating it. From Supernormal to Zapier, see which tools help businesses scale without adding complexity.", "date_modified": "2026-03-12T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "AI and agents" ] }, { "id": "urn:sha256:7c5c250a0d9941d1f0ede8b90cfb45fbed52648ca3d487c539d0bcd11fd8b544", "content_html": "With so many meetings happening online, being able to rewatch or save your Zoom recordings has become essential whether it's a team sync, client call, or training session. But if you've ever received a shared recording link and wondered, Can I download this?, the answer depends on how it was recorded and shared.
Cloud recordings are easiest to download and share if the host enables viewer downloads.
When there's no download button, ask the host first, then consider a browser extension, developer tools, or screen recording as fallbacks.
Downloading a recording without permission can breach confidentiality, consent laws, or company policy, so request access from the host instead.
Local recordings can't be shared through Zoom links and must be uploaded to a third-party service.
If you run into issues, check permissions and confirm the file hasn't been deleted or moved.
Before downloading anything, check the recording's share settings.
If the host shared the recording with view-only permissions, you'll only be able to stream it and not download. To allow downloading, they need to enable the \"viewers can download\" option when sharing.
Always make sure the share link explicitly mentions download access before you try to save the file.
Many meetings are recorded to Zoom's cloud, which makes sharing and downloading simple, as long as the host allows it. Cloud recording is available only for paid Zoom users on Pro, Business, or Enterprise plans. When you choose Record to the Cloud, Zoom saves your meeting's video, audio, and chat in the cloud so you can download or stream it later.
Once the meeting has finished processing, you can access, share, or download the files directly from a shared link.
Click the shared link to open the recording in your browser.
Under the video player, click Download.
A dialog box will appear. Select Save to choose where to store the MP4 file.
Once downloaded, the file will appear in your chosen folder and can be played like any video.
You can also check for recordings shared with you in your Zoom admin space. If you are trying to save a meeting you hosted yourself, see our guide on how to download Zoom meeting videos.
Zoom's mobile experience doesn't offer direct downloads. If you open a shared cloud recording link, you can watch it in your browser, but saving it locally requires a workaround.
To save it to your device:
Open the link in your browser or Zoom app.
Tap the Share icon.
Select Save to Files or a cloud app like Drive, depending on your device.
If you don't see a download option, try switching to the desktop version of the page. In Chrome on Android, tap the menu, then Desktop site. In Safari on iPhone, tap the Share button, then Request Desktop Website. Reload the page and look for the Download option. If it's still missing, the host likely disabled it.
💡 Tip: Cloud recordings can only be created by the meeting host or co-host, not participants. If you're on a free plan, you'll need to use computer recording instead, which saves the meeting to your local device.
Sometimes you open a shared link and see only a play button, with no way to save the file. That means the host shared the recording with view-only permissions. Here are your options, from the cleanest fix to the last resort: ask the host to enable downloads, use a browser extension, try your browser's developer tools, or screen record the playback.
The simplest fix is to ask the host to turn on downloads for that recording. From their cloud recordings, the host selects Share, toggles Viewers can download, then selects Done. On some accounts an admin first needs to enable cloud recording downloads in Account settings. Once it's on, your download button will appear.
Browser extensions such as ZED (Zoom Easy Downloader) detect the video on the page and add a download button back, then save the recording as an MP4. They can help when the button is missing, though reliability varies and some stop working without notice. Only use one on recordings you have permission to save.
If you'd rather not install anything, you can find the video file with your browser's built-in tools. Open the recording, press F12 (or right-click and choose Inspect), and open the Network tab. Reload the page, press play, then type mp4 in the filter box. Right-click the matching entry and open it in a new tab to view or save the file.
If nothing else works and you have permission to keep the recording, you can screen record the playback. On Mac, press Shift + Command + 5. On Windows, press Windows + G to open the Game Bar. On iPhone, add Screen Recording to your Control Center, then start it before you press play. The quality will be lower than a true download, so treat this as a fallback. For more options, see our guide on how to record a meeting.
If the host disabled downloads, that was a deliberate choice, and the best and safest path is to ask them to turn downloads on or to send you the file directly. Workarounds exist, but they come with real cautions worth knowing:
The recording may contain confidential client or company information the host did not intend anyone to save.
Recording and saving meetings without consent can break the law in some regions. Many places require one-party consent, and others require everyone on the call to agree, so check the rules where you and the other participants are based.
Saving content the host locked may also go against Zoom's terms or your own company policy.
If you have a legitimate reason to keep a copy, the professional route is to request access from the host. If you just need what was said, you could also transcribe the meeting instead of saving the video. It's faster than it sounds, and it keeps you on the right side of both the rules and the relationship.
Local recordings are stored directly on the host's computer, not in Zoom's cloud. That means they can't be shared through a Zoom link or uploaded back to the Zoom cloud. To share them, the host needs to upload the MP4 file to a third-party service. It's frustrating, but it's the only way to share a locally recorded meeting.
Find the file on your computer
Open the Zoom desktop app.
Click the Meetings tab, then open the Recorded tab to see your recorded meetings.
Select the meeting, then click Open to open the folder that holds the MP4 file.
By default, Zoom saves local recordings here:
Windows: C:\\Users\\[Username]\\Documents\\Zoom
Mac: /Users/[Username]/Documents/Zoom
Linux: home/[Username]/Documents/Zoom
Upload the MP4 to a cloud service
Use a service like Google Drive, Dropbox, or Vimeo.
Set the viewing permissions and copy the shareable link.
Share the link
Send the third-party link by email or Slack. Recipients can stream or download the recording directly.
Local recordings can't be downloaded through a Zoom link. Ask the host to upload the file and share it from a third-party service.
Local recordings give you control over your files, but downloading, uploading, and sharing them manually is time-consuming. If you mostly need what was said and the follow-ups, consider a tool like Supernormal, which captures your meetings without a bot and turns them into ready-to-send deliverables, so there's no video file to download, upload, or share.
Sometimes downloading doesn't go as planned. Here's how to fix the most common problems:
1. I don't see a download button
The host hasn't enabled it. Ask them to turn on \"viewers can download\", or see the options above for what to do when there's no download button.
2. The link says \"page not found\" or \"expired\"
This usually means the host deleted the recording or changed the sharing permissions. It can also happen if the host set an expiry date on the link. Contact them and request a new share link, or ask them to remove the expiry date.
3. The video won't play after downloading
Your download may have been interrupted. Try downloading again or use a different browser. If the issue persists, check your internet connection or try playing the file with another media player.
4. I can't find a local recording on my computer
Zoom saves local recordings in a default folder unless you've changed it.
Windows: C:\\Users\\[Username]\\Documents\\Zoom
Mac: /Users/[Username]/Documents/Zoom
Linux: home/[Username]/Documents/Zoom
If it's missing, search for .mp4 files or check your Trash or Recycle Bin.
5. I only have a local recording, not a link
Local recordings aren't shareable through Zoom links. You'll need to upload the MP4 file to a service like Google Drive, Dropbox, or Vimeo, then share that link instead.
Managing Zoom recordings, downloading files, uploading to cloud storage, and sharing links takes time away from client work and project deliverables. Supernormal's AI agents help agencies and consultants turn their meetings into finished client work. From client session summaries to project documentation and follow-up materials, Supernormal uses context from your meetings to create outputs you can review and send. If you are comparing options, see our roundup of the best notetakers for Zoom. Get started with the Supernormal desktop app, a bot-free notetaker and AI meeting assistant.
", "url": "https://www.supernormal.com/blog/how-to-download-a-zoom-recording-from-a-shared-link", "title": "How to download a Zoom recording from a shared link", "summary": "Download Zoom recordings from shared links on desktop or mobile. Step-by-step instructions for cloud recordings, local files, and MP4 downloads.", "date_modified": "2026-02-24T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "Meetings" ] }, { "id": "urn:sha256:aa1e2c8c58771de71948832072f01d1c9a861b0ce410eaee907bf7695c4495d4", "content_html": "Meetings generate a huge amount of information. Decisions get made, priorities shift, risks surface, and intent becomes clearer. Yet for many teams, that information never translates into action.
There's an ongoing debate about whether people are in too many meetings, but the problem isn't simply the volume of meetings. It's what happens around them. Preparation is rushed, context is fragmented, and follow-ups are often delayed or inconsistent. The cost isn't just time spent talking. It's the momentum that gets lost afterward.
Just like AI has revolutionized coding and copywriting, AI is going to revolutionize our meeting workflow too. Not by replacing meetings or simply transcribing them, but by acting as a continuity layer across conversations. AI makes it possible to surface the right context before a meeting, capture what actually matters during it, and turn conversations into usable outputs afterward. \"Meetings are no longer isolated events. They become crucial inputs for ongoing work.
What follows explores how AI enables that shift. Not which tools to buy, and not how many meetings teams should have, but what becomes possible once AI is applied across the full meeting lifecycle.
It's easy to think of meetings as moments in time. A block on the calendar. A call that starts and ends. Something you attend, then move on from.
In practice, meetings function more like information systems. They generate decisions, clarify intent, surface risks, and reveal how priorities are changing. Much of the most important work information doesn't appear first in documents or task trackers. It appears in conversation.
The problem is that most organizations still treat meetings as disposable. Once the call ends, the information created inside it is easily lost. It lives in partial notes, personal memory, or long transcripts that are rarely revisited. Context resets from one conversation to the next, even when the topics are deeply connected.
This is where AI creates a meaningful shift. When meetings are turned into data, the information they produce doesn't disappear when the call ends. Context can carry forward. Decisions can be referenced. Insights can build over time instead of resetting from one meeting to the next.
As Colin Treseler puts it: \"The AI tools you use are constantly keeping an update of your thoughts, your beliefs on certain topics, your relationships. These are all important parts of an assistant that doesn't just enable you—it enables the people around you.\"
Most teams using AI around meetings are already doing something. They're recording calls. They're generating transcripts. Some are even producing summaries or action items. On paper, that sounds like progress.
In practice, it really isn't. Take a common meeting setup:
What happens
A team records a client call using an AI notetaker
A transcript is generated
A summary is shared in a document or Slack channel
The meeting ends and everyone moves on
What still breaks
The transcript isn't revisited
The summary isn't connected to previous conversations
Follow-up work still relies on manually translating what was said into emails, tasks, or plans
This isn't a tooling problem so much as a systems problem. Most AI meeting tools are designed to capture a single conversation well, but they don't help teams carry meaning across conversations. Each meeting is treated as its own artifact, with little connection to what came before.
![\"Blue](\"https://framerusercontent.com/images/Iaz8MHjpwxjEVhmbYPvWMSlZo.webp\")
That gap shows up clearly in how people actually work. People skim summaries for highlights, meanwhile, decisions and nuances resurface in later meetings because there's no shared, evolving understanding of what's already been agreed.
This is why transcription alone isn't enough. A transcript records everything, but it doesn't explain what matters. AI's real value is in interpretation. It can identify decisions, highlight risks, track open questions, and connect related conversations over time. It turns raw conversation into structured understanding.
Most AI meeting tools optimise for capture
Records a single meeting well
Produces transcripts and summaries
Treats each meeting as a standalone event
AI applied as a system creates continuity
Connects related meetings over time
Carries decisions and open questions forward
Surfaces relevant context automatically
Turns conversations into inputs for ongoing work
When AI is applied only at the level of capture, meetings still behave like isolated events. When it's applied at the level of interpretation and connection, meetings start to function as part of a larger system. That's the shift that changes how preparation, follow-up, and execution actually work.
This shift isn't unique to meetings. In software development, a recent MIT Technology Review piece has described a move away from one-off AI interactions toward what's called \"context engineering,\" where systems are designed around the information they already have rather than the prompts they receive. That same lens helps explain why meetings matter so much in knowledge work.
💡 The Meeting Context Flywheel defined
The Meeting Context Flywheel describes how meetings improve over time when AI captures and carries forward work context across preparation, conversation, and follow-up. Each meeting feeds the next by preserving decisions, open questions, and intent, giving AI the context it needs to support increasingly specific and meaningful work.
Large language models become more capable as they're given richer, more recent, and more relevant information. The same is true for AI at work. For AI to move beyond generic tasks and support highly specific work, it needs context. And the most valuable work context rarely lives in documents or task lists. It lives in meetings.
Meetings are where intent is clarified, decisions are negotiated, and priorities change. That makes them the richest and most fragile source of work context most teams have.
![\"Diagram](\"https://framerusercontent.com/images/WcbmshR4QBCW8oNGRY8ar3kFCdo.png\")
This pattern isn't unique to meetings. Even though 64% of teams rate AI's productivity impact as high, research suggests its benefits grow as usage matures. That mirrors how meeting context compounds over time, rather than delivering value in a single interaction.
The key shift with the flywheel system is that context doesn't reset after each meeting. It accumulates and becomes more useful over time. Each part of the flywheel strengthens the next.
When AI is applied across meetings, preparation, conversation, and follow-up all change in distinct ways. What's key is how these stages connect, and how little friction it takes to move from one to the next.
For many teams, meeting prep still depends on memory, scattered notes, or a last-minute skim of the calendar invite. When meetings are frequent, ad hoc, or involve people outside the organization, that approach isn't enough. By the time the call starts, context is uneven and momentum is already fragile.
Meeting AI changes this by making orientation fast and cumulative. Instead of relying on memory, teams can quickly see the decisions and open questions that matter for this call. The context for the meeting is already there, without requiring someone to hunt for it.
This also shifts what good preparation looks like. Rather than reading everything, the most valuable prep becomes clarifying direction. Why does this meeting matters now? What needs to move forward? What outcome would make the conversation successful? When AI brings the surrounding context into view, those questions are easier to answer in minutes, not hours.
During a meeting, teams are often juggling competing demands. Keep the conversation moving. Make decisions. Stay aligned. Capture what matters for later. Under pressure, something usually gives.
Traditionally, that tradeoff shows up in note taking. Either someone tries to write everything down and falls behind, or notes get simplified to the point where nuance is lost. The meeting feels clear in the moment, then fuzzy afterward.
AI changes this dynamic by taking pressure off capture and putting focus back on the conversation. Its role during the call isn't to direct discussion or interrupt the flow. It's to quietly identify the moments that shape what happens next.
Decisions, commitments, risks, and unresolved questions often surface naturally in conversation, but they're easy to miss or misinterpret in real time. When those signals are preserved as they emerge, teams don't have to reconstruct meaning later. What mattered is already clear.
The point where most meetings fail isn't during the conversation. It's after the call ends.
Without support, follow-up relies on manual effort. Someone rewrites notes. Someone sends an update. Someone creates tasks. That work often happens late, inconsistently, or not at all, which is why momentum slips so easily between meetings.
AI changes this by reducing the gap between conversation and action. Instead of starting follow-up from scratch, teams can turn what was discussed into structured outputs immediately. Summaries, drafts, updates, and next steps become starting points for work, not extra admin layered on top.
When this stage works well, the output of one meeting directly shapes the next.
Most AI meeting tools work the first few times people try them. The difference between systems that stick and systems that quietly disappear isn't capability. It's fit.
The systems that last share a few common traits. They adapt to existing behavior rather than trying to correct it. That distinction matters more than most feature comparisons.
One of the fastest ways to weaken a meeting is to make people self-conscious. When participants feel observed or managed, the conversation shifts. People explain more than they need to. They hold back uncertainty. They default to safer language instead of honest discussion.
This shows up clearly with some recording-first tools. When a visible bot joins a client call, people often adjust how they speak. They recap things that were already agreed. They avoid open disagreement.
This tradeoff between convenience and conversation quality is why some teams actively avoid meeting bots altogether and look for approaches that don't require anything to join the call. You've likely seen this play out in practice, especially in client-facing or high-stakes meetings.
Durable systems avoid this by fading into the background. They capture what matters without interrupting the flow or requiring people to change how they run meetings. The meeting stays human, which is especially important in client-facing, sales, or high-stakes conversations where trust and tone matter as much as content.
Capturing everything isn't the same as capturing what matters. Many teams using tools like Otter or Fireflies have experienced this firsthand. A full transcript exists, but no one reads it. A long summary gets skimmed, then forgotten.
Even well-designed notetaker tools like Granola can fall into this trap if the output isn't structured around decisions, commitments, risks, and open questions.
Systems that last focus on signal. They highlight the moments that actually shape what happens next, instead of producing exhaustive records. This is why many teams still rely on simple one-page notes, decision logs, or short recaps, even when they have advanced tooling available. Signal beats completeness every time.
A common failure mode across many AI meeting tools is shifting effort rather than removing it. Notes still need to be rewritten into emails. Action items still need to be turned into tasks. Updates still need to be drafted for clients or stakeholders.
Some teams use Fireflies or Otter to capture meetings, then manually translate the output into Slack updates, project plans, or follow-up emails. The AI helped record the meeting, but the work of turning conversation into action still sits with the human.
Durable systems shorten that distance. Teams use AI to draft a client recap immediately after a call, turn meeting discussion into a first pass at a project plan, or generate a follow-up email that's ready to send. Human effort goes into refinement, not translation.
When that gap closes, meetings stop creating hidden admin work later in the day.
Adoption often fails not because a tool isn't powerful, but because it lives in the wrong place. If meeting output requires people to log into yet another workspace, it quickly gets ignored.
Teams export notes into Notion or Google Docs. Others move summaries into Slack, Linear, Jira, or email. The more steps that sit between the meeting and where work actually happens, the more likely context gets lost.
Durable AI meeting workspaces minimize those steps. Insights flow directly into the tools teams already rely on. Email drafts, documents, task trackers, and chat threads. Low friction beats perfect structure every time.
Finally, systems that last improve over time. As more meetings pass through them, they develop a better understanding of recurring topics, decision patterns, and where work tends to stall.
But there's a deeper shift happening too. Your team's most valuable knowledge doesn't live in documents or wikis. It lives in the conversations where strategy is debated, client needs are understood, and hard calls get made. When that context is captured and carried forward, it becomes an enduring asset that AI can reference and build on. Over months and years, this accumulated understanding turns into something competitors simply can't replicate. Their AI tools start from zero. Yours starts from everything your team has learned together.
Supernormal Notetaker follows this approach, focusing on context that compounds across preparation, conversation, and follow-up, so each meeting feeds the next.
When meetings are treated as a source of durable context rather than isolated events, a few things start to change.
Less time is spent re-explaining decisions
Teams don't have to revisit the same background or relitigate choices across multiple calls. What was decided stays visible.
AI becomes more useful over time
As context accumulates, AI can support more specific work. Drafts get closer to usable. Follow-ups get clearer. Suggestions reflect how the team actually operates.
Preparation feels lighter, not heavier
People spend less time hunting for information and more time deciding direction. A few minutes of orientation replaces pages of catch-up.
Follow-up work shrinks
Conversations turn into drafts, updates, and next steps without as much manual translation. Meetings stop creating hidden admin later in the day.
Meetings start to feel connected
Each call builds on the last. Progress carries forward instead of resetting. The system supports momentum instead of relying on memory.
\"I want my athletes to feel like anything they tell me, I remember and follow up with them on. Even small details about their lives.\" — Sara, Performance Coach
That's what it looks like when meeting context is allowed to compound.
", "url": "https://www.supernormal.com/blog/ai-and-meetings", "title": "AI and meetings | the meeting context flywheel", "summary": "Meetings generate your most valuable work context. Learn how AI captures, carries forward, and compounds that context across every meeting stage.", "date_modified": "2026-01-09T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "AI and agents" ] }, { "id": "urn:sha256:b2117b5bcc45773982c190baeca39139e019603294ca814b30f783d2be19a1e2", "content_html": "Being a \"manager\" is definitely not cool any more. The post-COVID layoffs at most major tech companies focused on removing layers of management. As Mark Zuckerberg, already a champion of senior individual-contributor career progression, put it in 2024:
\"I don't think you want a management structure that's just managers managing managers, managing managers, managing managers, managing the people who are doing the work.\"
And now that AI is workplace-ready, that's never been more true. Very small organizations are achieving what used to take teams of hundreds.
The most competent professionals I've met in my career are ambitious to be leaders, not managers, and almost everyone I know is suspicious of overly hierarchical organizations. All this has compounded to make being a manager something fewer people aspire to. But I think the skills that make someone a great manager have never been more valuable.
Because what actually IS management? Joe Fuller, a professor at Harvard Business School defines it as:
\"Getting the confused, misguided, unmotivated, and misdirected to accomplish a common purpose on a regular, recurring basis.\"
Apart from maybe the unmotivated bit, didn't he just explain what it takes to be good at deploying agents? Human management is about giving people the context, instructions, feedback, and frameworks to get great work done. Deploying an agent involves the exact same skills.
If you are a good manager, you will be good at using agents. If you think you are a good manager, but you are struggling to deploy agents, you may not be as good a manager as you thought. Take a look at that list. Are your agents confused, misdirected, or misguided? Maybe you aren't giving them the context, instructions, feedback, and frameworks they need.
", "url": "https://www.supernormal.com/blog/management-in-the-ai-era", "title": "Why management skills are valuable when working with AI", "summary": "Management skills matter more in the AI era, not less. Learn why traditional management expertise is crucial for deploying AI agents effectively.", "date_modified": "2026-01-07T00:00:00.000Z", "author": { "name": "colin-treseler" }, "tags": [ "AI and agents" ] }, { "id": "urn:sha256:9343cf8fb4e235982d3ac549d1d12962e84eb5ec103137a0bf69e711939bac29", "content_html": "This December, we tried something new. To celebrate the launch of the Supernormal app's shortcut feature, we showcased the shortcuts our early users already rely on across 12 working days. The 12 Days of Shortcuts series took off fast. Each day revealed a new way people use Supernormal to turn meetings into meaningful work in seconds.
Some shortcuts were practical workhorses. Some solved pain points every agency, consultant, or creator knows well. And a few were posted simply because they made us smile. Now that the series has wrapped, this roundup brings everything together so you can catch up, steal your favorites, and get ideas for your own workflows.
Twelve weekdays. Twelve shortcuts. A whole lot of \"wait, I can really do that in Supernormal?\"
Here's the full lineup.
![\"Blue](\"https://framerusercontent.com/images/kfkbys7eaZP2jwvuzGk2xJ0BUA.webp\")
Sean Nolan at Mango Media shared /creative-ideas, his shortcut for turning early client conversations into solid creative starting points. The shortcut pulls out the client's expectations, constraints and project goals, and generates a list of ideas that are both ambitious and feasible within scope. It keeps the creative process grounded while still giving Sean the momentum he needs to move straight into concept development.
Here's what it delivers:
A curated list of viable creative ideas based on real client input
Concepts aligned tightly with scope, goals and expectations
A faster path from conversation to first-round creative
A practical antidote to the blank page
![\"\"](\"https://framerusercontent.com/images/zBxDkTjUxEwBgHeQcEhDrGnMc.webp\")
Austin Rehorn created /summarize to produce structured, reliable meeting summaries without any manual shaping. Supernormal analyzes the full meeting transcript and breaks it into the sections teams actually need: objectives, key discussion points, decisions, action items with owners and deadlines, unresolved questions, next steps and notable ideas.
It even surfaces issues or concerns raised during the call. The result is a clean, comprehensive summary that's instantly useful for busy teams juggling multiple projects.
Here’s what it delivers:
A clear breakdown of meeting objectives and agenda.
Key topics and decisions captured with context.
Action items assigned to owners with suggested deadlines.
Unresolved questions and next steps neatly surfaced.
Notable ideas worth exploring further.
Issues or blockers flagged for follow-up.
![\"\"](\"https://framerusercontent.com/images/0M2APWDjZRcu0pWtm8kgzhbrW8M.webp\")
Sara Reichenbach works with athletes pushing for elite performance, and she knows inboxes aren’t where accountability lives. That’s why she uses /coaching-text. The shortcut drafts a concise text message that captures the key points from the session, highlights action items for both coach and client, and mirrors the tone of the call. It delivers a quick reminder the client can act on immediately, helping them integrate insights into their day-to-day training and mindset.
Here’s what it delivers:
A short, personalised text that reinforces the session’s core ideas.
Clear action items for both coach and client.
Tone of voice matched to the conversation’s energy.
A fast, effective follow-up that strengthens commitment and progress.
![\"\"](\"https://framerusercontent.com/images/GWaidgwGmknq2n6oZuVDwpbfRs.webp\")
After coaching and sales calls, Brad Hussey uses /follow-up-email to turn a productive meeting into a crisp, forward-driving message in seconds. This shortcut focuses on outcomes, commitments and next steps, not a recap. Supernormal adapts the tone and structure based on the meeting type—sales, internal, customer success or product—and immediately surfaces what was decided, who owns what and the deadlines attached.
It keeps momentum high and ensures that nothing slips through the cracks after an important conversation.
Here’s what it delivers:
A focused follow-up email anchored in decisions and actions.
Action items assigned to clear owners with suggested deadlines.
Key commitments and open questions captured without the fluff.
Formatting tailored to the meeting type for maximum clarity and impact.
![\"\"](\"https://framerusercontent.com/images/gg8aUSYZ5O0ldmDisnZYtqXIs.webp\")
Recruiters loved this one. Thomas Heywood at WeEngage Group built /salarydetails to extract every detail a candidate shares about compensation and notice period without digging through transcripts manually. Supernormal pulls out salary expectations, preferred ranges, non-negotiables, bonus structure, benefits mentions and notice period nuances.
If the candidate referenced exceptions, constraints or flexibility, those get surfaced too. It’s a clean, recruiter-ready snapshot that saves time and reduces the risk of missing details that matter later.
Here’s what it delivers:
A clear statement of the candidate’s salary expectations and preferred structure.
Notice period details, including special conditions or flexibility.
Granular insights into compensation signals mentioned during the call.
A recruiter-friendly summary that cuts out transcript hunting entirely.
![\"\"](\"https://framerusercontent.com/images/eWEDFbyB1ervIyTSc2nKIvUFiJg.png\")
Arcjet founder Zevy Blockh designed /proposal because proposal writing was holding him back from the creative work he actually cares about. This shortcut listens to the meeting, pulls out the client’s goals, constraints and priorities, and drafts a clean proposal with two pricing tiers. If the transcript mentions a budget, Supernormal weaves it in. And because the proposal speaks directly to the client in second person, the result feels personal, relevant and ready to refine.
Here’s what it delivers:
A proposal draft tailored to the client’s needs and language.
Two pricing tiers based on scope and constraints.
Budget alignment when numbers appear in the conversation.
A structure that moves from goals to approach to investment smoothly.
![\"\"](\"https://framerusercontent.com/images/0kFru3hd1PVLHRwdna6SLDTUs.png\")
Joe Casabona, presenter of the Streamlined Solopreneur podcast, uses /show-notes to turn his raw recordings into clean, publish-ready episode notes without the usual editing grind. The shortcut listens to the conversation and generates everything a podcaster needs: a tight summary, timestamped chapters, editing cues, and a polished episode description.
It even pulls out standout quotes worth highlighting. For creators working solo or producing on a tight schedule, it’s a serious workflow upgrade.
Here’s what it delivers:
A clear, concise summary of the episode’s core message.
Timestamped chapters with meaningful titles for easy navigation.
Editing notes that flag moments needing cleanup.
A draft description that fits podcast platform expectations.
2–3 memorable quotes to highlight in marketing or show notes.
![\"\"](\"https://framerusercontent.com/images/WhOT8OsxtYJYl07JrgQBBoM59E.webp\")
PMPlace created /governance-gatekeeper as the ultimate shortcut for project managers who need clarity fast. It takes a meeting transcript and turns it into a complete Governance Gatekeeper Report, structured with the kind of detail normally produced after hours of PM work.
Supernormal identifies decisions, action items, risks, dependencies and stakeholder communication needs, then assembles everything into a polished report the PM can use immediately. It’s comprehensive, reliable and removes the manual lift from one of the most time-consuming parts of project delivery.
Here’s what it delivers:
An executive summary that captures purpose, outcomes and the key things the PM should watch.
A decision log table with IDs, rationale and scoped impact across timeline, budget and risk.
Detailed action items with owners, priorities, context and suggested due dates.
A risks, issues and dependencies section with clear severity and mitigation.
A ready-to-send stakeholder email with decisions, actions and escalations already formatted.
A tailored “Next meeting optimization checklist” to help the team run sharper sessions next time.
![\"\"](\"https://framerusercontent.com/images/oGBBhXCMS9bhrfzHk3coqE4tDjw.webp\")
Calie G at Numero Accounting uses /customer-problems to turn raw client conversations into clear, structured problem briefs her team can act on quickly. Instead of capturing feature requests or scattered commentary, the shortcut reframes the discussion around actual user needs, pain points and desired outcomes.
Supernormal generates problem statements in a consistent “As a user…” format, defines the scope, highlights affected user segments and outlines the business impact. It gives teams a crisp starting point for strategic decisions without getting stuck on surface-level fixes.
Here’s what it delivers:
Problem statements that reflect the real user need, not the suggested solution.
Clarity on scope, users affected and business impact.
Success criteria to guide prioritisation and design decisions.
A structured brief that product and service teams can act on immediately.
![\"\"](\"https://framerusercontent.com/images/tWHcGXJuzmNplVxWyM8DqeP4.webp\")
Dass Atma Singh at SikhNet relies on /action-items to keep his work moving. This shortcut extracts every task from a meeting transcript and turns it into a clear, prioritised checklist the team can use immediately. It identifies the task, who owns it, any timelines mentioned and any dependencies.
Supernormal then formats everything in a clean, shareable list with the responsible person bolded at the end of each line, so nothing gets missed and everyone knows exactly what they’re accountable for.
Here’s what it delivers:
A full list of action items pulled directly from the meeting.
Ownership, deadlines and prerequisites clearly identified.
A checklist prioritised for fast execution.
Formatting that makes it instantly usable across teams.
![\"\"](\"https://framerusercontent.com/images/3Obv7FSxVFGuBgOsMcEyJd75d8.webp\")
This shortcut started as a happy accident. Kevin Fernando realised he could use the Supernormal desktop app to capture the audio from any video, even when no transcript exists, and instantly turn it into something far more useful. By playing the video and capturing it in the app, /video_summarizer transforms the content into a structured, easy to use breakdown.
Here’s what it gives you every time:
Summary. A clear explanation of what the video covers and the main ideas.
Key takeaways. The most important insights worth remembering.
Action steps. The actual steps or instructions mentioned, formatted so you can follow them immediately.
Resources mentioned. Any tools, links or references the speaker calls out.
Additional notes. Helpful context that brings the whole thing together.
It’s perfect for tutorials, walkthroughs, interviews or any long video where you’re thinking, “just tell me what to do.”
![\"\"](\"https://framerusercontent.com/images/NEfDDLyjCrGFtqMyQvendrDEVYg.webp\")
We wrapped the series with something a little different. Audrey Bai is a software consultant who uses AI to work smarter, not just faster. She created /praise as a way to surface every moment of positive feedback she’s received across conversations. Supernormal searches through meetings and pulls out the times people have praised her work, her thinking, or her impact. The result is a living self-reflection document she didn’t have to write herself. It’s useful, grounding, and quietly powerful. Whether you’re prepping for a review, updating a portfolio, or just need a reminder that your work matters, this shortcut turns everyday conversations into proof of progress.
Here’s what it delivers:
A collection of praise pulled from real conversations.
Clear examples of impact, strengths, and recognition.
A ready-made reflection document with zero manual effort.
A thoughtful way to close the loop on your work.
That's the full set from our first 12 Days of Shortcuts. Seeing how quickly people personalized Supernormal has been one of the clearest signs of that the product is growing in popularity rapidly while still in beta. Shortcuts aren't about doing more. They're about removing the drag of repetitive tasks so you can work the way you want.
Build your own today, adapt anything you saw here, or send us your favourites. The best ideas usually start with someone saying, \"I wish this were easier.\" With Supernormal, it can be.
", "url": "https://www.supernormal.com/blog/12-days-shortcuts", "title": "12 days of shortcuts: Real workflows you'll want to steal", "summary": "Explore 12 powerful Supernormal app shortcuts that automate real workflows. See how quickly you can get work done and steal these proven time-savers.", "date_modified": "2025-12-17T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "AI and agents" ] }, { "id": "urn:sha256:df6d409fa7c286623fefcac436f2068ba78381075e20ff09598795f050ddc61f", "content_html": "Personal knowledge management promises clarity in a noisy digital world. The idea is simple: capture what matters, connect it meaningfully, and make it easy to recall later. In practice, though, PKM can quickly become another project to manage. Endless dashboards, color-coded tags, and “second brains” fill feeds and promise productivity, but they often do the opposite.
Building a PKM system can feel productive, even when it isn’t. Designing templates, tweaking tags, and reorganizing notes can create the illusion of progress while leaving your actual work untouched.
The goal of PKM isn’t to perfect the system itself. It’s to build something light, flexible, and genuinely supportive — a way to think more clearly in the middle of everyday chaos.
Personal knowledge management (PKM) is the process of collecting, organizing, and using information so it becomes usable information. Unlike traditional note-taking, PKM focuses on connecting ideas over time, turning scattered thoughts into patterns and insights you can act on.
Whether you’re managing projects, studying, or just trying to keep ideas straight, PKM helps turn scattered information into reliable knowledge you can use.
Research shows why this matters. In one large review, about one in five people identified information overload as a major source of stress at work. Another study found that actively using and applying what you capture, rather than simply storing it, has a measurable impact on productivity.
For anyone who juggles ideas or finds focus difficult, PKM can bring calm and structure to complex work. It’s also been shown to be especially helpful for people who are neurodivergent.
Here’s how to create a PKM system that fits the way you think, without overcomplicating it.
The first mistake most people make is starting with tools instead of purpose. Before opening Notion or Obsidian, ask what you actually want your PKM to do.
Do you want to track ideas over time? Remember insights from meetings? Keep projects organized? Help you turn thoughts into action?
If you can’t describe the purpose of your PKM in one sentence, you’re not ready to build it yet.
A PKM system should serve a goal, not become one. It exists to make the path from thought to action smoother. Whether you use it to manage knowledge at work or to make sense of your own thinking, its value lies in how easily you can use it when you need it most.
Try this: Write one sentence that completes the phrase “My PKM helps me ___.” Keep it at the top of your workspace.
Example: “My PKM helps me turn client feedback from meetings into project updates.” or “My PKM helps me connect research notes to my writing ideas.”
The best PKM systems often begin as chaos. The goal is not to create order instantly but to start capturing what matters.
Start with one inbox for ideas. That could be a notes app, a physical notebook, a voice memo, or a folder on your desktop. The point is to collect ideas while they’re still fresh instead of waiting for the perfect structure.
If many of your ideas come from meetings or discussions, a tool like Supernormal can make that process seamless. The Desktop App captures the conversation automatically, summarizes what was said, and drafts the next steps for you, whether that’s a follow-up email, project update, or document outline. From there, you can review, refine, and move the draft directly into other tools like Notion or Google Docs. Supernormal turns the flow from discussion to finished work into one smooth step, so capturing doesn’t interrupt your focus.
Most people give up on PKM because they confuse capturing with organizing. The act of collecting ideas should be frictionless. Sorting and structuring can come later, when you have time and context.
Try this: Create one note or file called “Thoughts today.” Add three ideas before the end of the day. No structure, just capture.
Example: Jot down a quote you liked, a half-formed idea from a meeting, or something you want to research later. Don’t worry about where it fits yet.
Once you have enough material to work with, add gentle structure. Many people overbuild their PKM systems by trying to anticipate every possible scenario. Instead, start with three simple categories:
Projects: work or goals in progress
Ideas: insights, references, or research
Resources: material you may need later
That’s enough to begin. If your PKM only works on your best day, it’s too complicated. If you already use Notion, Apple Notes, or Logseq, set up a lightweight version of this structure there.
Frameworks like Tiago Forte’s PARA method (Projects, Areas, Resources, Archives) can help, but only if they make your setup simpler. The right structure is the one you can use when you are tired, distracted, or busy.
Try this: Make three folders or tags: Projects, Ideas, Resources. Stop there until you’ve used them for a week.
Example: Move your ongoing tasks into Projects, put saved articles in Resources, and keep all creative or work-related thoughts in Ideas. Notice which folder fills fastest before adding anything new.
No matter what tools you use, every PKM system comes down to three actions:
Capture: Gather ideas quickly in one place from meetings, things you’ve read, or spontaneous thoughts.
Organize: Sort them by purpose or project once you have context.
Review: Revisit regularly to refine insights and act on what matters most.
This loop of capture, organize, review is what turns information into knowledge, and it’s often more valuable than any specific app or structure.
There is no single “best” PKM tool. The right one depends on how your brain works and how much structure you like.
Tool | Best for | Why people like it | Watch out for |
Beginners or minimalists | Built in, fast, and impossible to overthink. Many users on Reddit’s r/PKMS say switching back to Apple Notes helped them focus on thinking, not system-building. Features like document scanning, note linking, and encrypted sync make it more capable than most realize. | Limited formatting and weak search make it harder to scale once your notes grow large. | |
Visual thinkers | Highly flexible with AI-powered search that feels natural. Ideal for people who like structure and integrated databases. Mobile apps are faster and smoother than early versions. | Complex interface and long-term data lock-in. | |
Deep thinkers and writers | Free, local, and endlessly customizable. Excellent for connecting ideas with plugins like Dataview and Canvas. Perfect for researchers or those who enjoy visualizing knowledge networks. | Steeper learning curve, requires setup and maintenance. | |
Daily journaling and task-oriented users | Simple out of the box, with great local file storage and easy task tracking. Fans praise its clean interface and “just works” setup that avoids plugin rabbit holes. | Mobile sync and whiteboard features are still improving. | |
Meeting-driven professionals | Captures discussions, summarizes key points, and drafts next steps automatically. You can refine ideas by chatting with it and send drafts to tools like Notion or Google Docs in one click. | Rquires meeting context, emails, and work documents to do its best work. | |
Neurodivergent thinkers | Combines notes, tasks, and AI-powered search in one place. It focuses on fast capture, automatic organization, and conversational retrieval, reducing the need for manual tagging or complex systems. | Users have reported issues with deleting tasks. |
Each of these tools can be effective, but only if you actually use them. A beautiful system that collects dust is still clutter.
One Redditor summed it up nicely:
“I forced myself to use only Apple Notes. It was horrible. Then it was amazing.”
After a week, they realized they’d stopped thinking about the app entirely and started thinking about their work again. That’s the hidden strength of simple tools: they don’t get in the way.
A recent community poll on r/PKMS (440 votes, late 2025) shows Obsidian as the clear favorite, followed by Logseq, with tools like Capacities and Anytype building smaller but loyal followings.
![\"\"](\"https://framerusercontent.com/images/Hm6wApJBxcFj8Nxm3pi0WHXTf0c.png\")
Image credit: Reddit user u/TomLucidor, r/PKMS
The results reflect what many PKM users have been saying elsewhere:
Obsidian stays on top for flexibility, control, and local storage.
Logseq wins over people who want something simpler and ready to use out of the box.
PKM should evolve naturally. It does not need a full reset every few months. Instead of redesigning your setup, build in regular reviews to keep it light and useful.
A short weekly or monthly review can make a big difference. Ask yourself:
What notes or ideas am I using regularly?
What feels heavy or unnecessary?
Which parts help me think, and which just take time?
The goal is to adjust, not reinvent. A short check-in once a week is better than a perfect overhaul once a quarter. The more regularly you revisit your system, the more it starts working for you instead of against you.
Try this: At the end of the week, delete or archive one note that no longer matters. Clarity grows by subtraction.
Example: If you captured a meeting note or article summary you never revisited, archive it. A lighter system is easier to trust.
Complexity kills momentum. A PKM system that feels heavy will eventually stop being used. The most effective systems are the ones that feel invisible. They get out of your way so you can think clearly. If your PKM demands more attention than the ideas inside it, it has become a distraction.
As PKM expert Nick Milo, founder of Linking Your Thinking, says, the goal is “less collecting, more connecting.” In his video below, he explains the knowledge loop: the cycle of encountering information, thinking about it, and then expressing it.
Most of us rush from input to output, reacting instead of reflecting. But as Milo puts it, “don’t cheat the middle.” That middle, the thinking space, is where ideas mature and connections form. It is also where your PKM system proves its worth.
Try this: Once a week, open a recent meeting summary or note. Link it to one other idea or project you’re working on. Those small, thoughtful connections are what keep your PKM light and alive.
Example: You finish a project meeting. The Supernormal app captures the discussion and drafts a summary. Before moving on, you take a minute to highlight one key insight and push the summary to your project notes in Notion. The next time you open that project, the context is already there; a connected record of what was decided and why.
There is no right way to do PKM, only the way that helps you think better. A system built around someone else’s template may look impressive, but it will rarely feel natural.
The best PKM systems don’t exist to show how organized you are. They exist to help you make sense of what you know and act on it.
", "url": "https://www.supernormal.com/blog/how-to-create-pkm-system", "title": "How to create a pkm system that fits the way you think", "summary": "Build a simple PKM system that fits how you think. Capture ideas, stay organized, and turn knowledge into action without complex workflows.", "date_modified": "2025-12-15T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "Productivity" ] }, { "id": "urn:sha256:adfc5abe7014ec6134fa19a4c06c2f02953b6bdaf0c487e2fb3affb4d7698fc6", "content_html": "If you want a second brain that helps you think more clearly and stay organised, these are the best picks for 2026:
Mem for an AI second brain that organises itself
Obsidian for deep thinking and linked notes
Supernormal for people in lots of meetings
Notion for flexible, visual systems
Tana for structured thinking that grows as you write
Heptabase for those who think visually and plan with whiteboards
Tool | Best for | Key features | Pros | Cons | Pricing |
Mem | People who want a second brain that organises itself | AI-powered organization, automatic tagging, fast retrieval, mobile app | Zero maintenance, excellent search, great for capturing without thinking | Can feel too loose, limited control if you enjoy designing systems | Paid plans only (typically mid-range personal productivity pricing) |
Obsidian | Non-linear thinkers who want control and local storage | Local Markdown vaults, backlinking, graph view, plugin ecosystem, integrates with COG AI workflows | Fully private, endlessly customisable, great for connecting ideas | Easy to overcomplicate, setup requires learning curve | Free core app, optional paid sync and publish |
Notion | People who want an all-in-one workspace for tasks, docs, and notes | Databases, templates, flexible views, Notion AI for summarising and drafting | Easy to customize, strong at project and knowledge hubs, great collaboration | Systems can become high maintenance, can feel slow for large workspaces | Free personal tier, reasonable paid plans |
Supernormal | People whose most important work happens in meetings and want a second brain that builds itself | On-device meeting capture, instant summaries, decisions, actions, ready-to-send drafts | Automatic context, no bots, structured memory from calls, fast follow-ups | Not for people who are rarely in meetings | Free to try, pricing coming soon |
Tana | Connected thinkers who want structure that emerges naturally | Supertags, AI-assisted cleanup, daily notes, linked structure | Powerful but simple workflows, automation without heavy setup | Was invite-only for a long time, conceptual learning curve | Subscription required |
Heptabase | Visual thinkers handling complex ideas or research | Card-based whiteboards, clustering, PDF annotation, AI summarisation | Perfect for mapping information, great for synthesis, intuitive once set up | Desktop first, fewer collaboration features than Notion, can feel pricey for casual use | Paid plans only |
Second brain tools didn’t take off because people suddenly fell in love with productivity trends. They became popular because knowledge workers reached a breaking point. Too many tabs. Too many conversations. Too many ideas slipping away. Most people needed a reliable offload system so their minds could spend more time thinking and less time remembering.
Tiago Forte’s CODE method (Capture, Organize, Distill, Express) gave people a simple process they could follow. It also made something obvious: your brain is for having ideas, not keeping all of them in active memory. As people adopted these habits, many unknowingly built lightweight personal knowledge management systems along the way, similar to the simple approach we outline in our guide on how to create a PKM system.
Some of the rise was driven by people whose minds work fast or differently. ADHD, autism, and other forms of neurodivergency often come with rapid idea generation, associative thinking, and executive load challenges. Many of those users were already exploring personal knowledge management through that lens.
2000s: Evernote and the cloud
Evernote helped define the modern era of cloud-based personal note-taking. It made the idea of a searchable, cross-device “backup brain” feel accessible to a broad audience. Over time, added features, pricing changes, and growing competition from simpler or built-in apps led many long-time users to reconsider their setup.
2019–2020: Roam Research
About a decade later, Roam Research popularised bi-directional links and graph-style thinking in a way that caught the attention of knowledge workers and PKM enthusiasts. Instead of nested folders, Roam encouraged networks of ideas, helping many people see personal knowledge bases as living graphs rather than static archives.
2016–2021: Notion
Notion’s public release in 2016 and rapid growth after 2019 turned “second brains” into customizable workspaces rather than just note collections. Its mix of pages, databases, templates, and different views let individuals and teams design their own systems for projects, documents, and tasks all inside one flexible interface.
2020–2021: Obsidian
Obsidian emerged at the start of the 2020s and brought local-first, Markdown-based knowledge management back into the spotlight. It appealed to people who cared about owning their files, extensibility, and deep customization, with plugins and custom workflows that could grow alongside their ideas.
2020–2023: AI-first note tools
In the early 2020s, tools like Mem began to lean heavily into AI-led organisation and resurfacing. Instead of asking users to design elaborate folder systems, these apps focused on quick capture, semantic search, and automatic reminders of what mattered, nudging the category toward “self-organising” second brains.
2024–2025: Supernormal and workflow-native second brains
The latest wave centres on tools like Supernormal that build a second brain directly around everyday workflows, especially meetings. Rather than just recording calls, Supernormal quietly captures your meetings, turns them into structured summaries, and drafts follow-ups and updates in your existing tools. This shifts second-brain thinking from static storage toward live, automated execution woven into daily work.
To compare second brain apps fairly, we looked at each one across the five criteria that matter most to people doing modern knowledge work:
1. Day-to-day usability
Is it easy to learn how to use? Does it help you think, not slow you down?
2. Speed and friction
How quickly can you capture something? Can you find it again with zero stress?
3. Flexibility
Does the tool support different thinking styles without forcing a rigid structure?
4. Long-term reliability
Will your notes still be accessible years from now? How portable is your data?
5. Real-world workflows
Does the tool support how people actually work — from meetings and research to daily planning and creative thinking?
We also looked at Reddit threads, YouTube reviews, and direct user feedback to understand how each tool performs rather than just what it claims on the website.
If you want to understand how these tools fit into a wider AI workspace ecosystem, we break down the bigger picture in our guide on AI workspaces explained.
Best for: people who want a second brain that organises itself in the background
Mem 2.0, announced in October 2025, is a complete rebuild designed to make the app faster, more reliable, and more capable as an AI partner. It adds offline support, smarter capture tools like voice mode, and a more agentic AI layer that can act on your notes instead of only organising them. Even with the overhaul, it keeps Mem’s core promise intact: you drop in thoughts, and it quietly makes them usable.
![\"Screenshot](\"https://framerusercontent.com/images/7MBhdsYZgKAwrwiLnA6K5R3JABE.png\")
Mem 2.0’s redesigned home screen, which now combines notes, collections, and an AI chat panel to help you capture and organise ideas with less setup.
Mem is one of the few second brain apps that genuinely lowers your shoulders the moment you start using it. You don’t need to think about folders or structure. You just write and Mem quietly does the organising for you.
One reviewer on Reddit put it perfectly: they said Mem was the only tool where they felt relieved after taking a note, because they didn’t have to think about where it lived or how they’d find it again. That lines up with my experience. When you drop something into Mem, you trust it won’t disappear into a black hole.
It’s not perfect. The structure-light approach can feel too loose if you love clearly defined systems. Some people will want more control than Mem is willing to give. But if your goal is to have a reliable, low-maintenance repository, it nails that use case better than almost anything else.
Mem shines at the exact pain point where most second brain systems fall apart: retrieval. Note-taking is easy; finding things later is hard. Mem flips that dynamic by letting AI handle the classification and resurfacing so you don’t have to keep the system alive manually. The less you manage it, the better it works.
You want a tool that handles structure automatically
You capture lots of notes but rarely review them
You value fast, AI-powered retrieval
You want everything in one place, including mobile use
Across Reddit, reactions to Mem 2.0 range from impressed to conflicted. Many people who’d written off earlier versions say the upgrade feels dramatic, describing it as “night and day” and praising how well the AI now organises messy notes or connects related ideas. Others highlight the new handwriting recognition and PDF reading as standout improvements.
At the same time, the community is open about the rough edges. Several users mention slow performance, missing integrations, or uncertainty about whether the pricing feels right for what Mem offers today. A few long-time testers also point out frustrations with export options, task management, and features that still feel behind other AI-driven tools.
A snapshot of common reactions:
“Night and day compared to the old version.”
“AI organising my chaos… game-changing.”
“I’m just not sure it’s worth the price yet… it doesn’t feel fun to use.”
“I’m having great success doing everything through the AI chat — it’s organising everything.”
“It feels slow sometimes, which makes the price harder to justify.”
“Best retrieval I’ve used, but I wish the task system was better.”
Overall, people see Mem 2.0 as a big leap forward, especially for retrieval and automated organization, even if performance and deeper workflow features continue to shape how users feel about sticking with it long term.
Best for: people who want total control, local storage, and deep connections
Obsidian is the tool people graduate into. It’s powerful, extensible, endlessly customizable, and surprisingly lightweight once you get the hang of it. But it can also be the place where second brains collapse under their own complexity if you stack too many plugins or frameworks on top of each other too quickly.
At its core, Obsidian is just Markdown files stored locally on your device. That simplicity is a feature, not a limitation. It means you own your notes completely, they are portable to any future tool, and you can use any workflow or structure that suits the way your brain works.
Where Obsidian becomes genuinely special is in the way it lets you connect ideas. Backlinks, graph views, knowledge maps — it’s perfect for people who think in links and patterns rather than rigid hierarchies.
While Obsidian can be complex, it also allows for minimal-maintenance systems. One of the most interesting emerging approaches is a system called COG, which stands for Claude + Obsidian + Git.
![\"A](\"https://framerusercontent.com/images/bN5svKnN8w0VLBayvPlh79vbM.webp\")
Source: 0xlight, r/ObsidianMD
COG is essentially a self-organising second brain built on top of Obsidian. The idea is simple:
You dump messy thoughts into Obsidian
AI (Claude) automatically organises, categorises, and links them
Everything stays in plain Markdown files
Git keeps a full history of your thinking
You don’t do any manual filing or maintenance
It’s built for people who love capturing ideas but never want to tag, reorganise, or structure them by hand. If you’re the type who can take notes all day but never gets around to reviewing or sorting them, COG is a clever way to let AI handle the parts you’d normally abandon.
It won’t be for everyone, but it shows what Obsidian can become when you combine a flexible foundation with the right kind of automation. While it’s brilliant for deep thinking, it’s not the fastest tool for quick capture, especially on mobile, so it fits best when you have time to think rather than when you need instant drop-and-go notes.
You want total control and local-first storage
Your thinking is non-linear and relies on connections
You want an AI-supported system that reduces manual maintenance
Across reviews, including this detailed look from Android Authority and a 2025 community report card from creator Mike Schmitz, the consensus is remarkably consistent: Obsidian is one of the most powerful second-brain platforms available, but it asks a little more from you than other tools.
“The desktop app is practically perfect — fast, clean, and handles huge vaults without blinking.”
“Mobile is fine for reference, but not great for quick capture. It needs work.”
“Community plugins are incredible, but long-term maintenance is hit-or-miss.”
“I love owning my notes. Knowing everything is just Markdown gives me peace of mind.”
“Obsidian clicked for me when I stopped chasing the perfect setup.”
Most reviewers place Obsidian in the “expert tier” of second brain tools due to its unmatched flexibility and longevity, as long as you don’t mind shaping it a little to fit your brain.
Best for: people who want a flexible, visual workspace for notes, tasks, and knowledge
Notion is the tool most people stumble into long before they ever say the words “second brain.” Teams use it for company docs, project hubs, hiring pipelines, and internal wikis, so for many knowledge workers it becomes their first exposure to the idea of storing thinking outside their head. If you’ve ever joined a business where everything lived in Notion, you’ve already brushed up against knowledge management without noticing.
![\"Screenshot](\"https://framerusercontent.com/images/HtEG3OEMLltKRIJ9qRUAr3BcId4.png\")
Notion’s flexible pages let you structure documents, brand guidelines, and project materials in one place, making it easy to build reusable templates.
As a second brain, Notion feels instantly powerful. You can mix documents, databases, meeting notes, tasks, and lightweight knowledge bases in one place. You can switch views whenever you want: table, gallery, calendar, Kanban. That flexibility makes it easy to build a system that feels like yours.
The catch is that freedom can become its own tax. Many people design beautiful setups that quietly collapse because they require constant maintenance. It’s deceptively easy to spend more time perfecting your system than actually using it.
Notion is ideal if you need structure without coding or configuring plugins. PARA templates, weekly review dashboards, and task databases are simple to set up. Notion AI also helps tidy notes, summarise pages, pull out highlights, or turn scattered thoughts into something actionable, all without switching tools.
![\"Screenshot](\"https://framerusercontent.com/images/HuloA8DRvwWmv7uNa2yV6f3ddXE.png\")
Notion AI can generate full databases like this task tracker, complete with properties and views, turning a simple prompt into a structured workspace.
And because so many companies already run on Notion, using it personally can create a sense of continuity. Your work notes, project docs, and reference material already live there, so adding your own second brain on top feels natural.
You like visual organization and flexible views
You want a single workspace for tasks, notes, docs, and knowledge
You prefer templates over tinkering
You want built-in AI but don’t need deep customisation
Your workplace already uses Notion and you want a system that fits around it
Looking at the community responses on Reddit, people tend to fall into two camps: those who love Notion’s flexibility, and those who find it overwhelming or too much work to maintain.
Here’s a snapshot of the most common themes:
“I really love Notion… I use it daily for solo projects and organising my household.”
“It gets a bad rep because people optimise it instead of doing the actual work.”
“I tried so hard but it felt like too much work. I want something that just works, not something I have to build.”
“I love the UX overall… the most complete docs experience I’ve found.”
“It’s cluttered and time-consuming. I deleted it the same day I installed it.”
“It does too much and too much is going on.”
“Databases are very practical. I put all my college notes in Notion.”
“The newbie mistake is using someone else’s over-designed template. Start simple.”
Overall, users who take the time to shape Notion around their workflow tend to swear by it. Users who want something lightweight, fast, or opinionated often bounce after a day or two.
Best for: people who want their meeting insights turned into summaries, actions, and drafts automatically
Supernormal is for people whose workday revolves around meetings. Instead of starting with blank pages and frameworks, the Desktop App captures your conversations, then uses that context to generate summaries, next steps, action items, and ready-to-use drafts you can copy into Slack, Gmail, Linear, or anywhere else you work.
![\"Screenshot](\"https://framerusercontent.com/images/E0pKcwSTADx5tNzQwvLHKGTfRU.png\")
The Supernormal App home screen brings your day into focus, surfacing upcoming meetings, instant summaries, and suggested follow-ups so you can move from conversation to action.
If Notion, Obsidian, and Mem start with notes you write yourself, Supernormal starts with the conversations that already fill your calendar. It becomes the automatic layer of memory between what happened in a call and what needs to happen next.
“It’s fascinating that we can help you document the conversations, but it’s way more interesting if we can help you do the work after the conversations.” - Colin Treseler, Supernormal Co-Founder
For people who spend most of their day switching between internal standups, client reviews, creative syncs, and project check-ins, that shift from “notes” to “doing the work” is the whole point.
Supernormal’s strength is how it handles context. Every meeting you finish becomes a searchable, structured record with summaries, decisions, and actions. You can reopen a past call and instantly see what was agreed, what slipped, and what needs to be followed up. This removes the mental load of remembering fifty moving parts across fifteen conversations a week.
Supernormal can also generate contextual drafts for you. Need a follow-up email, a project update, or a summary for your marketing lead? Supernormal writes a starting point using the content of the meeting itself. It feels like your second brain is not just storing information but helping you act on it.
![\"Screenshot](\"https://framerusercontent.com/images/qYRrXngIePSC4nLETtk1KToaLBA.png\")
The Supernormal App generates clean, structured summaries for every meeting, capturing decisions and key points so you can review information or share updates without rewriting notes.
You focus on high-stakes knowledge work that happens during calls
You want automation to turn conversations into actionable memory
Your week is built around meetings and live conversations
You need a reliable way to turn discussions into actions without rewriting notes
You want a second brain that works automatically rather than one you have to maintain
You prefer on-device capture and a private workflow
You often think “what did we decide in that call?” and want instant clarity without digging
Supernormal is still early, but the sentiment from users so far is consistently strong. People who rely heavily on meetings say it saves them meaningful time and produces cleaner, more actionable outputs than most tools in the category. A few themes stand out both in direct user feedback and in early reviews like this one from The Business Dive.
“I’ve been using Supernormal for the last few months, and it’s become an essential part of how I capture and repurpose my meetings… It can prioritise among all the things discussed in a meeting and rarely gets it wrong.”
“The accuracy of the content is one of the best I’ve used so far… My clients were impressed with the MoM pointers it generates.”
“Confirmed working! Great speed. Enjoying the pace of updates.”
“As a video production company… having a shortcut like this that can instantly generate structured, creative thinking is incredibly useful for us.”
Several users mentioned they’d like optional video recording in the future, especially for training sessions or calls where visuals matter.
Most people who try Supernormal describe the same shift: they stop worrying about capturing meetings and start relying on the summaries, action items, and shortcut-generated drafts to move things forward.
Best for: people who think in webs of ideas and want structure to emerge automatically
Tana is one of the most ambitious second brain platforms out there. It blends the fluidity of networked notes with the structure of databases, all wrapped in a clean, fast interface that feels purpose-built for people who think in connections. If Obsidian is a blank canvas and Notion is a canvas with grids, Tana is more like a living, organic system that grows with you.
What makes Tana different is its core idea of “Supertags”. Instead of manually building databases or templates, you shape your system by tagging notes with types like “project”, “meeting”, “task”, or “idea”. Each tag can carry its own fields, views, and automations, which means your second brain becomes more structured the more you use it, rather than through hours of setup.
![\"Tana](\"https://framerusercontent.com/images/Pu6k7GJGfJtjoztaFUpsEsk0v8.webp\")
Tana’s Supertags dashboard, where structured tags turn everyday notes into an organised, connected workspace.
Tana is incredible for people who like connected thinking but want more structure than a pure graph tool. Supertags let you build lightweight systems without feeling like you’re maintaining a database. You can write a messy daily note, tag something as a task, and it will automatically appear in your task view. No fiddling, no routing, no admin.
![\"Tana](\"https://framerusercontent.com/images/9O8B6Nf9UVs9atv6hoQbo6hTQk.webp\")
Tana’s Task Supertag turns notes into a simple, structured task board for managing work in context.
Its AI features also help clean up the chaos. You can summarise notes, extract action items, or reshape a messy brain dump into a structured outline. Paired with Tana’s ability to link everything together, it becomes a powerful place to think, plan, and revisit ideas as they evolve.
You think in webs rather than folders
You like the idea of Obsidian but want something more guided
You want structure to emerge naturally from your writing
You enjoy daily note workflows
You want an AI layer that helps shape and reorganise your thinking
Across Product Hunt, the sentiment is unusually consistent: people don’t just like Tana, they feel like it finally solves a problem they’ve wrestled with for years.
One reviewer called it “revolutionary,” explaining that after bouncing between Evernote, Roam, Notion, Logseq, and Capacities, Tana was the only system flexible enough to hold any kind of information without forcing them into someone else’s structure.
Another described it as the first app that didn’t fight their brain: “It finally lets me work the way I think.”
A few especially enthusiastic notes from users:
“Tana stands in a league of its own… it adapts to how my brain works.”
“It’s the note-taking tool of the future.”
“I switched from Notion and now I’m addicted.”
“Supertags keep everything organised without extra effort.”
“The community is amazing — supportive, active, and full of power users.”
For many reviewers, Tana becomes the place where projects, knowledge, and daily thinking all converge. The tradeoff is portability, since your information lives inside Tana’s structured graph rather than plain files, which makes it powerful but less future-proof than a local Markdown system.
Best for: visual thinkers who organise ideas through cards, clustering, and spatial layouts
Heptabase is a second brain for people who think best when they can see their ideas. Instead of working inside folders or long documents, you create cards and lay them out on infinite whiteboards. It feels a bit like covering your desk in Post-its, except everything is searchable, linkable, and organised in a way that actually scales.
Where other tools expect you to think in lists or outlines, Heptabase lets you organise by proximity and shape. You drag ideas around, cluster them, connect them, and rearrange them as your thinking evolves. For research-heavy work or anything complex enough to need real synthesis, that visual approach hits differently.
Heptabase shines when you need to make sense of messy information. The card-based system helps you break ideas into small, digestible pieces, and the whiteboards become a canvas where you can map relationships, spot patterns, and build structure without committing to a rigid system upfront.
![\"Heptabase](\"https://framerusercontent.com/images/PDU4cIBh2HlldIOcryFqi1JKQAc.png\")
Heptabase’s onboarding whiteboard teaches new users how to create cards, organise ideas, and navigate core features.
It supports backlinks, tags, PDF highlighting, AI-generated summaries, and knowledge linking, but the power comes from how visual it is. Your second brain becomes a living map rather than a vault of notes.
It’s especially popular with designers, PMs, researchers, and people who handle long-form content because it helps you zoom out on your thinking, not just store it.
You’re a visual thinker who loves clustering, sorting, or sketching ideas
You manage complex research or multi-step projects where relationships matter
You prefer cards and whiteboards over long notes
You struggle to make sense of huge blocks of text in other apps
You want a second brain that feels tactile and flexible
A lot of the most helpful feedback on Heptabase comes from people who’ve used several second brain tools and finally found something that matches how their mind works. Tom from the Paperless Movement summed this up well in his YouTube review (see below) where he shows how the visual connections, whiteboards, and meaning-rich links create a workflow that other tools don’t quite reach.
On Product Hunt, the sentiment is strikingly consistent: Heptabase finally gives visual thinkers a home. Many people say they tried everything before realising Heptabase was the first tool that matched how their mind actually works.
One reviewer called it “the most powerful visual learning tool I’ve ever used,” praising its clear UX and rapid updates. Another said it was “game changing for brainstorming,” especially for mind mapping and fast idea synthesis.
A few especially enthusiastic notes from users:
“It’s not just a notebook — it’s a new way to extract and connect your thoughts.”
“The UI and UX show a level of thought that other tools never reach.”
“I moved from Notion and Obsidian… this became my main app.”
“Visual note-taking and mind mapping are unmatched.”
“The team listens, ships constantly, and the community feels involved.”
Some users mention performance issues on very large boards, but even those reviews stress how strong the core ideas are. Similar to Tana, the main tradeoff is portability, but for a different reason: Heptabase’s spatial whiteboards and card layouts don’t translate cleanly into other tools, so long-term exportability is trickier.
A recent post in r/secondbrain really stuck with me. The OP (original poster) listed five abandoned second brain attempts — Notion, Obsidian with a swarm of plugins, even a custom React app — all of which collapsed under the weight of manual organization. What finally worked was letting AI do the sorting and synthesising, while they focused on ideas. It’s a familiar pattern for anyone who has tried to build a perfect system only to discover that “maintenance” becomes the system.
“The pattern was always the same: initial excitement → capture lots of notes → manual organization becomes overwhelming → abandon ship. This time, I tried something different: I made AI do all the boring parts. Three months later, I'm still using it. First time that's happened.” OP, r/secondbrain
This is a pertinent reminder that the right tool isn’t necessarily the one with the most features, but the one that quietly keeps your system usable even when you don’t have the energy to maintain it. That’s the bar you’re trying to clear. Not maximal power, but minimal friction.
So when you’re choosing a second brain app, here’s what actually matters:
Ease of capture
You should be able to save a thought before it disappears. Quick notes, inboxes, clipping, transcripts… whatever helps you avoid “I’ll write it later,” because later rarely comes.
How much manual organization it expects from you
Tags, folders, linking, metadata — all useful, all massive sources of overhead. Tools that help you automate filing or surface things when you need them tend to last longer.
Search that understands how humans remember
You won’t remember the title of the note you wrote six months ago. You will remember a phrase or a concept. Search needs to work with that, not against it.
Compatibility with how you already work
If all your thinking happens in meetings, a conversation-centric tool like Supernormal Notetaker can function as your second brain because it automatically turns your calls into action-ready context. If you live in deep research or longform writing, a Markdown-based app might feel more natural.
The right amount of automation
AI is most useful when it takes away the admin you were never going to do anyway, things like summarising, categorising, pulling context together. You don’t need a robot to run your life, just something that stops your system collapsing the moment you get busy.
Choose the tool that supports how your brain works on a normal Tuesday afternoon, not the tool you imagine you’ll maintain after watching a productivity guru on YouTube. The more honest you are about that, the stronger your second brain becomes.
", "url": "https://www.supernormal.com/blog/best-second-brain-apps", "title": "The second brain apps that will redefine thinking in 2026", "summary": "Discover the best second brain apps for 2026. Find tools that help you think more clearly, organize ideas, and stay on top of complex work.", "date_modified": "2025-11-28T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "Productivity" ] }, { "id": "urn:sha256:5e733780653e805a4421a699ed2b039c2bc01e1103817126631bcbcd97765714", "content_html": "A workspace used to mean a desk. Then it meant a computer desktop, and a jumble of apps, documents, chats, and endless tabs. Now it’s becoming something new: a digital environment that doesn’t just hold your work but helps complete it too.
“There’s going to be a whole category of AI workspaces, tools that don’t just organize your projects, but actually work across them. Documents, presentations, tasks, and decisions will start to live in the same, intelligent space.” - Colin Treseler, CEO, Supernormal
The next wave of tools is blurring the line between where work is stored and where it gets done. Instead of apps that capture output, we’re seeing “AI workbooks” — interactive spaces designed for collaboration between people and AI. The workspace itself is becoming an active participant in the work.
Interest in this shift is rising fast. In the past year, U.S. searches for “AI workspace” have surged, reaching an all-time high in late 2025, according to Google Trends.
![\"Google](\"https://framerusercontent.com/images/PWh0FwyxVvoUDbqKo51TV9Khq4g.jpg\")
Google Trends data showing the steady rise in U.S. search interest for “AI workspace” from early 2024 to late 2025.
This shift is giving rise to a new category: the AI workspace.
The term is starting to appear everywhere, from Notion’s “AI workspace for teams” to emerging tools like Saga, Glean, Logically, and Skywork. Even long-established platforms such as Miro and Google Workspace are being reshaped by artificial intelligence, though not always in the same way.
Each tool is chasing a similar promise: a space where your ideas, notes, meetings, and next steps live together and where AI helps connect them.
An AI workspace is a unified environment that uses artificial intelligence to connect your notes, meetings, and documents into one living system that keeps context, continuity, and completion in sync.
If traditional productivity tools were filing cabinets, AI workspaces are more like creative partners. They read the room, remember the details, and help move things forward.
A true AI workspace does three things well:
Understands context. It knows that a paragraph in your notes connects to a task or project in your calendar.
Generates outputs. It helps write, summarize, or execute tasks, not just organize them.
Adapts to you. It learns your patterns and preferences so it can anticipate, not just react.
This combination of intelligence, memory, and initiative is what separates AI workspaces from AI features. The best ones feel almost invisible, thinking with you, not for you.
But as more tools adopt the label, not all of them mean the same thing.
Notion helped define what a “workspace” could be long before AI entered the picture. It was the all-in-one hub for documents, tasks, and notes, flexible enough to become whatever users needed it to be.
Now, with AI woven into its writing and knowledge tools, Notion has turned that foundation into something smarter: an AI workspace that can summarize, draft, and organize as you work.
👉🏻WATCH: Make with Notion 2025 explores how thoughtful workspace design helps AI deliver real value.
But Notion’s approach is just one flavor. It’s still rooted in documents, a place where structure and content come first. Others start from different directions:
Logically leans into focus and clarity, stripping away distractions to help people write and reason better.
Saga works more like a mind map that never stops growing, connecting notes into a living knowledge graph.
Glean adds an AI layer across entire companies to make scattered information instantly searchable.
Jeda.ai and Skywork experiment with visual, collaborative spaces that feel closer to digital whiteboards than word processors.
Some tools are built for individual focus, others for collective intelligence. But the most meaningful progress may come from workspaces that merge the two — spaces where you can collaborate not only with your team but with AI agents that understand your shared goals.
As the term becomes a buzzword, it’s worth asking what separates true AI workspaces from tools that simply use AI.
Take Google Workspace with Gemini. It’s an extraordinary suite of AI-enhanced tools, each capable, context-aware, and deeply integrated. Gemini can summarize emails, generate presentations, and even act as a meeting assistant.
With Agentspace (now folded into Gemini Enterprise), Google moved closer to the idea of AI-driven environments.
Agentspace combines Gemini’s reasoning capabilities with enterprise data, allowing teams to create custom agents that can search, summarize, and act across systems like Drive, Jira, and Confluence. It’s a powerful model for organizations, a kind of “workspace of workspaces,” where every employee can access or build specialized AI assistants to automate parts of their job.
But even with that sophistication, Google’s model is still a network of AI-enabled apps, not a single, living workspace where context continuously flows from thought to output. Enterprise ecosystems like Gemini and Agentspace orchestrate knowledge across teams. True AI workspaces will help individuals and teams move fluidly from ideas to action, without orchestration at all.
Even OpenAI’s Sam Altman has hinted at what might come next for workplace tools. In a recent conversation with Tyler Cowen, he described the potential for an AI-driven replacement for today’s productivity suite, one where agents handle most of the work and humans step in only when needed.
👉🏻WATCH: Sam Altman discusses the future of AI-driven productivity tools on Conversations with Tyler.
That’s the difference between tools that have AI and tools that are AI workspaces. One enhances productivity. The other is built from the ground up for an AI-native environment.
Most of today’s productivity tools, even the most advanced, still expect humans to do the integration work. You tag, link, and move information between apps — one for writing, one for meetings, one for planning. It’s a constant shuffle of context.
AI workspaces are starting to close those gaps. They act as connective tissue between how you think, what you decide, and what gets done.
Imagine taking notes during a brainstorm and having your workspace instantly draft the project outline. Or summarizing a client call and getting a ready-to-send follow-up. That’s not automation for its own sake, it’s the digital version of working with a sharp assistant who already knows what you mean.
As AI becomes more deeply embedded in our tools, the center of gravity in digital work is shifting again, from documents to decisions. The next generation of tools isn’t being built around where information is stored, but around the moments where work actually happens: meetings, brainstorms, and discussions.
Some newer tools are focusing less on structure and more on flow. They start with the conversations where ideas are formed and carry context forward automatically, turning spoken thoughts and meeting notes into next steps and drafts. That continuity, from discussion to output, is what makes this new generation of tools feel almost human in how they work alongside you.
It’s a quiet but significant evolution. Productivity tools once demanded constant maintenance; AI workspaces are beginning to manage themselves.
If you already work across multiple tools, the short answer is yes. But not every AI workspace fits every kind of work. Choosing the right one depends on how you think and what you do most often.
Here are a few ways to decide if it’s worth trying and what to look for.
If your day involves writing, brainstorming, or designing, look for an AI workspace that supports flow.
Tools like Notion and Logically help turn early ideas into structured drafts, while visual-first spaces like Figma Make, Jeda or Skywork keep creative thinking flexible.
The key is to choose something that feels like a canvas, not a checklist. It should help you capture sparks of inspiration and turn them into something tangible without breaking your rhythm.
If your work revolves around reading, analyzing, and connecting ideas, you need a workspace that can think alongside you.
NotebookLM and Saga both excel here, helping you surface connections across large sets of notes or documents. They act like collaborative thinking partners that make it easier to move from insight to synthesis.
If your focus is on moving projects forward, look for a workspace that blends planning and execution.
ClickUp is a strong example. What began as a project management tool has expanded into a converged AI workspace that combines tasks, documents, chat, goals, dashboards, and automations under one roof. Its new AI system, ClickUp Brain, connects context across your work so agents can summarize updates, draft content, or assign tasks automatically.
It’s designed for teams that want to close the gap between planning and doing, offering structure without rigidity.
If collaboration is central to your work, look for AI workspaces that connect context rather than just share files.
Glean, Notion, and Google Workspace all make it easier for teams to access the same knowledge base and surface what matters. The more distributed your team, the more valuable that shared understanding becomes.
If most of your work happens through conversation, such as team check-ins, client calls, or investor updates, the hardest part isn’t remembering what was said. It’s acting on it.
AI workspaces built around meetings help bridge that gap. Supernormal Notetaker captures key discussions directly from your Mac and turns them into summaries and next steps you can share or build on. It turns scattered insights from conversations into structured progress, helping leaders spend less time chasing details and more time leading.
Before you try anything new, ask two simple questions:
Does this tool make my work feel lighter or heavier?
Does it help me think better, not just faster?
If the answer to both is yes, you’ve probably found an AI workspace worth keeping.
The modern workday is overflowing with tools, each one promising to make you faster, yet together creating more friction than focus. Between documents, chats, and apps, productivity has become a juggling act.
AI workspaces are the first real correction to that sprawl. They don’t just help you manage work; they bring it back together. Instead of scattered systems competing for your attention, they give you one intelligent environment where ideas flow naturally into action.
The point isn’t to add yet another tool. It’s to bring some calm back to how we work. To replace the chaos of tabs and dashboards with something that feels simpler, more connected, and maybe even a little more human.
", "url": "https://www.supernormal.com/blog/ai-workspaces-explained", "title": "Where and how work happens is changing - meet AI workspaces", "summary": "AI workspaces connect your meetings, notes, and ideas in one living space that helps you move from thinking to doing, automatically.", "date_modified": "2025-11-11T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "AI and agents" ] }, { "id": "urn:sha256:7faf61d86d169962650b7697e02b3e3dbf072087c339582472e04371d10ff918", "content_html": "Meetings are where decisions get made, but without the right tools, key takeaways and action items can slip through the cracks. That’s why more teams are turning to AI notetakers that capture every detail, summarize discussions, and make it easy to act on what was said.
In 2025, AI meeting tools have evolved far beyond simple transcription. Some platforms like Fireflies focus on team-wide meeting intelligence, helping organizations analyze patterns and surface insights across every call. Others like Demodesk specialize in supporting sales teams with an AI-powered note-taker and coach for every deal. And newer assistants like the Supernormal app go a step further by turning meeting output directly into ready-to-send documents, messages, and next steps.
Whether you work in sales, operations, or product, today’s AI notetakers don’t just record your meetings. They help you get the work done afterward.
When creating this list, we focused on practical, real-world utility. Our goal is to help you find the best meeting recording tools for your needs, whether you’re looking for full-video recordings, meeting insights, or the ability to autoshare meeting summaries. Here’s how we made our selections:
We analyzed third-party ratings and reviews
We looked at reviews from trusted sources like G2 to understand what real users value (and critique) about each tool. This helped us spot common pain points and standout strengths.
We highlight each tool’s best use case, not just who’s “#1”
Instead of ranking tools from first to last, we chose to spotlight what each product does best. Some are great for integrating with your CRM and productivity systems, while others shine at generating instant summaries or serving async teams. We believe the best tool depends on how you run your meetings.
By combining user feedback, real-world experience, and tailored evaluation, we’ve curated a list that helps you make an informed choice faster.
Fireflies.ai is an AI meeting assistant designed to help teams remember, analyze, and act on every conversation. It automatically records, transcribes, and summarizes meetings across all major platforms, from Zoom and Google Meet to Microsoft Teams and RingCentral. With advanced conversation intelligence and powerful search, Fireflies turns your team’s calls into an organized, searchable knowledge base.
95% accurate AI transcription in 100+ languages with speaker detection
Automatically records and summarizes meetings, calls, and uploaded files
“AskFred” AI lets you query past meetings to find exact moments or insights
Conversation analytics with talk-time tracking, sentiment analysis, and topic detection
Deep integrations with 50+ tools, including Salesforce, Slack, and Asana
SOC 2 Type II, GDPR, and HIPAA compliance for enterprise security
High transcription accuracy and reliable speaker labeling
Searchable conversation memory across all meetings and teams
Built-in analytics help uncover patterns, risks, and opportunities
Works with virtually every conferencing platform and CRM
Requires a meeting bot to join calls for recording
Large teams may need admin setup to manage channels and permissions
Free plan: Unlimited transcripts (with caveats) with 800 minutes of storage
Pro plan: $10 per seat/month — unlimited transcription and app integrations
Business plan: $19 per seat/month — includes analytics, collaboration tools, and advanced summaries
Enterprise: $39 per seat, per month – includes HIPAA compliance, dedicated account manager, and SSO
“The analysis the AI performs is amazing, and can be adapted based on the insights you're looking to uncover.” - Ethan B, G2
“It’s not perfect. Sometimes the speaker labeling gets it wrong, especially if people talk over each other. Also, if the audio quality isn't great or someone has a strong accent, the transcript might need a bit of manual editing. Another thing is that some of the more advanced features—like deeper integrations or analytics—are locked behind the higher-tier plans. It’s worth it if you use it a lot, but it can feel limiting if you're just getting started or working with a smaller team.” - Fernando B, G2
Fathom is an AI-powered meeting assistant designed to streamline the process of capturing and managing meeting content. Compatible with major video conferencing platforms, it automatically records sessions, transcribes conversations in real-time, and generates concise summaries highlighting key points and action items. Its seamless integration with CRM tools like Salesforce and HubSpot ensures that meeting insights are incorporated into existing workflows.
![\"fathom](\"https://framerusercontent.com/images/QbRTscCzsSQuAxm14iITqzhmQ4.webp\")
Real-time transcription and recording within Zoom, Google Meet, and Microsoft Teams
AI-generated summaries ready in less than 30 seconds post-meeting
Integration with CRM tools like Salesforce and HubSpot
“Ask Fathom” feature enables users to search and extract key insights from recorded meetings
Seamless integration with popular video conferencing platforms
User-friendly interface with real-time highlights and summaries
Generous free plan offering unlimited recordings and storage
Currently requires a bot to be present in meetings for recording and transcription, which some users might find intrusive
Limited integration with tools designed for repurposing meeting content
Fathom pricing includes a generous free plan with unlimited recordings, while advanced AI summaries and collaboration features are available in paid tiers.
Free Plan: Unlimited recordings and storage, with advanced AI summaries available for the first 5 meetings each month.
Premium Plan: $15 per user per month, offering unlimited advanced AI summaries and additional features.
Team Edition: Starting at $19 per user per month, includes team collaboration tools and integrations.
“Fathom’s automatic meeting transcription and AI-powered summarization are incredibly accurate and time-saving. The integration with Zoom is smooth, and I appreciate how it organizes key insights, action items, and questions automatically.” - Jaytirth J, G2
“How it tries to join every scheduled meeting when in some cases it is a meeting which does not need to be transcribed or recorded.” - Sebastian, G2
Supernormal is an AI notetaker designed to simplify how teams document meetings. It connects with Zoom, Google Meet, and Microsoft Teams to automatically capture calls, transcribe conversations, and deliver clear summaries with action items.
Meeting notes are organized by speaker, timestamped, and formatted into a clean, shareable document. Everything is stored in a central workspace so teams can quickly search past meetings, follow up on tasks, and stay aligned across projects.
Automatic meeting capture across Zoom, Meet, and Teams
AI-generated summaries with speaker labels
Action item detection and follow-up notes
Calendar integration for easy setup
Searchable workspace to review past meetings
\"Ask Norma\" feature lets you quickly extract actionable insights from your meeting notes
Notes are structured, accurate, and quick to share
No installation required to get started
Currently works only with supported video platforms
Limited export formats
Supernormal pricing offers a free starter plan with core recording and transcription features, with additional functionality and storage unlocked through paid plans.
Free Plan: $0, includes unlimited meetings across supported platforms and up to 1,000 minutes of storage per user.
Team Plan: $20 per month, unlimited seats with shared credits, offering enhanced features and increased storage.
Business Plan: $40 per month, includes advanced integrations and priority support.
\"I liked the little pop-up that enabled you to annotate your meetings as you went. I liked how it showed up in the Zoom meeting - it came in as audio rather than video so you could hide the non-video participants so it wasn't in your face.\" - Jay K, G2
\"After finishing the free trial, it's hard to compete with other apps that offer more for free, especially the ones with noise cancellation features\" - Verified User, GetApp
![\"\"](\"https://framerusercontent.com/images/xd620bx2ttS26s6iKg8lk51Btt0.png\")
Demodesk is a purpose-built AI sales assistant that takes care of note-taking, CRM updates, and follow-ups so sales reps can stay fully focused on the conversation. Unlike general-purpose notetakers, Demodesk is designed around revenue workflows, capturing every key moment from demos and discovery calls, then syncing notes directly to your CRM.
AI notetaker tailored to sales meetings and deal workflows
Automatically records, transcribes, and summarizes every call
Logs notes, tasks, and updates directly in Salesforce, HubSpot, or Pipedrive
Generates personalized AI follow-ups within seconds
Flags at-risk deals and recommends next steps based on conversation data
Automates CRM hygiene and follow-up tasks
Fine-tuned for sales use cases, not a one-size-fits-all notetaker
High-quality transcription and summary accuracy across 98 languages
Zero setup required and fast adoption for teams
Primarily focused on sales workflows, not general meetings
Custom automations are currently in beta and may require setup time
Coaching & AI: €49 per user/month, which includes AI Assistant (recording, transcription, summaries, CRM sync), AI Coach (scoring, conversation insights), and AI Analyst (performance analytics).
Enterprise: Custom pricing for teams of 30+, with onboarding, dedicated account manager, and premium support.
Viewer licence: Free access for reviewing team recordings and analytics.
“The quick and good results from Demodesk really impressed me; both the summary and the follow-up email were directly on point and very helpful. The email could be sent without major adjustments, and the sales coach provides helpful feedback that will enable us as a sales team to set a similar focus in meetings, depending on the metrics used.” – Michael S, G2
“Sometimes the integration into our CRM feels like it takes too long, so that a seamless continuation of work has to wait a bit.” – Wolfgang W, G2
The right AI notetaker doesn’t just record your meetings, it remembers them for you. Unlike basic meeting recorders that preserve every word, notetakers are built to surface what actually matters: decisions, action items, and key context you can use right away.
Whether you're looking to sync summaries with your CRM, keep remote teams aligned async, or simply lighten your mental load, there’s a tool in this list to meet you where you work.
If you’re not sure where to start, Supernormal offers a frictionless way to get accurate, structured notes, no video rewatches, no extra steps. Just the right information, at the right time.
", "url": "https://www.supernormal.com/blog/best-ai-notetakers", "title": "Most notable AI notetakers 2025", "summary": "Compare top AI notetakers to find the right fit for your team. Capture decisions, summarize conversations, and keep everyone aligned effortlessly.", "date_modified": "2025-11-03T00:00:00.000Z", "author": { "name": "danelle-azzopardi" }, "tags": [ "AI and agents" ] }, { "id": "urn:sha256:4f87287bf1e952c7944982f8825519887c0adaa08ed5bbf8ac6af7e01590e3f9", "content_html": "For a while, the idea of having an AI assistant in every meeting felt like a glimpse into the future. Tools that could automatically join your Zoom or Meet calls, record everything, and generate summaries promised to save time and transform how teams work. But somewhere along the way, the novelty wore off.
Now, when “Fireflies.ai has joined the meeting” pops up, it’s just as likely to prompt an eye-roll as excitement. People mute, cameras go off, and small talk stops dead. The meeting’s tone shifts from human conversation to monitored transaction.
We’re entering an age of bot fatigue: a growing frustration with the visible, intrusive presence of AI in spaces that are supposed to feel personal and collaborative. It’s not that people don’t want AI support. They just want it to feel invisible, private, and secure. And that’s where a new kind of tool is emerging: AI meeting notes without bots.
As the bots disappear, transparency remains crucial. If you’re using AI to record or summarize a meeting, it’s still important to let participants know upfront. AI may be simultaneously more ubiquitous and less visible, but the humans using it still need to focus on courtesy and transparency as they use these tools to achieve more at work.
When AI notetakers first appeared, they solved a real problem. Professionals were drowning in calls, and manual note-taking was impossible to sustain. The first generation of tools, from Otter to Fireflies and Fathom, offered a simple fix: send a virtual participant into every meeting to record, transcribe, and summarize what happened.
It was clever and, for a time, empowering. Bots became silent teammates that never forgot a word. But as they spread, cracks began to show.
Every meeting invitation turned into a potential privacy discussion. Clients asked, “Who’s that?” Colleagues hesitated before speaking freely. Teams started creating parallel workflows to decide when bots were allowed. The supposed automation breakthrough had introduced a new layer of friction.
What began as efficiency started to feel like surveillance. Even the humor around meeting bots says a lot about how people feel.
![\"Cartoon](\"https://framerusercontent.com/images/a4rMNMnUVRsvU3xVAgX2JKhD0OQ.png\")
Source: Tom Fishburne, Marketoonist.
When every call is filled with AI “attendees,” the irony is hard to ignore. The tools meant to make meetings more human can end up doing the opposite.
As AI crept into every aspect of work, enthusiasm turned to exhaustion. People weren’t rejecting AI itself, they were rejecting poorly designed AI experiences that demanded attention instead of saving it.
In meetings, that frustration looks like this:
Bots announcing themselves and interrupting flow.
Participants feeling watched or recorded.
Workspaces cluttered with overlapping AI summaries, transcripts, and recaps.
Conversations shifting from human to performative, with everyone aware they’re being logged.
The frustration has even become meme-worthy.
![\"Meme](\"https://framerusercontent.com/images/dAywJyZWmkG4hOrb9cMtwmSErg.webp\")
Source: Troy Kirwin
It’s funny, but it captures something real: professionals are tired of feeling like every meeting comes with a digital entourage.
And it is not just social media exaggeration. Even professionals who rely on AI daily are feeling that frustration.
![\"Screenshot](\"https://framerusercontent.com/images/JYBp6XAhjaMaPdX6OLH8Oik.png\")
Source: Screenshot from one of Aerin Paulo's recent meetings, where four AI bots joined the same call.
Even people who appreciate AI notetakers admit the experience can feel strange. One Reddit user summed it up neatly:
“I think the main weirdness comes from the bot joining the call as a separate participant with a name like ‘Fathom Notetaker’. It feels a bit like having a silent, faceless stenographer in the room…”
They went on to say that while they appreciate the value (getting accurate notes and summaries so they can focus on the conversation) the difference between internal and client meetings still matters. The idea of announcing “hope you don’t mind, we have our notetaker on” never stops feeling slightly awkward.
This reflects a growing preference for meeting tools that integrate AI quietly, rather than showing up as extra participants. Even when people see the benefit, they don’t necessarily want to see the bot.
To see whether this discomfort shows up in real meetings, I ran a quick poll in a marketing Slack community. I asked: “True or false — AI bots in meetings change the dynamic or the way people behave.”
![\"Bar](\"https://framerusercontent.com/images/u5jedUsO6w5b4hb3KVTfsA18brs.webp\")
Source: A poll I posted in the All In marketing Slack community.
Two-thirds of respondents said they notice a difference when a bot is present, especially in how freely people speak. It’s a small sample, but it echoes what many already say: visible bots subtly change how meetings feel.
“Bot fatigue” doesn’t mean people no longer want AI in meetings. It means they want AI to serve the work, not dominate the space.
Today’s professionals are used to automation. They use AI daily to summarize articles, brainstorm ideas, or rework emails. The frustration isn’t with AI’s presence, but with its form. When the interface becomes the star of the show, such as a bot joining your meeting or a notification announcing itself, it pulls focus away from the actual purpose of the meeting.
This creates three major issues:
Disrupted flow
A sudden “recording started” banner or bot name in the participant list can change how people speak. Instead of focusing on clarity and creativity, they start filtering themselves.
Privacy and compliance concerns
Many companies now block third-party meeting bots altogether due to unclear data storage and privacy policies. Even when consent is granted, users are often unsure where their data goes or who can access it later.
Erosion of trust
When AI shows up uninvited, it undermines confidence. Clients, partners, and even colleagues may interpret it as an invasion of confidentiality, especially in sensitive discussions.
These frustrations have driven a new expectation: that the best AI tools should be powerful but unobtrusive. The best AI is ambient, not announced.
A recent study offers an interesting perspective on how AI should behave in meetings. The researchers introduced the “Observe, Ask, Intervene” framework for AI agents, which encourages systems to quietly observe, ask users for input, and only intervene when invited.
While the study focused on meeting inclusion rather than notetaker bots, its findings echo a broader sentiment: people prefer AI that acts gently in the background instead of interrupting or drawing attention to itself.
The takeaway is universal. When AI respects human agency and stays subtle, people feel more comfortable and more in control. This shift in preference points to a deeper truth: people don’t want to remove AI from their work, they just want it to work with them.
If you talk to professionals today, especially those working in fast-moving, AI-native environments, their message is clear: they want the benefits, not the bots.
They still want automated notes, action items, and summaries, but they want it all to happen quietly, privately, and securely.
They want AI that:
Captures key points automatically
Processes data locally, not in the cloud
Produces next-step work quickly and accurately
Stays invisible while doing it
In short, they want AI that feels personal.
That shift in sentiment is showing up in how people search, too. Queries like “AI meeting notes without bot”, “botless AI meeting assistants”, and “AI meeting assistant that doesn’t join by bot” are starting to rise. They’re small in volume but big in signal, evidence that the market is maturing and expectations are changing.
The most exciting frontier in productivity software isn’t more visibility, it’s less.
Invisible AI is about technology that blends seamlessly into your workflow, anticipating what you need without demanding your attention.
Apple is building on-device intelligence that never leaves your hardware. Tools like Perplexity and Claude emphasize conversation that feels human and intuitive. And on the meeting side, apps like Supernormal Notetaker are embracing a botless model, processing audio locally on your Mac and surfacing insights only when you need them.
This design philosophy is about trust and autonomy. Users keep control of their data, their conversations, and their pace. The AI works for them, not the other way around.
Invisible AI isn’t just a UX choice. It’s a moral and emotional one.
By removing intrusive interfaces, we create space for real connection, focus, and creativity to return to work.
The conversation about AI at work often centers on productivity, saving time, automating tasks, and cutting effort. But the real opportunity lies in how AI can give attention back to people.
When the interface becomes the star of the show, such as a bot joining your meeting or a notification announcing itself, it pulls focus away from the actual purpose of the meeting. The best AI tools should be powerful but invisible, designed to help without interrupting.
As AI becomes part of everyday workflows, thoughtful use matters as much as the technology itself. Even when AI operates quietly in the background, transparency still matters. Going botless removes the distraction of a visible notetaker, but it does not remove the responsibility to be clear about capture. Anyone recording or transcribing a meeting, or using AI to process it, should inform participants and obtain consent, in line with local laws and company policy. A quick chat message or note at the start is often enough to set expectations and maintain trust.
Example you can use:
“Hey, just a heads-up, I’m using AI to capture notes for this meeting so I can stay focused on the conversation. Let me know if anyone prefers that we don’t record.”
Invisible AI is not only a design choice. It is an ethical one. People deserve to know when their words are being recorded or processed, even when the experience feels seamless. Respect for privacy and presence is what turns technology from intrusive to empowering.
The goal is not to eliminate AI from meetings, but to bring the focus back to the people in them, and to design tools that enhance communication rather than mediate it.
If early AI meeting tools were about visibility, showing up in your calls and announcing what they could do, the next phase is about invisibility.
In a few years, the idea of inviting a bot to every meeting will feel outdated, like printing agendas on paper. The tools that win will be the ones that combine intelligence with restraint, that deliver the benefits of automation without the distraction or discomfort.
The future of AI meetings won’t be crowded with avatars and alerts. It will be quieter, smarter, and more personal. AI will still take your notes, write your follow-ups, and move your projects forward, but you’ll never have to explain to your client why a robot just joined the call.
The Supernormal was built from the ground up on the idea that meeting AI shouldn’t get in the way of meetings.
It captures discussions using your system audio, with no browser extensions and no bots joining your calls. When your meeting ends, the app automatically drafts your notes, summaries, and follow-up actions, ready for review in seconds.
This is what AI meeting notes without bots look like in practice:
Automatic capture: The app detects when a meeting starts and captures the discussion seamlessly, without adding a participant to the call.
Instant summaries: Once the meeting wraps up, it presents a clear, ready-to-review summary.
Next-step drafts: It writes your follow-up emails, task lists, and project updates based on what was discussed.
Instead of acting like another participant, the app behaves like an assistant sitting quietly beside you, ready to handle the admin once you’re done.
You stay present in the meeting. Supernormal handles the rest.
The shift from bots to botless isn’t just technical. It’s cultural.
It reflects a growing maturity in how we want AI to fit into our work, not as a visible extra participant, but as an invisible ally that amplifies what we already do best. Bot fatigue is a sign of progress, not rejection. It means we’re learning to demand more thoughtful, human-centered design from the tools that are transforming our workflow.
Meetings should be conversations, not performances. The best technology doesn’t need to show up in your meeting, but the people in it should always know when AI is helping in the background. That clarity builds the kind of trust that keeps collaboration human, even as our tools evolve.
", "url": "https://www.supernormal.com/blog/bot-vs-no-bot", "title": "Bot vs no bot: Why meeting notes without bots are the future", "summary": "Tired of bots joining your meetings? Learn why bot fatigue is growing and how botless AI assistants keep your work private and effortless.", "date_modified": "2025-10-28T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "AI and agents" ] }, { "id": "urn:sha256:551340e912d08e28ab71259baa784f5991d562bcbf25878389dad497bf8f0857", "content_html": "Neurodivergent professionals already drive some of the most creative and technical work today. They see patterns others miss, think quickly, and bring originality to complex problems.
What slows them down is not lack of ability, but friction.
The modern workplace still follows a neurotypical rhythm, with long meetings, multitasking, rigid systems, and constant context switching. For people who think differently, those routines can drain energy fast.
Personal knowledge management, or PKM, offers a different approach. It helps people design systems that match how their brains work instead of forcing them to fit into systems that do not.
Most organizations already use knowledge management systems to store and share information across teams. Personal knowledge management takes that same concept and applies it at the individual level, focusing less on documentation and more on sense-making.
PKM is a way to collect, organize, and connect your ideas, notes, and learnings over time. It keeps important information close at hand and turns insights into usable knowledge.
Some people see it as a “second brain.” Others use it as a way to bring clarity and calm to the constant movement of modern work. It can be as simple as a folder of daily notes or as complex as a system like Notion or Obsidian.
At its best, PKM helps reduce cognitive load. It turns thoughts and ideas into tangible, trackable actions that feel less overwhelming, something that can be especially helpful for people who live with ADHD, autism, PTSD, or executive dysfunction.
PKM works especially well for neurodivergent people because it mirrors how their minds already operate: fast, associative, and full of creative connections that do not always follow a straight line.
It can also support emotional regulation and provide a sense of continuity when memory or focus is inconsistent.
Recent research reinforces this. A 2023 study on designing personal knowledge management systems for people with ADHD found that voice-to-task automation, adaptive to-do structures, and flexible categorization significantly reduced cognitive overload by aligning with users’ natural thought flow. The research emphasized that low-friction, adaptive systems that make it easy to capture and act on ideas quickly help neurodivergent users stay engaged without becoming overwhelmed.
One Reddit user in r/adhdwomen described how building a Notion-based PKM system helped them manage both cognitive and emotional load:
“It’s helped me a ton in managing my anxiety around tasks, appointments, and just general daily management, as I’m not obsessing over the mountains of things to do in my head at all times… The tasks will linger if I miss them in a day, or if there’s a chain of tasks to complete that are done in order, only the first will show to minimize overwhelm. I break down almost everything I want to do into tasks I can finish in a session. It’s very forgiving.”
Commenter, r/adhdwomen
![\"\"](\"https://framerusercontent.com/images/l2iOlNrCgrXwTN5rAQKdRrABzvQ.webp\")
Example of a PKM template for Notion. Source
That idea of forgiveness, where systems adapt to changes in energy and focus instead of punishing lapses, comes up often in neurodivergent communities. A good PKM setup works with your rhythms, not against them.
For others, PKM takes on an even more profound purpose. One Redditor, a brain injury survivor, explained how their PKM became a way to retain identity and memory:
“PKM for me is essentially PKR (personal knowledge retention). After years of obviously existing and doing things, I wish I had at least some recall of that, even the day-to-day things. I finally made significant progress when I found Obsidian last year… Those things like daily notes, tasks, projects, and habit tracking are the scaffolding upon which we build our lives. Those bits become more valuable as we accumulate more of them because they give perspective on patterns, trends, and connections that are inobvious when viewed individually.”
Commenter, r/PKMS
Their story captures a powerful truth: PKM is not just about getting more done. It’s about creating a record of thought and experience that can help people reconnect with themselves, even after moments of loss or burnout.
For neurodivergent users especially, PKM can become a quiet companion that holds their thinking, creativity, and emotions in one safe place, ready to be revisited when the world feels too fast.
Not everyone finds PKM transformative. Common frustrations appear again and again.
Overbuilding the system. People often spend more time designing their setup than using it.
Information clutter. PKM systems can become digital “idea hoards” filled with half-finished notes.
Maintenance fatigue. Even the best system can feel heavy during periods of low focus or burnout.
Some creators have gone further, questioning the entire idea of personal knowledge management itself.
Watch: Bas Grolleman’s “Personal Knowledge Management is Stupid” on the Tools on Tech YouTube channel explores why overbuilding systems can backfire.
In the video, Grolleman explains that many people spend hours maintaining “perfect little notes for our own satisfaction,” only to find that when they actually need information, those notes rarely deliver.
Grolleman compares note-taking to advertising:
“Half the money I spend on advertising is wasted, and the trouble is, I don’t know which half. Note-taking often feels the same way. Half the notes you make are wasted, but you don’t know which half.”
He suggests that the obsession with capturing everything leads to diminishing returns:
“We end up creating a vast desert of knowledge with occasional rocks of insight. But because we’re so busy exploring that desert, we don’t have the time to look up and see the mountain in the distance.”
His argument is not against taking notes, but against doing it mindlessly. His conclusion is simple:
“You don’t need a copy of the internet. What you need are your personal thoughts and ideas, and fewer of them.”
That critique mirrors what many neurodivergent users describe when their PKM systems become too complex. What begins as a helpful structure can quickly turn into a source of pressure. The emotional weight of keeping up can outweigh the value of the system itself.
The answer is not to give up on PKM, but to simplify it. A sustainable system should be light, flexible, and forgiving. It should help you think clearly without demanding constant attention.
When PKM is viewed in this way, as a tool for reflection rather than perfection, it becomes a genuine companion to real life instead of another layer of work.
The growing conversation about neurodiversity at work is shifting from accommodation to design. The goal is no longer to make space for different ways of thinking, but to build systems flexible enough to support them all.
PKM embodies that mindset. It does not exist to fix attention, focus, or memory. It exists to shape the environment around them. When people can externalize their thoughts, create flexible structure, and see patterns emerge, they often find themselves working with greater clarity and confidence.
In this way, PKM becomes less about productivity and more about cognitive accessibility. It helps reduce friction so that energy goes toward insight, not self-management.
That same principle is shaping the rapid adoption of AI note-taking tools. The global market for AI note-taking tools is projected to grow from USD 535.9 million in 2024 to over USD 2.5 billion by 2033, reflecting the demand for smarter, more intuitive ways to manage information.
![\"A](\"https://framerusercontent.com/images/bET2irrUqjv5ot8nfS2hXAmpGE.png\")
This growth signals a broader shift in knowledge work. People are no longer seeking to capture everything but to design tools that lighten mental load, preserve focus, and turn attention into action.
Tools like Supernormal share that same philosophy. Rather than replacing human intelligence, they create space for it to thrive, transforming ideas and meetings into tangible progress.
One neurodivergent professional in the customer success space described it this way:
“It really is my catch-all for helping things not fall through the cracks. I can be chaotic and doing a lot at once, but Supernormal keeps me on task. I can review my calls and to-dos, and catch if I never hit send on an email.”
That experience captures what cognitive accessibility truly feels like — not control, but confidence.
The future of work will not belong to those who collect the most information, but to those who create systems that make information manageable.
The future of work will not belong to those who collect the most information, but to those who create systems that make information manageable and usable.
PKM is most powerful when it feels personal and forgiving. The goal is not to build the most sophisticated setup, but the one you will actually use when your brain feels busy or tired.
Many people find success by following simple, structured frameworks like Tiago Forte’s PARA Method, or by building their own PKM around the way they naturally think. PARA stands for Projects, Areas, Resources, and Archives, four categories that capture every piece of information in your life. The method’s strength lies in its simplicity: instead of creating endless folders or tags, you organize information around what you are actively working on.
That focus on actionability makes PARA particularly valuable for neurodivergent users. It turns organization into a series of small, achievable decisions rather than an abstract, ongoing task.
Here are a few principles and some tools that can help you start small and grow over time.
Capture thoughts in one place. Complexity can come later.
If you are new to PKM, a simple note app like Apple Notes or Google Keep is enough to begin. Focus on capturing ideas as they happen, not on tagging or linking. The first habit to build is getting things out of your head.
When you have focus, link ideas or summarize key takeaways.
Tools like the Supernormal App can do that automatically after meetings, turning notes into action items or updates. If you want to go deeper into knowledge linking, apps like Obsidian offer networked thinking, perfect for visual or associative minds.
A short weekly review can reveal patterns and priorities.
Some people do this with a paper notebook or Notion dashboard; others prefer automation. Tools such as Mem or Reflect can resurface past notes automatically, helping you spot trends in your thinking without manual work.
Whatever tools you choose, remember that you set the rules. PKM should serve your mind, not manage it.
Some professionals use a structured process to make that happen. One self-described neurospicy knowledge worker explains it as a three-part cycle: curation, note-making, and utilisation.
“All my actions are mapped with actions to consequences, so if I read something it should be worth my time. Then I follow this process.
Curation — the things I read and find interesting.
Note-making — my own thoughts on what I’ve read.
Utilisation — how I use it to improve my work and creativity.”
They use a mix of tools to support that flow: Sublime for curation, Logseq for notes, and Msty to put those ideas into practice. The specifics will vary for everyone, but the principle holds: the most effective systems are the ones that help you use what you know, not just store it.
Visual notes can also play a powerful role. Some people add images, screenshots, or references directly into their PKM tools to spark memory or bring abstract ideas to life. For example, a Notion page might combine meeting takeaways, creative prompts, and visual assets in one place to help connect thoughts and next steps.
![\"Screenshot](\"https://framerusercontent.com/images/4rMPtSFGbmV5v96QLNePQU0lzxk.webp\")
When your system is light enough to bend but strong enough to hold your thoughts steady, it becomes more than a productivity framework. It becomes a personal record of how you think, learn, and grow.
Neurodivergent professionals do not need fixing. They need tools and systems that help their natural strengths thrive.
Personal knowledge management is one of those tools. It turns fleeting thoughts into steady progress, helps ideas stay visible, and gives structure without pressure.
For many, PKM is more than a productivity method. It is a form of self-support, a way to feel grounded, confident, and capable in the chaos of modern work. When people have the right systems, they do not just manage information; they unlock their full potential.
", "url": "https://www.supernormal.com/blog/personal-knowledge-management-and-neurodivergent-minds", "title": "Is pkm the secret weapon for neurodivergent minds at work?", "summary": "Personal knowledge management brings order and confidence to chaotic workdays. Discover why neurodivergent professionals are embracing PKM systems.", "date_modified": "2025-10-14T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "Productivity" ] }, { "id": "urn:sha256:a8bfc834f9c5ba036e6ab0ab98fd3da721105efd98d09b5484f4d76ca6bb7540", "content_html": "Top performers don’t just work harder, they use the right tools to go further, faster. AI has moved beyond novelty into necessity, reshaping how ambitious professionals manage their calendars, capture meetings, and execute follow-ups. But with so many apps competing for attention, the real challenge is knowing which ones actually deliver results.
This isn’t another catch-all list. Instead, we’ve evaluated the most talked-about AI productivity tools and categorized them according to the job they do best. Each tool solves a specific pain point, whether that’s keeping your schedule sane, writing faster, or turning meetings into finished tasks. By the end, you’ll know which tools deserve a place in your stack and which problems they’ll actually solve for you.
Tool | Best For | Ideal User |
Motion | Scheduling and task automation | Teams, solopreneurs, and agencies juggling complex workflows |
Focus Bear | Eliminating distractions and supporting deep work | Professionals and students who struggle with constant distractions |
Arc Browser (+ Dia) | Space and tab organization, creative flow | Knowledge workers and creatives who live in their browser |
Supernormal | Turning meetings into action and automatically completing your to-dos | Agencies, product, sales, and operations pros who want meetings to generate completed work |
Lex | AI-native writing and editing | Writers, students, and professionals needing fast drafting and editing |
Notion | All-in-one productivity and knowledge management hub | Teams and individuals centralizing docs, tasks, and projects |
Motion started out solving a simple productivity killer: hours lost to constant rescheduling. Now, it’s expanded to become the world’s first agentic work suite, helping individuals and teams reclaim even more time by blending human task management with AI execution. Inside Motion, you can spin up “AI employees”, virtual teammates that act like an executive assistant, sales rep, marketer, recruiter, or project manager. These AI employees handle the repetitive but essential work that slows you down: scheduling, drafting emails, chasing leads, creating reports, or keeping projects on track.
For busy employees and managers, the promise is straightforward: less time lost to admin, more time focused on high-value work. Motion doesn’t just organize your day; it actively moves work forward so you don’t have to. Of course, the trade-off is complexity. AI employees still need oversight, and smaller teams may find the setup and pricing heavy compared to lighter tools. But for professionals drowning in repetitive tasks, Motion offers a way to scale your output without stretching your hours.
Pros
Combines human and AI task management in one platform
Offloads repetitive admin like scheduling, CRM updates, and reporting
Helps employees stay focused on meaningful work
Cons
Setup and oversight can feel complex at first
Pricing better suited to teams than solo users
AI outputs still need review for accuracy
Reviews
⭐ “I never get caught in a position where I don't know what I could be working on. Motion always has all my tasks prioritized.” - Nick G, G2⚠️ “There is a bit of a learning curve on Motion. I am really committed to making this work, because I need it to...but for others who may not be, the learning curve may be a bit of a derailer. ” - Verified User, G2
Ideal users
Busy teams, solopreneurs with complex workflows, agencies, and productivity enthusiasts seeking a next-gen AI assistant.
Focus Bear is designed for one of the hardest productivity challenges: actually staying focused. For employees battling constant notifications, endless context switching, or the temptation of distracting apps, Focus Bear enforces structure so you can get deep work done. It blocks apps and websites during work sessions, encourages healthy breaks with movement reminders, and helps you build consistent work habits.
The productivity payoff is clear: less time wasted, more hours of actual concentration. While it can feel overly strict if you don’t customize the rules, the app is flexible enough to adapt to your workflow. By making it harder to slip into distraction and easier to stay locked into important tasks, Focus Bear helps employees produce more in less time.
Pros
Blocks distracting apps and sites automatically
Encourages healthy breaks for sustainable productivity
Helps build focus habits over time
Cons
Strict defaults can frustrate casual users
Customization needed to avoid “over-blocking”
Less useful for collaborative work sessions
Reviews
⭐ “It's an amazing app specifically for people who want to have complete focus on things while working, taking regular breaks, blocking distractions, and managing the day routine effectively! Addons like the time tracker helps analyze productivity.” – Manish Jain, Product Hunt
⚠️ “...at first app was confusing and for person like me with busy schedule and mind all over the place it was hard to set up…” – Maria Alvarez, Product hunt
Ideal users
Professionals and students who struggle with distractions and want to carve out real focus time.
Arc isn’t just a browser, it’s a productivity platform built around how modern professionals actually work online. Developed by The Browser Company, Arc replaces the clutter of traditional browsers with a clean, intentional interface that organizes your tabs into spaces, lets you split screens, and even embeds mini-apps directly in the sidebar. For employees who spend hours a day in the browser, Arc reduces cognitive overload and makes multitasking feel structured instead of chaotic.
![\"\"](\"https://framerusercontent.com/images/ze7wj0CejU1jNDlEDjNRfyzcFdU.webp\")
One of Arc’s strongest productivity benefits is how it eliminates friction. Need to write, plan, or research? You can pin your workflows into spaces, keep projects separated, and use features like “Peek” to open links without losing your place. The result is less tab chaos and more focus on the task at hand. For creative professionals and knowledge workers, Arc becomes less of a browser and more of a digital workbench.
Arc’s innovation doesn’t stop there. The team recently introduced Dia, an AI-powered browser currently in early access. Dia takes Arc’s philosophy further by embedding AI directly into every text box and tab, acting as an inline writing assistant, a tutor for learning, or a planner that formats insights from your browsing context. While Dia is still developing, it signals where browsers are heading: productivity tools that don’t just hold the web, but actively help you work within it.
Until recently, Arc was Mac-only, but as of 2025 Arc for Windows is available for the general public. You can download it safely from arc.net/download or directly from the Microsoft Store, making it accessible to millions more users.
Pros
Organizes tabs and projects into dedicated spaces
Clean, distraction-reducing interface
Built-in features (split view, mini apps, Peek) streamline workflow
Now available on both Mac and Windows
Forward-looking with Dia: AI support inside the browser itself
Cons
Learning curve if you’re used to traditional browsers
Some workflows still Chrome-dependent for extensions
Dia features are still in early access
Reviews
⭐ “The continuous updates show the team's commitment to excellence. It's more than a browser; it's a revolution in productivity and organization...” - Abdul Yousef, Product Hunt
⚠️ “It's a shame it's no longer under active development, but I'm intrigued to see what The Browser Company has in store with Dia (which I hear is getting side tabs soon)!” – Daniel Croome-Horgan, Product Hunt
Ideal users
Knowledge workers, creatives, and students who spend most of their day in the browser and want it to actively support their productivity.
Superormal takes a different approach to productivity than most meeting tools: instead of just documenting conversations, it helps you finish the work that comes out of them. Running quietly in the background, the desktop app detects when you’re in a meeting, whether it’s a Zoom call, a Google Meet, or even an unscheduled Slack Huddle, and automatically captures the discussion. It doesn’t “record” audio for playback, which can feel intrusive, but rather documents what’s said and organizes it into summaries, tasks, and updates.
From there, Supernormal suggests next steps inside the app and connects directly to your tools so you can move the work it has has done for you straight to Gmail, Slack, Notion, or wherever else. If you need to send an email, update Linear, or share a Slack message, Supernormal drafts it with you, lets you review, and then pushes it to the right tool with a single click. For individuals in product, sales, or operations, this means meetings stop being a source of backlog work and start to become a driver of completed work.
The trade-offs: The ecosystem of integrations is growing but not yet complete. But for anyone who wants meetings to actually move work forward, it's a game-changer. The Supernormal Desktop App is free to try.
Pros
Detects and documents meetings automatically, even unscheduled ones
Generates summaries and suggested next steps instantly
Collaborates with you to draft emails, updates, and tasks before pushing them into your tools
Cons
Best suited to teams already using supported tools like the Google Suite, Slack, or Linear
Reviews
⭐ “I use AI to optimize all my work. I've used many AI agents, and Supernormal is my favourite AI agent for meeting summaries, notes, and tasks.” - Patrick McGuire, beta user and CEO at Empowered
⚠️ “Loving Supernormal so far. But one thing I found tough today was trying to find the notes I took in a meeting. Once I used the ‘improve notes’ feature, I can no longer find my raw notes consistently.” – Megan Beardsley, Supernormal user feedback
Ideal users
Busy professionals in product, sales, marketing, and operations who want their meetings to directly generate completed work, not just notes.
Lex is a new kind of word processor designed from the ground up with AI built in. Unlike add-ons that bolt AI onto existing tools, Lex is a distraction-free writing space where AI is part of the flow: it can suggest completions as you type, rephrase awkward sentences, or generate entire paragraphs when you’re stuck. For professionals and students who write daily, this means less time fighting writer’s block and more time producing polished drafts.
Where Lex shines is speed. Instead of hopping between ChatGPT and your doc editor, you can stay in one place and get suggestions in context. It’s lightweight, fast, and focused; a tool built for people who want to write, not manage complex workspaces. The trade-off is that it doesn’t yet have the deep formatting or collaboration features of Google Docs or Notion. But for individuals and small teams who care about clarity and output, Lex offers a refreshing, AI-first writing experience.
Pros
AI suggestions built directly into the writing workflow
Clean, distraction-free interface
Excellent for overcoming writer’s block and drafting quickly
Cons
Limited formatting and collaboration features
Still evolving compared to legacy word processors
Best for individuals, not large enterprise teams
Reviews
⭐ “I think where Lex helps is kicking one past \"sticky\" points as a writer, not really doing the bulk of the work. It's a \"light\" product, so not a lot of friction when you use it.” – Mary Pat Cambell, Product Hunt
⚠️ “One shortfall is that Lex doesn't automatically replace your text with the AI-generated version. Instead, rewritten text appears in a floating comments box.” – Khamosh Pathak, PC Mag
Ideal users
Writers, students, and professionals who want a lightweight, AI-native tool to draft and edit text faster.
Notion has established itself as the go-to workspace for teams who want to bring docs, wikis, tasks, and projects under one roof. For employees, that means less time lost to app switching and more time focused on actual work. Instead of bouncing between a project management tool, a knowledge base, and a writing app, Notion brings it all together in a single, flexible interface. Teams use it to capture meeting notes, build project dashboards, or draft documents, and then layer on automations and integrations to keep everything connected.
Notion has recently leaned into AI as an optional upgrade, helping users summarize content, draft text, and auto-fill databases. While not everyone finds the AI features indispensable, the broader platform remains a powerful productivity driver because it reduces complexity, especially for small to midsize teams that don’t want to manage half a dozen separate tools. The trade-off is that poorly organized Notion workspaces can quickly become hard to navigate, and scaling it across large enterprises requires careful setup. Still, for individuals and teams that value flexibility, Notion is one of the most versatile productivity platforms available, and now it's available offline.
Pros
Combines docs, tasks, wikis, and projects in one place
Reduces app sprawl and context switching
Highly customizable with templates and integrations
Cons
Steeper learning curve for new users
Can become messy without clear structure
Scaling for large enterprises requires admin oversight
Reviews
⭐ “Notion is a true productivity powerhouse. Its intuitive database management capabilities make organizing information effortless, while its flexibility allows teams and individuals to customize workflows to their exact needs.” - Justin H, G2
⚠️ “Sometimes it feels a bit slow when pages are too heavy or full of databases. Also, if you are new, it can look overwhelming at the start because it has so many features. But once you get used to it, it becomes much easier and worth the learning curve.” - Martha Maria D, G2
Ideal users
Teams and individuals looking to centralize documentation, project management, and knowledge sharing in one flexible platform.
The best AI tools don’t just save time, they make sure the time you do spend moves the needle. Each tool in this list is designed to tackle a specific productivity challenge:
Motion keeps your schedule and tasks under control so you’re never lost in calendar chaos. Learn more about how Motion and Supernormal compare as AI Meeting Assistants.
Focus Bear eliminates distractions so you can finally get deep work done.
Arc Browser organizes your digital workspace and, with Dia, brings AI directly into your browsing flow.
Supernormal ensures meetings don’t just create notes, but generate the follow-ups and tasks that keep work moving.
Lex helps you write faster and with more clarity, turning drafts into finished text in less time.
Notion centralizes your projects, documents, and knowledge, reducing app sprawl and context switching.
Together, these tools form a well-rounded productivity stack covering planning, focus, collaboration, communication, and knowledge management. The right mix depends on where your bottlenecks are, but each one is proven to make a measurable difference in how work gets done.
", "url": "https://www.supernormal.com/blog/best-ai-tools-productivity", "title": "Best AI tools for productivity 2025: Smarter work, faster", "summary": "Cut distractions, manage your calendar, and work faster with AI tools that solve real productivity pain points. Get more done in less time.", "date_modified": "2025-09-18T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "AI and agents" ] }, { "id": "urn:sha256:abbe2ed12222247d71652a2cd6a04a8249fe3b66c5c42f68863019e1650c2f1d", "content_html": "Working memory is your brain's ability to hold and manipulate information in the moment. It allows you to remember the first half of a sentence while reading the second, keep track of next steps in a meeting, and juggle multiple tasks without losing focus.
Unlike long-term memory, working memory is limited and highly sensitive to distractions. Think of it as your mental \"RAM.\" It is essential for decision-making, problem-solving, and productivity at work.
\"...Life comes at us, and it comes at us very quickly, and what we need to do is to take that amorphous flow of experience and somehow extract meaning from it with a working memory that's about the size of a pea.\" - Educational Psychologist, Peter Doolittle
Strong working memory helps you:
Follow complex conversations without missing details
Prioritize tasks and avoid dropping action items
Stay focused when switching between projects
Turn meetings into clear, actionable next steps
For example, think about a sprint planning session where tasks are flying around the room. Strong working memory helps you keep track of what was just discussed while anticipating the next point. Or consider a client call where expectations are laid out quickly. It makes the difference between leaving with a clear list of deliverables versus trying to reconstruct the details from memory later.
When working memory is low, you may notice:
Forgetting details right after hearing them
Losing track of tasks midway through
Struggling to prioritize or organize information
Several factors influence how well your working memory functions:
Stress and overload: Too much input can overwhelm your mental bandwidth. When three Slack threads, a Linear update, and a Zoom call all hit at once, your brain struggles to decide what deserves focus.
Sleep and rest: A tired brain struggles to hold onto information. Late-night work or skipping breaks can reduce recall the next day.
Environment: Constant interruptions reduce your ability to focus. If you are trying to absorb a design review while monitoring email pings, your verbal memory is already working at a disadvantage.
Tools and systems: Relying on memory alone to manage tasks adds unnecessary strain. Without support, important details from a meeting or brainstorm are easy to lose.
Researchers often explain these challenges through Baddeley's Model of Working Memory, one of the most widely used frameworks. It describes four components that work together:
Central Executive: Directs attention and focus. Stress or heavy multitasking can overload this system, making it harder to prioritize.
Phonological Loop: Handles verbal and auditory information. Distractions during a conversation reduce its efficiency.
Visuospatial Sketchpad: Manages visual and spatial data. Fatigue makes it harder to process diagrams, designs, or layouts.
Episodic Buffer: Integrates information from different inputs and links it to long-term memory. Overload here weakens your ability to connect new ideas with what you already know.
The good news: working memory is not fixed. You can strengthen it with the right habits and systems.
You have probably heard this before: write things down instead of juggling them in your head. Offloading information frees up mental space for deeper thinking. With apps like Supernormal, action items from your calls are captured and organized automatically, so you can focus on the conversation itself.
Use tools that automatically capture and organize meeting notes, tasks, and follow-ups. This prevents important details from slipping through. For example, instead of trying to remember who owned a task from your weekly standup, you can review the notes Supernormal captured and move directly to execution.
This approach is part of what many call personal knowledge management: creating a reliable system outside your brain to store, organize, and retrieve important information. A good PKM system strengthens your working memory by making sure you always know where to find details, projects, and ideas when you need them.
Exercises like learning a new language, playing memory-based games, or practicing mental math can sharpen your working memory over time. Even structured brainstorming with teammates can provide a workout, as you actively hold and connect multiple ideas at once.
Limit distractions, block focus time, and keep your workspace clear. Clear desk, clear mind. The same applies digitally: shutting down unused tabs and muting Slack during deep work is the online version of tidying your desk.
Adequate sleep, regular breaks, and short walks improve recall and focus. A well-rested brain holds onto information longer. Even a quick reset between meetings can make it easier to recall the discussion later.
Repeat new information out loud to reinforce recall
Sketch a quick diagram of what you just heard
Pause after a meeting to write down two or three key takeaways
Summarize a conversation to yourself in one sentence
Challenge yourself to remember a short list backward and forward
You do not need to rely solely on your brain to remember everything. By understanding what working memory is, recognizing what impacts it, and adopting strategies for how to improve working memory, you set yourself up for sharper thinking and better execution.
Professionals are increasingly relying on external systems to extend their memory. This concept, sometimes called \"cognitive offloading,\" allows you to free up mental space for higher-level thinking.
Tools like Supernormal not only capture your meetings but also organize your knowledge, moving toward a future where your working memory and your digital memory work seamlessly together.
", "url": "https://www.supernormal.com/blog/working-memory", "title": "Working memory: How it works and how to improve it", "summary": "Discover what working memory is and practical ways to improve it. Boost focus, productivity, and performance at work with proven strategies.", "date_modified": "2025-09-10T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "Productivity" ] }, { "id": "urn:sha256:f90089aae85b2539aed87d9c6e590971ce51d5d9fc5d71f530659866783c8a0f", "content_html": "Workflow automation is the use of technology to perform repetitive tasks or processes with minimal human input. Instead of manually moving information from one tool to another, sending reminders, or tracking tasks, automation handles these steps for you. The result is more efficiency, fewer errors, and more time to focus on meaningful work.
In practical terms, workflow automation means things like:
Automatically routing incoming support tickets to the right team
Generating follow-up emails after a meeting without writing them from scratch
Updating a project board when a task is completed
By reducing manual effort, automation helps teams save time and create more consistent results.
At its core, workflow automation follows a simple formula: trigger → action → outcome.
Trigger: An event happens, such as receiving an email or marking a task complete.
Action: An automated process runs, like sending a message in Slack or updating a record in your project tool.
Outcome: The workflow moves forward without manual effort.
Modern automation often uses AI to make these workflows smarter. For example, instead of just sending a generic email after a call, an AI system like Supernormal can generate a personalized follow-up that reflects the actual meeting content.
The benefits of workflow automation go beyond saving a few clicks. McKinsey research shows that employees are far more ready for AI than leaders expect. Many already use it regularly, and they are three times more likely than leaders realize to believe that AI will replace 30 percent of their work in the next year.
In another study, 80% of workers reported that automation improved both their productivity and efficiency. Beyond output, it reduces stress and improves working memory by removing tedious, error-prone work.
Real-world example: A product team that used to spend hours summarizing meetings and creating tickets can now have the Supernormal App automatically generate follow-ups in Linear. Instead of slogging through repetitive admin, they are free to focus on shipping features faster.
If you are new to automation, the key is to start simple and build over time. Here’s a practical roadmap:
Identify repetitive tasks
Look for tasks that eat up time but do not require unique judgment. Examples: sending reminders, updating spreadsheets, or moving information between tools.
Select appropriate tools
Choose workflow automation platforms that fit your needs. For simple automations, tools like Zapier or Make may be enough. For more advanced AI-driven workflows, explore tools like Supernormal App that integrate directly with your daily apps.
Map out workflows
Visualize the current process step by step. Identify where information gets stuck or where people repeat manual steps.
Implement automation
Start with one workflow and set up automation rules. For example, “When a client meeting ends, capture notes and create follow-up tasks in Slack and Linear.”
Monitor and optimize
Automation is not set-and-forget. Review regularly to refine triggers, remove unnecessary steps, and add new ones as your needs evolve.
Workflow automation looks different depending on your role. Here’s how it creates impact across common teams, the pain points it solves, and the tools that support it:
Role / Use Case | Pain Points | Automation in Action | Relevant Tools |
Sales Teams | Too much time on admin (logging notes, updating CRM, chasing follow-ups). Sales reps spend just 28% of their time actually selling【Salesforce†source】. | Call ends → notes logged, follow-ups drafted, CRM updated instantly. | Salesforce AI, HubSpot, Supernormal App |
Product Teams | Tasks lost between meetings and execution. Misalignment delays sprints. | Meeting outcomes captured → tickets created in Linear/Jira → team leaves aligned. | Linear, Jira, Supernormal |
Operations Teams | Approvals stall in email, slowing projects and frustrating staff. | Approvals routed automatically → reminders sent → cycles reduced from days to hours. | Process.st, Greenhouse, Supernormal |
Marketing Teams | Repetitive campaign tasks like email sends, social posts, and lead routing. | Campaign workflows automated → leads scored and routed instantly. | HubSpot, Marketo, Zapier |
Freelancers | Juggling invoicing, client communication, and admin eats into billable hours. | Invoices, reminders, and scheduling handled automatically. | Aeon, Bonsai, Supernormal |
Developers | Manual testing and deployment create bottlenecks. | CI/CD pipelines run automatically → code tested and deployed faster. | GitHub Actions, Jenkins |
HR & Recruitment | Screening, onboarding, and approvals slow down hiring. | Automated candidate screening, onboarding workflows, and approvals. | Workable, Greenhouse, Supernormal |
Executive Assistants | Following up on meetings, managing calendars, tracking requests. | Meeting captured → summary created → follow-ups and reminders generated. | Supernormal App, Notion AI |
According to Gartner, by 2025, 70% of organizations have already implemented structured automation to support productivity.
If you are curious about how workflow automation looks in practice, Supernormal makes it simple. Here’s how you can experience automation from the moment you install the app:
Download and install
Download the Supernormal Desktop App to start capturing your work context. Getting started takes only a few minutes, with no complicated setup or integrations required.
Automatic meeting detection
It detects meetings automatically through your system audio. You do not need to connect Zoom, Meet, or other platforms manually. The app quietly recognizes when a meeting starts so you never miss important details.
Capture and summarize
While you stay focused on the discussion, the app records your meetings unobtrusively in the background (no bots required). It then produces a clear, concise summary and suggests next steps based on the actual conversation. Instead of juggling notes, you leave with an organized record you can trust.
Execute next steps
Follow-up no longer requires bouncing between apps. From Supernormal, you can draft Slack messages, create tickets in Linear, or send quick updates. With next steps captured and execution options built in, projects keep moving without friction.
Stay unobtrusive
Supernormal is designed to work in the background. You get the benefits of workflow automation, capturing, summarizing, and executing, without changing the way you run meetings.
With Supernormal, workflow automation is not about setting up dozens of rules, writing prompts, or coding custom scripts. It is about removing the manual burden of capturing, remembering, and acting on meeting outcomes. This is one of the highest-friction areas of modern work, and automating it creates immediate value for busy professionals.
![\"Screenshot](\"https://framerusercontent.com/images/Ce3uNbZ544xvMoINF7YZh6u3ho.png\")
Supernormal Notetaker not only suggests next steps, but can do them for you too.
Workflow automation has shifted from a “nice to have” to a core driver of productivity. In 2025, it is no longer about experimenting with small automations but about building systems that help entire teams think and act faster. McKinsey research shows employees are often more ready for AI than leaders realize, but with 41 percent still apprehensive, the challenge now is guiding adoption with trust, not just technology.
Automation will not remove the human element of work. Instead, it removes the friction: the repetitive, administative tasks that drain focus and momentum. For sales teams, it means more time with customers. For product managers, it means fewer misaligned sprints. For executive assistants, it means less chasing and more leading.
“The future of workflow automation is not bots spamming your tools. It is AI running quietly in the background so you can focus on decisions and creativity.”
— Fabian Perez, Co-founder of Supernormal
Supernormal embodies that vision. It captures your meetings, generates next steps, and integrates them into the tools you already use — all while staying unobtrusive. By working alongside you instead of in front of you, Supernormal helps you focus on the work only humans can do: making decisions, building relationships, and driving ideas forward.
Automation is not about replacing people. It is about giving people the space to do their best work.
", "url": "https://www.supernormal.com/blog/workflow-automation", "title": "Workflow automation in 2026: What it is and how to start ", "summary": "Learn what workflow automation is, how it works, and how to start. Discover benefits, real-world examples, and AI tools that automate your work in 2026.", "date_modified": "2025-08-27T08:59:20.090Z", "author": { "name": "laura-james" }, "tags": [ "AI and agents" ] }, { "id": "urn:sha256:8f24d13b25deda888dc2bf839871930a26cbb0c71e9fdfe96adf958574e4f641", "content_html": "Whether you're producing content, running interviews, or managing remote teams, AI transcription tools have become essential. They turn spoken words into searchable, editable text, saving you hours of manual note-taking and making your work more accessible, shareable, and insightful.
In this guide, we evaluated six leading tools based on accuracy, speed, collaboration features, language support, and pricing transparency.
When creating this list, we focused on practical, real-world utility. Our goal is to help you find the best AI transcription tools for your needs, whether you’re looking for full-video recordings, meeting insights, or the ability to autoshare meeting summaries. Here’s how we made our selections:
We analyzed third-party ratings and reviews
We looked at reviews from trusted sources like G2 and Trustpilot to understand what real users value (and critique) about each tool. This helped us spot common pain points and standout strengths.
We highlight each tool’s best use case, not just who’s “#1”
Instead of ranking tools from first to last, we chose to spotlight what each product does best. Some are great for teams with a lot of regional accents, while others shine at real-time collaboration or lightning fast call summaries. We believe the best tool depends on you and what you need it for.
By combining user feedback, real-world experience, and tailored evaluation, we’ve curated a list that helps you make an informed choice faster.
Let’s dive into the tools.
Founded by a journalist, Trint emphasizes speed, accuracy, and collaboration, making it especially popular among media outlets, podcasters, and content creators. The platform offers multilingual transcription, real-time collaboration tools, and advanced editing features, all accessible via a user-friendly web interface and mobile app. Trint’s standout story-building tools enable users to pull quotes and assemble narratives directly from transcripts, streamlining the content creation process for fast-paced environments.
Key Features:
Converts audio and video files to text in over 40 languages, with claimed accuracy rates up to 99% for clear audio
Assemble quotes and segments from multiple transcripts to create articles, scripts, or podcasts
Multiple users can highlight, comment, and edit transcripts simultaneously, with granular access controls and shared drives
Pros:
Excellent for teams needing fast turnaround and collaboration
Built-in storytelling tools for podcasting or newsrooms
Intuitive design, easy file uploads, and compatibility with all major browsers
Cons:
More expensive than many competitors, especially for individuals or casual users; no permanent free tier
“Unlimited” plans have vague usage caps, which may frustrate high-volume users
Direct integration with Zoom is available, but not with Microsoft Teams or Google Meet
Pricing:
Starter Plan: €48 per seat/month (billed annually at €576). Includes up to 7 audio or video files and 3 translations per month, plus collaboration with 2 teammates.
Advanced Plan: €52 per seat/month (billed annually at €624). Offers unlimited transcriptions, AI-powered summaries, shared drives, subtitling, and 1 hour/month of mobile live transcription.
Enterprise Plan: Custom pricing. Adds live transcription from any device, automatic language recognition, advanced security, and team-wide collaboration tools.
Free trial available; no ongoing free plan.
What users say about Trint:
\"The transcription process is very robust. Customer support is fantastic, and I use Trint with every project involving a customer interview. Marking up videos for production could not be easier!\" – Verified User., G2
\"Bottomline: if you're looking for a transcription app: there's others which do the job as well but cheaper. If you're looking for an app that helps you create an EDL or XML for your editor, keep looking.\" – FE., Trustpilot
Descript is built for creators who want to produce, polish, and publish content without switching tools. Its standout feature is the ability to edit video and audio simply by editing the transcript, turning media editing into a word processor-style experience. Whether you’re cutting out filler words, rearranging clips, or using AI voice to fix mistakes, Descript makes the workflow seamless.
Key Features:
Automatic transcription with up to 95% accuracy for well-recorded content, delivering near-instant results for audio and video files up to 15 hours long
AI-powered “Speaker Detective” identifies and labels multiple speakers, with assistance for naming each participant and filler-word detection
Overdub (AI voice cloning) for script changes
Supports transcription of synchronized recordings with speakers on separate tracks for enhanced accuracy
Create personalized vocabularies to improve transcription accuracy for industry-specific terms, names, or frequently used phrases
Pros:
All-in-one platform for podcasters and creators
Multiple format options including plain text, rich text, markdown, HTML, Word docs, and subtitle formats (SRT/VTT)
Makes audio and video editing accessible to beginners by treating transcripts like documents, eliminating the need for complex timeline manipulation
Automatic identification and bulk removal of filler words like “um,” “uh,” “like,” and “you know
Cons:
Only 1 hour of transcription per month on the free plan, with significant limitations compared to competitor
Struggles with poor audio quality, heavy accents, background noise, and overlapping speech, requiring manual corrections
Pricing becomes expensive for teams with high transcription needs, and additional features require higher-tier plans
Pricing:
Free: $0 per month
Hobbyist: $24/month (or $192 annually, equivalent to $16/month)
Creator: $35/month (or $288 annually, equivalent to $24/month)
Business: $50/month (or $480 annually, equivalent to $40/month)
Additional Options:
White Glove Human Transcription: $2 per minute (up to 2-hour limit per file)
Extra Transcription Hours: $2 per additional hour beyond plan limits
Student/Educator Discount: Available through special application process
Basic Seats: Free collaboration seats for viewing and commenting (Business plan and above)
\"I used the free version to transcribe a few videos and I think it would be nice to leave at least a positive comment. Descript did what it had to, it lets you neatly organize stuff, has tons of features and pretty nice UI.\" - Jiri Fiala., Trustpilot
\"The transcription is not very accurate (in my estimation it’s about 85% accurate). It was so time-consuming to correct the transcripts that I gave up after a year. I thought by carefully correcting the transcripts, I would be helping the machine learning get better and better over time. But it doesn’t make any difference at all. So, then I switched to producing a raw transcript in PDF format for my listeners, but putting a warning page saying that it contains lots of errors or omissions.” - SSP., Trustpilot
Sonix is a fast, browser-based transcription platform designed for teams working in multiple languages. With support for over 40 languages, advanced subtitle exports, and AI features like sentiment analysis and topic detection, Sonix is ideal for global teams who need transcription that goes beyond the basics. Its powerful in-browser editor allows users to review, edit, and share transcripts easily, while integrations with tools like Zoom, Google Drive, and Adobe Premiere streamline content workflows.
Key Features:
AI-powered transcription with up to 99% accuracy in 50+ languages
In-browser editor with audio-text sync and word-level playback
Speaker identification with custom labeling
Sentiment analysis, topic and entity detection, and auto-summarization
Subtitles and captions with formatting options and burn-in support
Real-time collaboration, commenting, and sharing with permission controls
Custom dictionaries to improve accuracy on names and industry terms
Pros:
Great for global teams and international content
Delivers up to 99% accuracy for clear audio and can transcribe a 30-minute file in approximately 3-4 minutes
Clean, intuitive browser-based editor that combines the familiarity of a word processor with advanced audio-syncing capabilities
Cons:
No real-time transcription capabilities for live meetings (only available through integrations), requiring files to be uploaded post-recording
Pricing is higher than many competitors, particularly for individuals or casual users, with no permanent free tier
Some users report slow response times and generic troubleshooting advice, with no phone support available
The hybrid pricing model (subscription + usage fees) can be confusing and costs can escalate quickly for high-volume users
Pricing:
Free trial available, 30 minutes of free transcription only
Standard Plan (Pay-as-you-go): $10 per transcription hour, single-user access
Premium Subscription: $22 per user per month (or $16.50 annually), $5 per transcription hour (50% savings), $3 per translation hour (70% savings)
Enterprise Plan: Custom pricing quote, 5+ user minimum
Additional services:
AI Translation: $10 per hour (Standard), $3 per hour (Premium)
Specialized Legal Plans: Starting at $3,500 annually for basic legal transcription, $6,500 annually for AI analysis features
Volume Discounts: Available for companies requiring 100+ hours per month
\"I was really impressed with how easy Sonix was to use. It handled different audio formats with no issues, and the transcription was surprisingly accurate. The whole process was super straightforward, no learning curve at all. I’ll definitely be using it again\" – Simon V Muzenda., Trustpilot
\"I signed up for the $22 monthly subscription thinking it included transcriptions. To my surprise, Sonix charges extra for every single transcription on top of the subscription fee. In the end, I paid $50 in one month, even though I had already paid for the membership.\" – Tena Tena., Trustpilot
![\"\"](\"https://framerusercontent.com/images/t13RstyPOdFNcsuEcuT6poeNkY.webp\")
Rev stands out for its dual offering: lightning-fast AI transcription and highly accurate human services. It’s trusted by content teams, legal professionals, and researchers alike - anyone who needs dependable transcripts, whether speed or precision is the priority. The platform is easy to use, with transparent pricing and flexible options for teams of all sizes.
Key Features:
Offers both automated (AI) and human-verified transcription services, allowing users to choose based on their accuracy and budget needs
Human transcription with 99%+ accuracy
Provides captions in over 37 languages and supports FCC/ADA-compliant human captions in English and Spanish
Rush delivery available
Record, upload, and edit audio/video files via browser or mobile app, with real-time syncing between devices
Pros:
Both AI and human services are generally noted for quick delivery times
Web and mobile platforms are intuitive and easy to navigate, making uploads and edits straightforward
Secure, vetted workforce for human transcriptions
Cons:
Human transcription can become expensive for lengthy files or frequent use
Occasional complaints that “human” transcriptions are not always more accurate than AI, especially for nuanced or publishable content
AI-generated summaries may lack structure and depth compared to leading alternatives
Pricing:
AI Transcription: Starts at $0.25 per audio minute. Fast and budget-friendly, though you may need to clean up the results.
Human Transcription: $1.50 per audio minute with speaker IDs and timestamps. Ideal when precision matters.
Captions & Subtitles: $1.50 per video minute for human captions. Foreign subtitles are pricey and begin at $5.00 per minute.
Free Tier: New users can access limited AI minutes for free.
Enterprise & Bulk Discounts: Available for high-volume users.
No Monthly Minimums: Pay-as-you-go flexibility with optional enterprise plans.
What users say about Rev:
\"My team and I also really like the AI features such as summary and main points, which allows my team members without any previous knowledge of the subject matter to understand things very quickly opening the transcript in Rev. I also really love how I can easily share a transcript with one of our clients if we need to. I've also had very positive experiences with Rev's helpful and friendly customer support.\" – Brian U., G2
\"I don't have a lot of complaints-- but I do wish there was like a learning function inside my account-- since I use the same speakers, it could learn their voices/accents and get even more accurate over time.\" – Amanda D., G2
If you want to create subtitles from your transcript, VEED is a great option. VEED is an AI-powered video editing platform that helps creators add subtitles to video automatically while editing content in the same browser-based workflow. It's especially useful for marketers, educators, podcasters, and content creators who want to improve accessibility and engagement without relying on separate captioning software.
Key Features:
AI-generated subtitles with editable captions
Export subtitles in SRT, VTT, and other common formats
Subtitle translation and customizable caption styles
Browser-based video editor with support for social media exports
Pros:
Fast subtitle generation with an easy editing interface
No software installation required
Combines captioning and video editing in one platform
Cons:
Advanced AI features require a paid plan
Processing speed depends on video length and internet connection
Pricing:
Free plan available
Paid plans unlock longer exports, premium AI features, and higher-quality video output
What users say about VEED:
\"For me, the most valuable feature is the automatic subtitle generation. It saves a significant amount of time compared to manually creating captions and helps make videos more accessible and engaging across social media platforms.\" – Verified User, G2
The Supernormal app is a AI meeting assistant and workspace that generates transcripts directly from your device without sending a bot into the call. It’s built for people who want reliable meeting transcription with zero meeting disruption. The app captures the audio from Zoom, Google Meet, Teams calls, and even in-person conversations. It then produces transcripts, and then turns those transcripts into clear summaries and follow-up notes.
Its focus isn’t just accurate transcription. Supernormal is built to help you act on what was said, making it especially valuable for client-facing roles, project leads, and anyone who needs fast follow-through after every call.
Key Features:
Uses system audio to capture conversations and generate transcripts without requiring a meeting bot
Automatically produces meeting summaries, action items, and suggested follow-ups
Generates first-draft emails, updates, and project documentation from your meeting content
Integrates with Slack, Linear, and Cursor for quick handoff into your workflow
Works for Zoom, Google Meet, and Teams calls held on your Mac
Pros:
No bots joining your calls
Excellent for people who want meeting insights and follow-up materials, not just raw transcripts
Fast turnaround between meeting and ready-to-share draft
Helps reduce context switching by pushing output directly where you work
Cons:
Still expanding transcription-focused features compared with longtime incumbents
Pricing:
Supernormal pricing is coming soon but the full desktop app and web agent is currently free to try.
What users say about the Supernormal app:
\"...its AI feature is super useful, and the draft I receive always provides a good starting point. So, based on my personal experience, Supernormal’s AI tools are 10/10.\" - The Business Dive
\"I love that it runs quietly in the background and automatically captures the important details without me having to manage anything during the call.\" - Verified User, G2
The best AI transcription tool isn’t about finding the most features - it’s about choosing the one that fits your unique workflow. Whether you're recording team meetings, editing content for publishing, or managing multilingual interviews, there’s a tool built for your use case.
Need fast, structured meeting notes that sync with your CRM? Go with Supernormal. Want to edit video by editing text? Try Descript. Working across languages? Sonix might be your best bet.
Each tool on this list shines in different scenarios. Choose the one that saves you time, integrates with your stack, and supports how you actually work.
", "url": "https://www.supernormal.com/blog/transcription-tools", "title": "Best AI transcription tools for 2025: Compare accuracy, speed & features", "summary": "Find the best AI transcription tools for 2025 that fit your workflow. Compare features, accuracy, and pricing for meeting and interview transcription.", "date_modified": "2025-07-15T00:00:00.000Z", "author": { "name": "laura-james" }, "tags": [ "Meetings" ] }, { "id": "urn:sha256:3259aff7ba37239be5cf226d05c97006547d5dc4225b506bb1755ce50c23f4df", "content_html": "If you’re looking for the best notetakers for Zoom, you’re not alone. Zoom meetings are a daily reality for remote and hybrid teams - and without the right tools, it’s easy to lose track of key takeaways, action items, and decisions. That’s where AI-powered notetakers come in.
In this guide, we’ll compare five of the best Zoom notetaker apps available in 2025. Whether you need real-time captions, fast post-call summaries, or a bot-free solution for sensitive calls, we’ll help you find the right tool for your workflow.
When creating this list, we focused on practical, real-world utility. Our goal is to help you find the best Zoom notetaker for your needs, and that’s all. Here’s how we made our selections:
We analyzed third-party ratings and reviews
We looked at reviews from trusted sources like G2 to understand what real users value (and critique) about each tool. This helped us spot common pain points and standout strengths.
We highlight each tool’s best use case, not just who’s “#1”
Instead of ranking tools from first to last, we chose to spotlight what each product does best. Some are great for syncing to your CRM, while others shine at generating instant summaries. We believe the best tool depends on what you want to get out of your Zoom calls..
By combining user feedback, real-world experience, and tailored evaluation, we’ve curated a list that helps you make an informed choice faster.
![\"Zoom](\"https://framerusercontent.com/images/2bNVJxg2OZ0CBxwT1aHyL5qutm0.webp\")
Jamie AI is built for Zoom users who prioritize privacy. It records audio locally, then applies GPT-4 models to build summaries. No meeting bot ever appears, making it popular with privacy-conscious organisations. Jamie records locally on your device, so your meeting data stays more private and under your control - especially helpful in sensitive or external conversations.
Desktop app captures system audio for Zoom or in-person meetings
Supports 20 languages with speaker recognition
“Jamie Chat” lets you query past meetings
Action-item and decision extraction
No visible bot or cloud recorder
GDPR compliant with local processing
Smart follow-up capabilities via chat
Requires the desktop app on each host machine
Processing time rises with very long meetings
Free – €0/user/month: 10 meetings/month, 30‑minute limit per meeting, 20 AI chat messages/day
Standard – €24/user/month: 20 meetings/month, 90-minute meeting cap, 40 AI chat messages/day
Pro – €47/user/month: 50 meetings/month, 3-hour cap, 100 chat messages/day
Executive – €99/user/month: Unlimited meetings (3-hour max), unlimited chat messages
“As the founder of a still small boutique management consultancy, time is the most important resource. Using Jamie means that I can focus on my actual work and not waste time setting up meetings, etc. In reality, it even means that Jamie saves me the work of a human assistant (approx. 45k annual salary). In addition to the financial aspect, it reduces the risk of human mistakes and misunderstandings to an insanely low level, so that I am always perfectly prepared.” - Marcel, G2
“Sometimes the transcript may have a few minor hiccups but mainly due to the bad audio quality in our meeting room, I primarily rely on these notes any way” - User, G2
Tactiq began as a Chrome extension that captured live meeting captions. It now supports Zoom, Meet, and Teams, letting users tag highlights and export notes in multiple formats.
Captures Zoom live captions in real time
One-click tagging of key statements
Exports to Google Docs, Sheets, Markdown, PDF
AI suggestions for highlight snippets
Supports 60+ languages, including English, Spanish, French, and German, making it a strong choice for global teams
Lightweight Chrome extension with no bot joining your call
Real-time visibility into what’s being said
Powerful export flexibility across formats
Helpful tools for both live annotation and async recap
Caption accuracy limited by Zoom’s speech engine
No full audio or video recording
Free Forever: Up to 10 transcripts/month with 5 AI summary credits
Pro – $8/user/month (billed annually): Unlimited transcripts and AI insights, export integrations
Team – $16.67/user/month (billed annually): Adds team management and compliance features
Enterprise (Custom): Includes managed retention, dedicated support, SAML SSO
“There are a few things, but probably my favorite feature is the AI-powered summaries. After my meetings, Tactiq gives me a quick summary, highlighting key points and action items. This is amazing, especially if I have back-to-back meetings and don't have time to scribble down detailed notes. The feature I use the most is probably the search function. Instead of listening back to an entire recording to find a specific point, I can just search by keyword within the transcript. For example, the other day a client mentioned a feature they'd like, and I was able to jump straight to that part of the meeting to get all the details.” - Susan, G2
“Tactiq transcription from Spanish is quite basic. However, the option for customizing words seems simple and very practical.” - Feddy, G2
![\"__wf_reserved_inherit\"](\"https://framerusercontent.com/images/vv3cuuu6Sr5BOidGqAhHs01SI.webp\")
Supernormal is an AI notetaker designed to simplify how teams document meetings. It connects with Zoom to automatically capture calls, transcribe conversations, and deliver clear summaries with action items. Designed for professionals who want fast, searchable notes with minimal setup, Supernormal emphasizes clarity and speed.
Automatic Zoom recording and real-time transcription
Speaker-labelled summaries and action-item detection
Calendar integration for hands-off capture
Searchable workspace for past meetings
Structured notes are ready soon after the call
No installation required to get started
Built-in action item detection and custom templates for recurring formats
Seamless calendar and platform integration
Currently works only with supported video platforms
Limited export formats beyond web share links
Meeting hosts must configure Zoom permissions if integration is not used
Supernormal pricing offers a free starter plan with core recording and transcription features, with additional functionality and storage unlocked through paid plans.
Free Plan: $0, includes unlimited meetings across supported platforms and up to 1,000 minutes of storage per user.
Team Plan: $20 per month, unlimited seats with shared credits, offering enhanced features and increased storage.
Business Plan: $40 per month, includes advanced integrations and priority support.
“I liked the little pop-up that enabled you to annotate your meetings as you went. I liked how it showed up in the Zoom meeting - it came in as audio rather than video so you could hide the non-video participants so it wasn't in your face” - Jay K, G2
\"After finishing the free trial, it's hard to compete with other apps that offer more for free, especially the ones with noise cancellation features\" - Verified User, GetApp
![\"__wf_reserved_inherit\"](\"https://framerusercontent.com/images/bHVVqTlOOckNaHKHzSVIvwtDeA.webp\")
Fellow started as an agenda and feedback tool. Its founders quickly realized that as teams scaled, managing meetings got a lot harder. Since launching in 2019, Fellow has been used to help teams stay on the same page - from prepping agendas to tracking action items - all without jumping between tools. Their AI-powered Copilot makes it easy to capture notes and decisions right inside Zoom.
Start with a pre-meeting brief, collaborate on agendas, use AI during the call to take notes, and leave with clear summaries and action items
All participants can co-edit agendas, contribute notes, and assign action items directly within Zoom. This reduces fragmentation and ensures accountability across your team.
Templates for stand-ups, retros, 1-on-1s
Analytics on meeting health and workload
Keeps meetings on track with clear agendas
Built-in accountability for tasks
No tab switching - manage everything inside the Zoom window
Feature-rich interface requires longer onboarding
Some integrations and analytics features are locked behind the Business tier
Free: Unlimited meeting notes and agendas for teams up to 10 users
Pro – $7/user/month (billed annually) or $9/user/month (monthly): AI note taker included, integrations with Asana/Jira
Business – $10/user/month: Adds workspace templates, CRM integrations, feedback and analytics
Enterprise (Custom): Custom setup with SSO, admin controls, OKR support
“Fellow has been amazing at taking detailed and accurate notes during meetings so that I can engage stakeholders more personally and don't have to worry about scribbling on my notepad constantly. It is easy to implement and integrates with my project management software, allowing me to pass off action items between tools seamlessly. I use it every single day during every meeting that I can. Whenever I encounter issues, the customer support team is quick to find or provide a solution for me.” - Alan, G2
“Sometimes I want to review what talking points we discussed in recent meetings but sometimes the history shows you old ones instead of the most recent ones” - User, G2
[home page image]
Read AI is more than just a transcription tool. It’s designed to make Zoom meetings smarter in real time, giving you not only a record of what happened but also insight into how the meeting unfolded. From participant sentiment to engagement levels, Read AI helps teams run more efficient, productive meetings.
As one of Zoom’s most popular Essential Apps, it integrates directly into Zoom to deliver real-time analytics, automated meeting summaries, and intelligent coaching feedback.
See live engagement levels, sentiment tracking, and talk-time distribution across participants
AI summaries highlight questions and decisions
In addition to Zoom, Read supports Google Meet, Microsoft Teams, and even in-person meetings for comprehensive analytics across platforms.
Slack recaps and calendar insights
Real-time insights help presenters \"read the room\" and adjust mid-call
Free access to core features if you're on Zoom One Pro, Business, or Business Plus
Bot shows up as a participant
More focused on Zoom analytics than advanced task management
Free: 5 meetings/month with basic transcription, summaries, and search
Pro – $19.75/user/month (or $15 billed annually): Unlimited meetings, premium integrations (Notion, Salesforce, etc.)
Enterprise – $29.75/user/month (or $22.50 billed annually): Adds video playback, highlights, advanced reporting
Enterprise+ – $39.75/user/month (or $29.75 billed annually): Includes SAML SSO, data retention policies, domain capture
“I like that read gives you coaching recommendations for your meetings and analyzes the meetings. It also provides insights into your email as well.” - Tara, G2
“Trail period can be slightly extended and UI can be user friendly.” T.B, G2
While third-party apps offer deep customization and cross-platform features, Zoom itself now offers a robust built-in assistant: the Zoom AI Companion.
AI Companion is part of Zoom's AI-first work platform and is included in many paid Zoom Workplace plans. It focuses on more than just transcription. It helps manage tasks, provide real-time meeting summaries, and even suggest follow-ups. If you're already paying for Zoom, this may be a solid first step into AI notetaking without the need to install anything new.
Summaries are delivered automatically after meetings, regardless of whether they were in-person or virtual.
Helps keep meetings structured with generated agendas and time tracking.
Identifies and surfaces action items across Zoom meetings, chats, emails, and documents.
With Zoom AI Studio (available on paid plans), you can tailor AI behavior to your company’s data and needs.
No need to install third-party tools, it’s already built into Zoom
Integrated into Zoom’s broader productivity ecosystem
Strong task management and follow-up capabilities
Requires a paid Zoom plan to unlock most features
Lacks the deep summarization features and formatting flexibility of dedicated tools like Supernormal
Zoom AI Companion is not available on the free Basic plan. Paid Zoom Workplace plans (Pro and up) are required to unlock AI features.
“Zoom AI notetaking is top notch. The paragraph style meeting notes are easy to read, capture important action items, and reduce time / cost in taking meeting notes ourselves.” - Verified User, TrustRadius
“The AI companion gets many things wrong in the summary. It would be good if it learned from the meetings in our organization.” - Verified User, TrustRadius
Zoom has become the backbone of remote collaboration, and with the right notetaking tool, every meeting becomes a source of clear, actionable insight. Whether you're managing back-to-back client calls, running 1-on-1s, or aligning a distributed team, today's AI notetakers can help you stay organized without replaying hours of recordings.
In this guide, we’ve compared the best notetakers for Zoom across a range of use cases: bot-free recording, real-time captions, meeting agendas, and analytics dashboards. Tools like Supernormal, Jamie, and Tactiq shine in different scenarios depending on your workflow.
And if you're already on a paid Zoom plan, don’t overlook the Zoom AI Companion - a native assistant that brings task automation and summary features directly into the Zoom experience.
If you’re looking for a fast, easy way to take better Zoom meeting notes, Supernormal offers a seamless place to start with accurate summaries, action items, and search-ready transcripts, all with zero friction.
", "url": "https://www.supernormal.com/blog/best-notetakers-for-zoom", "title": "Best notetakers for Zoom in 2025 - AI tools compared", "summary": "Compare Supernormal, Jamie, and Tactiq to find the perfect AI notetaker for your Zoom workflow in 2025. Make meetings more productive.", "date_modified": "2025-07-01T00:00:00.000Z", "author": { "name": "danelle-azzopardi" }, "tags": [ "AI and agents" ] }, { "id": "urn:sha256:ee5b7eef7f7739fb5b69960eccff700d98d098b1c2b120a1b52cd5e005ddd919", "content_html": "Customer acquisition costs have risen across all industires, economic uncertainty is at an all-time high, and tech businesses remain vulnerable to market changes. The best products win in uncertain times, and the best way to manage those products is with customer-centricity that drives revenue growth even in challenging markets. Understanding customers' needs, defining and validating product features, and launching new products and features at speed requires careful orchestration. Managing all this with the help of AI tools can make product management dramatically more efficient and effective.
Product management oversees every phase of a product's life cycle—from development and research to positioning, pricing, and marketing. Customer insights are used to guide product decisions, helping teams create high-performing products that solve for real pain points and respond to evolving needs.
Modern product management, pioneered by companies like Amazon, Apple, and Microsoft, has evolved from traditional product-centric approaches to focus intensely on customer experience. Today, product teams typically include specialists in various roles, from Chief Product Officers and Product Managers to UX/UI Designers and Product Marketing Managers, all collaborating to bring successful products to market.
Product management is a complex process that varies depending on company ways-of-working, product lifecycle stage, and market conditions. However, here's a seven-step framework that provides a helpful starting point:
The core of every successful product begins with understanding a high-value pain point. What keeps your customers up at night? What are they struggling with?
At this stage, product teams conduct customer calls, surveys, and analyze data to understand customer needs. These needs are then transformed into viable problem statements they can solve for.
Market research helps identify the quantifiable opportunity for a product. Teams use customer surveys, competitor analysis, and market analysis to determine if the problem is worth solving and what investment is required.
This analysis helps define the target market, potential customers, pricing model, and revenue streams—critical information for making informed decisions about moving forward.
Once you've identified a problem worth solving, ideation techniques such as brainstorming, affinity diagramming, and drawing inspiration from existing products help develop potential solutions.
The key is to validate ideas with target customers to ensure market fit and collaborate with technical teams to assess feasibility.
MVPs offer the most value with the least amount of effort. Teams use UX design thinking and lean product development processes to create solutions that address immediate customer needs while reducing complexity.
At the same time, you want to develop a product customers love. A minimum lovable product (MLP) prioritizes customer experience and design to build brand loyalty and drive adoption. The challenge is finding the right balance of features and usability.
The most valuable feedback at this stage is that which challenges your assumptions. Analyze how customers think and feel about the actual product: What features do they like? What frustrates them? This goldmine of information should guide your product development decisions.
Now is the time to establish goals and objectives, define key performance indicators, and set up the product roadmap with prioritized features. Ensure the strategy is based on reasonable, achievable goals while remaining flexible enough to accommodate market changes.
This is also the stage where stakeholder alignment becomes critical. Ensuring shared understanding between product, engineering, and marketing teams is key to building a successful product.
With all pieces in place, deploy your product. Test thoroughly before launch, consider a soft launch or beta program to gauge customer reaction, and maintain clear communication with customers throughout the process.
Post-launch, continue collecting feedback and iterate accordingly. Track performance metrics closely to measure success, identify improvement areas, and create new features that enhance the product. Stay agile and be prepared to pivot if necessary.
When you understand the key components of successful product development, you can leverage the right AI tools to make each step in the process more efficient. Here are 15 AI-powered tools aligned with the product management process:
![\"viable](\"https://framerusercontent.com/images/rSLlA2aslPsMGBfDPruyFKEuxc.webp\")
What it does: Aggregates and analyzes customer feedback from multiple channels using AI
Why product managers love it: Automatically categorizes feedback by theme, sentiment, and urgency, helping identify the most pressing customer pain points
Best for: Synthesizing large volumes of customer feedback and identifying patterns that may not be apparent manually
![\"dovetail](\"https://framerusercontent.com/images/4VupM1JXEX26ozo6LEiHpnjmRG0.webp\")
What it does: AI-powered research repository that centralizes customer insights
Why product managers love it: Automatically tags and categorizes research findings, making it easy to find relevant insights when defining problems
Best for: Teams conducting extensive user research who need to organize findings systematically
![\"supernormal](\"https://framerusercontent.com/images/04w0ALt8Kyk5sfC30FQz1yH0cDg.webp\")
What it does: Automatically transcribes and summarizes customer interviews, team meetings, and stakeholder discussions.
Why product managers love it: Eliminates manual note-taking, creates searchable meeting minutes, and enables teams to focus on conversations rather than documentation.
Best for: Customer interviews, product strategy discussions, project task creation and management, and ensuring cross-functional alignment.
![\"Crayon](\"https://framerusercontent.com/images/FCm0AlrPOTfPtFXgMmYNhjnrfbM.webp\")
What it does: Uses AI to track competitor movements and market changes
Why product managers love it: Automatically monitors competitor websites, pricing changes, and feature launches to inform opportunity evaluation
Best for: Competitive analysis and identifying market gaps or threats
![\"\"](\"https://framerusercontent.com/images/goYMHnswv942ja77cVDBgN5Ic.webp\")
What it does: Provides advanced user behavior analytics enhanced with AI-powered insights
Why product managers love it: Automatically identifies patterns in user behavior, predicts churn risk, and surfaces unexpected product usage insights
Best for: Understanding feature adoption, optimizing user journeys, and making data-driven product decisions
![\"Figma](\"https://framerusercontent.com/images/EjoMXg4GDjYUTlGQWej1X7LjlAw.webp\")
What it does: AI-powered design assistant that accelerates prototyping
Why product managers love it: Generates UI components based on descriptions, suggests design improvements, and automates repetitive design tasks
Best for: Creating interactive prototypes, testing multiple design concepts quickly, and enhancing collaboration with design teams
![\"Lovable](\"https://framerusercontent.com/images/BCMbPnmvVaVVUcXQ2zQZDjk24lg.webp\")
What it does: Quickly generate working prototypes with just a prompt
Why product managers love it: Fully functional prototypes in seconds
Best for: Validating complex interaction patterns and customer reactions without coding
![\"usertesting](\"https://framerusercontent.com/images/TBurM2yYXhWYi43xgiyhTib4Uk.webp\")
What it does: AI-enhanced user testing platform that automatically analyzes user feedback
Why product managers love it: Summarizes hours of user testing into actionable insights, identifies common pain points, and suggests specific improvements
Best for: Continuous user research and validation of new features
![\"\"](\"https://framerusercontent.com/images/IBrtb22n9XZ2y7ArdIaxAHPmyw.webp\")
What it does: Creates AI-enhanced heatmaps and session recordings with automatic issue detection
Why product managers love it: Identifies usability problems, highlights abandoned interactions, and visually maps user engagement
Best for: Optimizing user interfaces, finding conversion blockers, and validating design hypotheses
![\"maze](\"https://framerusercontent.com/images/kzJaaDTDhprqKpO082e0BapBxaY.webp\")
What it does: Combines quantitative and qualitative user testing with AI-powered analysis
Why product managers love it: Automatically identifies usability issues, suggests A/B test variations, and creates comprehensive reports
Best for: Remote usability testing at scale
![\"product](\"https://framerusercontent.com/images/jexFIeHgJRXp30Eyf7RnhY14ryI.webp\")
What it does: AI-powered roadmapping that helps prioritize features based on business impact
Why product managers love it: Suggests optimal feature sequencing, identifies potential resource conflicts, and creates stakeholder-friendly visualizations
Best for: Strategic planning, resource allocation, and stakeholder alignment
![\"aha\"](\"https://framerusercontent.com/images/ghcP93Qb3huiboMun4OVbtc8A.webp\")
What it does: Combines traditional roadmapping with AI-assisted prioritization
Why product managers love it: Creates dynamic roadmaps that adapt to changing priorities, with AI suggestions based on historical performance
Best for: Enterprise product teams managing multiple product lines
![\"linear](\"https://framerusercontent.com/images/dc6gDqCjLT3ogUdPF6xgx1dSIo.webp\")
What it does: AI-enhanced project management with automated task organization and progress tracking
Why product managers love it: Intelligently organizes product development tasks, suggests optimal workflows, and provides predictive analytics on project timelines
Best for: Cross-functional teams managing complex product launches
![\"loom](\"https://framerusercontent.com/images/QPdkont7vASePcOwCthAYIY8WE.webp\")
What it does: Enhanced video messaging with automatic summaries and highlights
Why product managers love it: Creates shareable video updates with AI-generated chapters and key points, making asynchronous communication more effective
Best for: Distributed teams needing rich, asynchronous communication about product changes and launches
![\"pendo](\"https://framerusercontent.com/images/6UfvLjIFBJRUhjl8Abs7gbAx64.webp\")
What it does: Combines product analytics with AI-powered recommendations
Why product managers love it: Automatically identifies underperforming features, suggests targeted user guides, and predicts adoption rates for new features
Best for: Enterprise product teams focused on improving feature adoption after launch
The product management process is complex and requires constant collaboration between teams and stakeholders. AI tools can significantly reduce manual effort while improving outcomes at each stage:
Problem definition: Use Supernormal to capture and summarize customer interviews, then turn team planning meetings into tasks in your project management software automatically.
Opportunity evaluation: Leverage Amplitude AI and Crayon Intelligence to validate market opportunities with data.
Solution development: Accelerate prototyping with Figma AI and ProtoPie Intelligence.
User testing: Collect and analyze feedback efficiently with UserTesting AI and Hotjar Vision.
Strategy development: Create data-driven roadmaps with ProductPlan AI and Aha! Roadmaps.
Product launch: Manage development and communication with Linear AI Assistant and Loom AI.
Post-launch optimization: Continuously improve with Pendo Intelligence
By integrating these AI tools into your product management workflow, you can focus more on strategic thinking and customer relationships while accelerating execution and reducing the risk of building the wrong product.
Guide your product development with AI to reduce opportunities for errors, ensure perfect alignment between stakeholders, and ship better products that truly meet customer needs.
Try Supernormal today and transform your product management meetings.
", "url": "https://www.supernormal.com/blog/ai-tools-product-managers", "title": "15 AI tools for product managers in 2025", "summary": "Discover 15 essential AI tools that help product managers ship better products faster. Streamline research, prioritization, and stakeholder communication.", "date_modified": "2025-06-09T00:00:00.000Z", "author": { "name": "sarah-kiefer" }, "tags": [ "AI and agents" ] }, { "id": "urn:sha256:6eab333513162595761ad45391652181e3cffc85f7e8334bd33885b72c6000bf", "content_html": "At Supernormal, we’re always looking for ways to improve the accuracy and impact of the meeting notes our users generate. With AssemblyAI powering Supernormal transcripts, meeting notes became sharper, summaries more focused, and tasks easier to identify and assign.
\"Meetings are where we have our most productive, collaborative thoughts. They’re where we do our brainstorming, decision-making, and the things that push our work forward”
- Colin Treseler, CEO of Supernormal
Ask Norma, our AI meeting assistant became even more precise, helping users unlock better insights from past conversations. You can ask questions like, “What are the key takeaways and action items from my meetings with Maria?” and get an accurate, contextual response.
Implementing AssemblyAI was not just a technical upgrade. It was a strategic decision that changed how we serve our users. “AssemblyAI has put together incredible speech-to-text models,” Colin explains. “If we have a 1 percent improvement in our transcription, that directly impacts our business.”
Supernormal now processes millions of tasks every month, and accurate transcripts play a big part in making that possible. Getting transcription right is not just about capturing words, it’s about delivering on our promise to take the work out of meetings and make every conversation count.
", "url": "https://www.supernormal.com/blog/how-supernormal-partnered-with-assemblyai-to-improve-transcription-accuracy", "title": "How Supernormal partnered with AssemblyAI to improve transcription accuracy", "summary": "Learn how Supernormal partnered with AssemblyAI to deliver industry-leading transcription accuracy for better automated meeting notes.", "date_modified": "2025-06-03T00:00:00.000Z", "author": { "name": "danelle-azzopardi" }, "tags": [ "Company updates" ] }, { "id": "urn:sha256:bf04f3492d070ed5361244d870bcb14ec4b38c499001b58f6dfad9361fa6f09e", "content_html": "Project managers today aren’t just juggling timelines and stakeholders, they’re navigating a growing stack of digital tools, especially those now powered by artificial intelligence. Whether you're launching products, running events, managing sprints, or leading cross-functional teams, AI tools can dramatically improve your efficiency and clarity.
Here are 10 of the best AI tools for project managers in 2025, each built to streamline workflows, reduce manual effort, and help teams stay aligned.
AI for: Roadmapping, issue tracking, and product delivery
Linear helps product and project teams plan, track, and execute work quickly. Its clean interface and smart automation features make it a favorite for tech-driven teams.
AI features include:
Auto-generated timelines and changelogs
Summarized bug reports and task updates
Suggested next steps based on project activity
Best for: Fast-moving product or engineering teams.
![\"supernormal](\"https://framerusercontent.com/images/CwldTnLW94rrSM5bq2TOuEhd8.webp\")
AI for: Meeting notes and action items
Supernormal automatically records your meetings (Zoom, Google Meet, Teams), transcribes them, and sends action-ready summaries.
AI features include:
Instant meeting notes
Action item generation
Calendar-based auto-joining
Best for: Remote teams and busy PMs who need documentation without distractions.
![\"clickup](\"https://framerusercontent.com/images/3rsfopjPLaeopYxTpJ1PpfDSQU.webp\")
AI for: All-in-one task and doc management
ClickUp now includes a robust AI assistant that helps generate content, summarize project docs, and even answer workspace-specific questions.
AI features include:
Auto-generated task lists and SOPs
Document summarization and rewriting
Workspace Q&A assistant
Best for: All-in-one teams needing docs, tasks, and timelines in one place.
![\"notion](\"https://framerusercontent.com/images/ny2Mp4wAjJut2PFME3lcTpUoRT8.webp\")
AI for: Docs, planning, and brainstorming
Notion’s AI can help project managers draft specs, outline agendas, and query internal knowledge instantly.
AI features include:
Smart writing and content generation
AI search within your workspace
Quick action-item breakdowns
Best for: Planning-heavy teams, internal documentation, and any text-based assets.
![\"motion](\"https://framerusercontent.com/images/CST9IbxDGvL1nFxeIlT1Vhs4.webp\")
AI for: Smart calendar scheduling
Motion plans your entire day, automatically. It finds time for your tasks around your meetings and adapts in real time.
AI features include:
Auto-scheduling based on deadlines
Re-prioritization when things change
Calendar optimization
Best for: Overloaded PMs managing multiple parallel projects.
![\"\"](\"https://framerusercontent.com/images/szzZm1iVBe3GpluwL2o6dzn3Ps.webp\")
AI for: Project planning, resourcing, and forecasting
Forecast is designed for data-driven decision making. It uses AI to analyze past work and predict project risks and timeline issues.
AI features include:
Smart resource suggestions
Risk detection
Timeline optimization
Best for: Agencies and PMOs with complex scopes and large teams.
![\"wrike](\"https://framerusercontent.com/images/LKXC1jBAuUSat5RSnZpxQOW9taY.webp\")
AI for: Automation, prioritization, and analytics
Wrike helps large organizations manage workload, identify risks, and automate task generation based on project inputs.
AI features include:
Risk prediction
Smart task generation
Voice-to-action transcription
Best for: Enterprise or cross-functional teams needing scale, control and an overview of where problems may arise.
![\"\"](\"https://framerusercontent.com/images/2juKbn4Y96xx9IbYVEp02XGzYII.webp\")
AI for: Goal tracking and team alignment
Asana’s AI tools help project managers align tasks with goals and automate recurring workflows.
AI features include:
Natural language task creation
AI-powered prioritization
Progress tracking suggestions
Best for: Strategic teams focused on long-term objectives.
![\"trello](\"https://framerusercontent.com/images/enAgFxAS44v5p3hQ2844uhHahVM.webp\")
AI for: Lightweight task automation
Trello's Butler adds simple automation to a visual Kanban experience, perfect for quick task updates and reminders.
AI features include:
Auto-assigning tasks
Smart card movement
Rule-based automations
Best for: Simple projects or teams who want visual task flow.
![\"__wf_reserved_inherit\"](\"https://framerusercontent.com/images/qQG0fEydeS8mfmu0oPXBrUzu7U.webp\")
AI for: Data-rich project tracking
Smartsheet turns spreadsheets into intelligent project dashboards. With built-in AI, it helps summarize text, generate reports, and predict delays.
AI features include:
Dashboard generation
Predictive analytics
Smart form intake and workflows
Best for: Spreadsheet-heavy teams transitioning into smarter tools.
AI is no longer a nice-to-have for project managers—it’s a competitive edge. Whether you're handling timelines, documentation, or task dependencies, the right AI tools can help projects run smoother and faster.
And you don’t need to use every tool listed above. Maybe start small with a meeting assistant like Supernormal, then layer on a task management tool like Asana and a documentation hub like Notion. Before you know it, your projects will be running more smoothly than before, with AI handling a lot of the workload for you.
", "url": "https://www.supernormal.com/blog/ai-tools-project-managers", "title": "10 best AI tools for project managers in 2025 | boost efficiency with AI", "summary": "Explore 10 AI-powered tools that help project managers automate tasks, track progress, and lead smarter projects. Boost efficiency and deliver on time in 2025.", "date_modified": "2025-05-21T00:00:00.000Z", "author": { "name": "sarah-kiefer" }, "tags": [ "Client work" ] }, { "id": "urn:sha256:41011362ef147168c0f31736588f8fde2ee4df098002e564dc72709c1f016e55", "content_html": "As businesses grow more complex and cross-functional, project management has evolved beyond simple task checklists. Success today depends on choosing the right project management technique—a structured approach that guides how a project is planned, executed, and delivered.
Whether you're leading a small team or managing enterprise-scale initiatives, the right method can improve timelines, communication, and overall project success.
Here are 10 project management techniques every professional should know in 2025.
Best for: Projects with clear, fixed requirements
Waterfall is a traditional, linear project management technique. It breaks projects into sequential phases—like planning, design, development, and testing. Each stage must be completed before the next begins.
This technique is highly structured and ideal when deliverables are well-defined upfront. However, it’s less flexible if changes are needed mid-project.
Best for: Projects that require flexibility and frequent updates
Agile is not a single framework but a mindset rooted in adaptive planning and iterative development. Agile emphasizes collaboration, continuous delivery, and responsiveness to change.
Rather than managing tasks linearly, teams work in short cycles or “iterations” and refine plans based on feedback.
Best for: Software teams and fast-moving projects
Scrum is a specific Agile framework that organizes work into time-boxed sprints, typically 2–4 weeks. Teams hold daily stand-ups, sprint planning sessions, and retrospectives.
Scrum helps teams stay focused and continuously improve, making it a favorite among developers and product teams.
Best for: Visualizing workflows and limiting work in progress
Kanban uses boards to visualize the flow of tasks. Each task is represented as a card that moves through columns like \"To Do,\" \"In Progress,\" and \"Done.\"
A key concept in Kanban is Work In Progress (WIP) limits, which prevent teams from taking on too much at once—promoting focus and flow.
Best for: Reducing waste and optimizing resources
Lean focuses on delivering value by minimizing waste—whether in time, effort, or materials. Originating in manufacturing, it has since been widely adopted in tech and operations.
Lean emphasizes continuous improvement, customer feedback, and process efficiency.
Best for: Planning and scheduling complex projects
CPM helps identify the longest stretch of dependent tasks in a project—called the “critical path.” These tasks directly impact the project’s end date, so delays here affect the entire timeline.
CPM is useful for determining task priorities and understanding how delays will ripple through a project.
Best for: Estimating project timelines with uncertainty
PERT uses probability to estimate task durations. Each task is given three time estimates: optimistic, pessimistic, and most likely. These are averaged to provide a more realistic project schedule.
PERT is especially helpful when managing uncertainty or highly variable timelines.
Best for: Teams transitioning from Scrum to Kanban
Scrumban combines elements of Scrum and Kanban. Teams maintain a visual board (like Kanban) while working in sprints and holding regular retrospectives (like Scrum).
It’s a flexible hybrid approach for teams needing more structure than Kanban but more flexibility than Scrum.
Best for: Reducing defects and improving quality
Six Sigma is a data-driven methodology aimed at minimizing errors and improving processes. It uses tools like DMAIC (Define, Measure, Analyze, Improve, Control) to guide project execution.
While most common in manufacturing and healthcare, Six Sigma principles can be applied in any project environment where quality is critical.
Best for: Projects with rapidly changing goals
APF is designed for high-change environments. Instead of locking into a fixed plan, teams work in cycles, continuously reviewing what’s working and adjusting based on results and stakeholder feedback.
This technique suits innovation-driven or client-facing projects where requirements are likely to evolve.
The right project management technique can be the difference between confusion and clarity—or missed deadlines and on-time delivery. As work environments shift and grow more complex, project managers must adapt their approach to fit their team’s needs, stakeholder expectations, and project complexity.
Whether you're a veteran or new to the role, mastering these techniques will equip you to lead with confidence in 2025 and beyond.
", "url": "https://www.supernormal.com/blog/project-management-techniques", "title": "Top 10 project management techniques in 2025 | proven strategies for success", "summary": "Master 10 essential project management techniques for 2025. Learn Agile, Scrum, Kanban, Lean, and proven methods to lead projects with confidence.", "date_modified": "2025-05-21T00:00:00.000Z", "author": { "name": "sarah-kiefer" }, "tags": [ "Client work" ] }, { "id": "urn:sha256:12fd0987a476d251d090f45ac56c4dd71bea338143060902ca713e623b1f5a38", "content_html": "In today's fast-paced digital world, finding the best task management software can be the difference between staying organized and drowning in deadlines. Whether you're a solo entrepreneur, run an agency, or are part of a large enterprise, the right task management tool can transform your workflow and supercharge your productivity.
Why You Need Task Management Software
Before diving into our list of the best task management software options, let's understand why these tools are essential:
Improved organization: Keep all your tasks in one centralized location
Enhanced collaboration: Share tasks and updates with team members in real-time
Better time management: Set priorities and deadlines to stay on track
Increased accountability: Assign tasks to specific team members
Reduced stress: Never worry about forgetting important deadlines
Data-driven decisions: Generate reports to analyze productivity patterns
For agencies and freelancers juggling multiple demanding clients effective task management tools have become particularly business critical. Often tools like this form the backbone of their project delivery systems, ensuring client work stays on schedule and within scope.
Selecting the right task management tool depends on several factors:
Team size: Are you a solo operator or managing a team of 50?
Workflow preferences: Do you prefer visual Kanban boards or traditional lists?
Integration needs: What other tools does your task manager need to connect with?
Learning curve: How much time can you invest in learning a new system?
Budget: What's your price range for productivity tools?
Mobile access: Do you need to manage tasks on the go?
Now, let's explore the 10 best task management software options available in 2025:
G2 Rating: 4.4
![\"trello](\"https://framerusercontent.com/images/AwhgsEWn5jAuBeRfhDkrBg724.webp\")
Key Features:
Intuitive Kanban board interface
Drag-and-drop functionality
Customizable workflows
Power-Ups for extended functionality
Robust free plan
Pricing: Free plan available; Premium plans start at $5/month per user
Best For: Visual thinkers, small to medium teams, and those who prefer the Kanban method
Trello shines with its simplicity and visual appeal. Its board and card system makes it easy to see your workflow at a glance. Create boards for different projects, add cards for tasks, and move them around as you progress. It's like having a digital whiteboard accessible from anywhere.
What to Expect From Trello
Trello might look simple on the surface, but there's a lot going on under the hood. Once you’ve got your boards and cards set up, you can start taking advantage of features like Butler automation to handle those repetitive tasks—think auto-moving cards, setting due dates, or sending reminders.
Want to see your tasks in a calendar or timeline view? Power-Ups let you switch things up and connect Trello to tools you’re already using, like Slack, Google Drive, or Jira. It’s flexible enough for solo users and powerful enough for growing teams.
You can also use labels, checklists, and team assignments to keep everything organized without feeling overwhelmed. And since updates happen in real-time, everyone stays on the same page—whether you’re in the same office or working across time zones.
💡 Did you know? Trello was originally developed as a side project by Fog Creek Software and quickly became one of the most popular tools for Kanban-style task management.
User Review Highlight:
“The best task and project management solution. Allows me to customize and structure my projects and tasks with infinite possibilities.” - Claudi R, G2
![\"asana](\"https://framerusercontent.com/images/E8xYlozHFhk9AzDwjS64NUkO4.webp\")
Key Features:
Multiple project views (lists, boards, timelines)
Goal tracking and milestones
Workflow automation
Advanced reporting
Team workload visualization
Pricing: Free for up to 15 users; Premium plans start at $10.99/month per user
Best For: Agency teams juggling multiple client projects simultaneously
Asana has become the secret weapon for those managing complex client work across multiple campaigns. With alternate views including lists, boards, and timelines, it's perfect for complex projects with tight deadlines. You can set goals, track milestones, and even automate repetitive tasks. The workload visualization feature is particularly valuable for resource planning, allowing team leads to see who's overloaded and who has bandwidth for new projects. Its workflow capabilities make it one of the best task management software options for growing companies.
![\"todoist](\"https://framerusercontent.com/images/q8n73KPkr3IwevFnYRivev40.webp\")
Key Features:
Clean, minimalist interface
Natural language input
Recurring task management
Priority levels
Cross-platform synchronization
Pricing: Free plan available; Pro plan at $4/month per user
Best For: Individuals and small teams who value simplicity and efficiency
Todoist excels at organizing tasks into projects and sub-projects with a clean interface. Its standout features include natural language input (type \"tomorrow at 3pm\" and it automatically sets the deadline) and recurring tasks. For those who love structure without complexity, Todoist ranks among the best task management software choices.
![\"monday](\"https://framerusercontent.com/images/ELo4Rq8ZAu6t60YkjUDGMy2Qek.webp\")
Key Features:
Highly customizable workflows
Colorful, visual interface
Multiple dashboard views
Automation capabilities
Integration with 40+ tools
Pricing: Basic plans start at $8/month per user
Best For: Agencies that need client-facing dashboards and comprehensive project tracking
Monday.com has become a favorite for its ability to create client-friendly dashboards while managing complex work behind the scenes. Its highly customizable interface allows users to create branded client portals that provide transparency without overwhelming clients with unnecessary details. This comprehensive platform serves as a Swiss Army knife for operations, connecting everything from initial creative briefs to final asset delivery.
Key Features:
15+ ways to visualize tasks
Docs, goals, whiteboards, and chat
Custom automation
Time tracking
1,000+ integrations
Pricing: Free plan available; Paid plans start at $5/month per user
Best For: Teams looking for an all-in-one productivity solution
ClickUp has rapidly risen as one of the best task management software platforms by combining tasks, documents, goals, and communication in one place. With over 15 ways to view your work and extensive customization options, it adapts to virtually any workflow need.
Key Features:
Streamlined, elegant interface
Powerful keyboard shortcuts
Automated workflows
Built-in roadmaps
GitHub and Figma integrations
Pricing: Free for up to 6 users; Standard plan starts at $8/month per user
Best For: Product teams, design agencies, and technical project management
Linear has quickly become the go-to task management solution for modern, tech-focused companies. Its beautifully designed interface and powerful automation capabilities make complex technical projects flow smoothly. Digital agencies and companies with large teams of engineers and developers particularly appreciate Linear's roadmap features, which make it easy to view project timelines and keep internal teams aligned.
![\"microsoft](\"https://framerusercontent.com/images/g5z9UJ33sQtgCNDBlYftajt9OEI.webp\")
Key Features:
Seamless Microsoft 365 integration
My Day planning feature
Shared tasks and lists
Smart suggestions
Cross-platform availability
Pricing: Free with Microsoft account; enhanced features with Microsoft 365 subscription
Best For: Microsoft 365 users who need simple task management
Microsoft To Do integrates perfectly with the Microsoft ecosystem. This straightforward tool works well for personal task management and small team collaboration. If you're already using Microsoft products, this trusty sidekick slots right into your existing workflow.
Key Features:
All-in-one workspace capability
Flexible database structure
Wiki, docs, and task management combined
Customizable templates
Collaborative editing
Pricing: Free personal plan; Team plans start at $8/month per user
Best For: Teams that need to combine documentation, knowledge management, and task tracking
Notion blends task management with notes, wikis, and databases in a uniquely flexible system. This versatility makes it one of the best task management software options for teams who need more than just task lists. Create custom workflows, documentation, and project trackers all in one place.
![\"basecamp](\"https://framerusercontent.com/images/wsiD08ynbWw0B5q1ItiO7dq8Pc.webp\")
Key Features:
Message boards
To-do lists
Schedules and calendars
Client access portal
File storage
Pricing: Flat fee of $99/month for unlimited users
Best For: Client-facing teams and agencies who need to combine communication with task management
Basecamp has been a longtime favorite of boutique creative agencies and smaller companies. Its unique approach combines task management with robust communication tools, creating a single source of truth for client projects. The flat-rate pricing regardless of team size makes it exceptionally attractive for growing agencies, particularly those with fluctuating staff sizes due to freelancers. One mid-sized design agency reported saving over $15,000 annually by switching from per-user pricing to Basecamp's flat rate model. For agencies managing multiple client projects simultaneously while maintaining clear client communication, Basecamp offers an excellent unified solution that scales with your business.
Key Features:
AI meeting assistant
Automatic task extraction from meetings
Task assignment capabilities
Integration with other task platforms
Smart follow-ups
Pricing: See here. Pro plans start at $10 per month.
Best For: Teams that conduct frequent meetings and need to capture action items efficiently
The future of task management is here with Supernormal, an AI-powered meeting assistant that's revolutionizing how we capture and manage action items from meetings. It listens to your virtual meetings, identifies tasks, and automatically assigns them to team members. You can also set it up to send tasks directly to your preferred task management platform. For meeting-heavy organizations, this cutting-edge tool represents the next evolution in the best task management software category.
![\"superlist](\"https://framerusercontent.com/images/wmQZiNgYWN1WOJQXJdumE4A7Txo.webp\")
Key Features:
Home screen widget
To-do lists
Chats in tasks
Meeting transcription
Repeating tasks for routine actions
Email and Slack integrations
Tasks all private by default
Pricing: See here. Pro plans start from $15 per month.
Best For: Freelancers and individual contributors.
Superlist started its evolution from the perspective of a personal to-do list, and its focus on privacy and individual efficiency makes it ideal for freelancers working on multiple projects with multiple clients.
“Even the best task management software out there won’t work if your company isn’t using it properly. Garbage in, garbage out, as is the case with any work tool really.”
Colin Treseler, CEO and Co-Founder of Supernormal
Having the right tool is just the first step. To maximize your productivity with any of these best task management software options:
Commit to consistent use: Make entering and updating tasks part of your daily routine
Create specific, actionable tasks: \"Draft Q2 sales report introduction\" beats vague tasks like \"Write report\"
Prioritize ruthlessly: Not all tasks deserve equal attention
Break down large projects: Turn mountains into manageable hills
Schedule regular reviews: Weekly task list audits keep everything current
Use due dates wisely: Reserve them for genuine deadlines
Embrace collaboration features: Keep everyone aligned with comments and mentions
Even with the best task management software, you might encounter these common hurdles:
Task overload: Be ruthless about prioritization
Outdated task lists: Build a habit of regular updates
Team misalignment: Establish clear usage guidelines
Task duplication: Check existing tasks before creating new ones
Motivation challenges: Break large tasks into smaller wins
Context switching: Group similar tasks for focused work sessions
Analysis paralysis: Remember the goal is completion, not perfect organization
The task management landscape continues to evolve with artificial intelligence and automation leading the charge. Tools like those listed above represent just the beginning of what's possible when AI meets productivity. According to Asana’s Anatomy of Work report, knowledge workers spend 58% of their time on work coordination rather than skilled work, but organizations using structured work management systems report 45% higher employee engagement. So selecting the right task management tool is likely to result not only in greater efficiency, but also in a happier team.
As these technologies advance, we can expect task management to become even more intuitive and proactive—anticipating our needs before we even recognize them and potentially transforming how companies staff and structure their project teams.
The best task management software is ultimately the one that fits your specific needs and work style. Whether you prefer the visual simplicity of Trello, the robust capabilities of Asana, or your tasks all come from meetings so you suit something like Supernormal, there's a perfect solution waiting for you.
Don't be afraid to experiment with different tools until you find your ideal match. And remember—these tools exist to make your life easier, not to add another layer of complexity. The right task management software should feel like gaining a superpower, not adding another tool to juggle.
", "url": "https://www.supernormal.com/blog/comprehensive-guide-task-management-software", "title": "11 best task management software tools to boost your productivity in 2025", "summary": "Discover 11 best task management tools for 2025. Compare features, benefits, and how AI-powered solutions automatically track action items from meetings.", "date_modified": "2025-05-21T00:00:00.000Z", "author": { "name": "sarah-kiefer" }, "tags": [ "Productivity" ] }, { "id": "urn:sha256:2df17d8096df9c00d99e6b104b63a554fd3f5a5192bb8aa8de9287536125d39e", "content_html": "Inbound sales are the lifeblood of many businesses, but it’s not without its challenges. Responding to leads promptly, qualifying prospects, and keeping up with follow-ups can overwhelm even the best sales teams. That’s where AI Sales Agents come in. These conversational AI agents are transforming the way businesses handle inbound sales, automating repetitive tasks, and ensuring no lead falls through the cracks.
In this blog post, we’ll explore how AI Sales Agents can supercharge your inbound sales process, provide real-world examples, and show you how Supernormal makes it incredibly easy to build and deploy your own AI Sales Agent. Let’s dive in and see how AI is reshaping sales.
Inbound sales have one major challenge—timing. You have to respond to leads when they’re most interested, which is often immediately after they interact with your website or submit a form. But sales teams can’t always keep up. Leads can go cold quickly, and missed opportunities are costly.
This is where AI Sales Agents shine. These conversational AI agents can engage with leads instantly, 24/7. Whether it’s answering a question, scheduling a demo, or qualifying the lead based on their responses, an AI Sales Agent is always on call, ready to act. No more waiting for a human salesperson to follow up.
Speed is key in inbound sales. Studies show that contacting a lead within five minutes of their inquiry increases the chances of converting them into a customer by a factor of 10. But not every sales team can respond that quickly, especially during off-hours. AI Sales Agents solve this problem by providing instant responses to incoming leads.
Imagine this: A potential customer fills out a form on your website at 10 p.m. asking for more information. Your AI Sales Agent immediately engages them, answers their questions, and schedules a demo for the next day. That’s the power of automation combined with conversational AI. No more missed opportunities, and no more cold leads.
AI Sales Agents aren’t just about quick responses—they’re also smart. They can ask qualifying questions to determine whether a lead is worth pursuing. This saves time for your human sales team, who can focus on the most promising leads instead of sifting through every inquiry. And because the AI agent can handle complex conversations, it can tailor its responses based on the lead’s answers, making the interaction feel personal and engaging.
Despite being AI, these agents can still provide a personalized experience. Thanks to natural language processing (NLP), they understand context and can respond in a way that feels human. For inbound sales, this level of engagement is crucial. It’s not just about answering questions—it’s about making the lead feel heard and valued. AI Sales Agents can create that sense of connection while scaling the process in ways that human sales teams alone cannot.
The beauty of AI Sales Agents lies in their versatility. They can operate at multiple stages of your inbound sales funnel, from lead generation to nurturing, ensuring that the right prospects move smoothly through the pipeline. Let’s explore how these AI-powered agents can make each stage of the funnel more efficient.
At the top of the sales funnel, AI Sales Agents act as the first point of contact. Whether it’s through a video call or a chatbot on your website or an automated email response, these agents can greet leads, answer their initial questions, and provide helpful information. The goal here is to keep leads engaged long enough to qualify them for the next stage.
AI agents can also capture contact information and pass it along to your CRM system. By automating this step, you reduce the workload for your sales team, allowing them to focus on higher-value tasks. And since AI Sales Agents operate around the clock, they can engage with leads at any time, whether it’s during business hours or at 2 a.m.
Once a lead has been qualified, it’s all about nurturing that relationship. AI Sales Agents can take on this role as well, following up with leads at regular intervals and providing additional information based on their previous interactions. By keeping the conversation going, AI agents ensure that leads don’t go cold.
Think of it as a smart follow-up system. Instead of relying on humans to remember to send emails or make phone calls, the AI agent does it automatically. It can even tailor the follow-up messages based on the lead’s preferences and behavior, creating a more personalized experience that keeps leads moving down the funnel.
At the bottom of the funnel, AI Sales Agents can assist with scheduling calls, demos, or product walkthroughs to close the deal. And because the agent has been involved in previous conversations, it has all the necessary context to ensure a seamless handoff to a human salesperson, when needed.
This integration makes the transition from automated interaction to human interaction feel smooth and professional. No more awkward handoffs or miscommunication. The AI agent provides a full transcript of its interactions with the lead, allowing your sales team to pick up right where it left off.
Now that you understand how valuable AI Sales Agents can be for inbound sales, you’re probably wondering how to get started. That’s where Supernormal comes in. Supernormal is the fastest and easiest platform to build and deploy your own AI Sales Agent, even if you have no technical background.
Supernormal is designed with simplicity in mind. You don’t need to be a coder or have extensive AI knowledge to create a powerful AI Sales Agent. The platform’s intuitive interface allows you to set up, customize, and deploy your agent in minutes. You can easily tailor your AI agent to meet your specific sales needs, from the types of questions it asks to how it interacts with leads.
One of the key features of Supernormal is that it supports Voice Agents. Voice Agents are conversational AI agents that are more engaging and easier to use than traditional chatbots. This ensures that you can engage with your leads in a way that is natural and requires less effort from them to get the information they need.
Supernormal's voice agents are also able to seamlessly schedule followup meetings for prospects with a sales agent. That's right, no more filling out forms and going back and forth on email to find a time. Your Supernormal Voice Agent has it all covered.
Businesses using Supernormal to power their inbound sales efforts have seen incredible results. From higher conversion rates to shorter sales cycles, Supernormal’s AI Sales Agents are helping companies close more deals, faster. Whether you’re a small business looking to scale or a large enterprise with a massive sales pipeline, Supernormal’s platform is designed to help you succeed.
And the best part? It’s incredibly user-friendly. With pre-built templates, easy-to-use customization tools, and seamless integrations with your existing sales stack, Supernormal makes it easy to get started with AI Sales Agents. No headaches, no long learning curves—just results.
The future of sales won’t be AI replacing humans—it will be AI working alongside humans. AI Sales Agents will handle the repetitive, time-consuming tasks, allowing salespeople to focus on high-value activities like building relationships, negotiating deals, and closing sales. This collaboration will create a more efficient and effective sales process, where everyone plays to their strengths.
By embracing AI Sales Agents like Supernormal’s Voice Agents now, businesses can prepare for this future and start reaping the benefits of automation, personalization, and efficiency in their sales process.
", "url": "https://www.supernormal.com/blog/boosting-inbound-sales-with-ai-voice-agents", "title": "Boosting inbound sales with AI voice agents", "summary": "Boost inbound sales with AI voice agents that respond instantly to inquiries, qualify leads 24/7, and route prospects to the right team.", "date_modified": "2024-10-14T00:00:00.000Z", "author": { "name": "sarah-kiefer" }, "tags": [ "AI and agents" ] } ] }