Two years ago, Omaha Steaks lost 16% of callers before anyone could help them. Last year, the rate fell to 9%. Today it is 3%, measured on a line that handles roughly 12 times its normal call volume in December.
That change carries real revenue weight. Roughly half of Omaha Steaks’ inbound calls are sales calls. When one of those callers hangs up, the company loses a customer who was already close enough to buying that they picked up the phone.
Call abandonment rate is easy to bury among service-level metrics. On a sales line, it belongs next to conversion rate. Both tell you how effectively the business turns existing demand into revenue.
The improvement came from three practical changes to how calls entered and moved through the queue.
First, answer time went to zero. An AI agent picks up on the first ring and can handle calls concurrently, including the holiday surge. The queue that used to form in front of a fixed number of people no longer forms before the greeting. That kind of capacity is particularly valuable for retail voice AI, where a few peak weeks can carry an outsized share of annual revenue.
Second, most callers no longer need the human queue. Omaha Steaks reached about 60% overall containment, so the majority of callers finish what they came to do with the AI agent. People who need a person still get one, but they enter a much shorter line.
The third change was less obvious. Callers who speak with the AI and then choose to wait for a person stay on the line about 30 seconds longer than callers did before. Being greeted, understood, and told what will happen next appears to make the wait feel more tolerable. Across a holiday queue, another 30 seconds is enough to keep a meaningful number of calls from disappearing.
Consider a sales line receiving 10,000 calls in a peak week. At 16% abandonment, 1,600 calls end in the queue. At 3%, that number falls to 300. The difference is 1,300 conversations that the business now has a chance to complete.
That is an illustration, not Omaha Steaks’ reported weekly volume. But the revenue logic is straightforward. Multiply rescued sales calls by the conversion rate for answered calls, then by average order value. If one-third of those 1,300 callers buy, the change produces about 433 additional orders from demand the company had already acquired.
An annual average can hide a queueing problem. Omaha Steaks does roughly half its annual revenue during the holiday period, when call volume rises to about 12 times steady state. Hiring enough people for that peak meant recruiting months in advance, training thousands of seasonal workers, and still accepting that queues would lengthen when demand arrived faster than agents could finish calls.
Concurrency changes that equation. The AI answer layer does not need a December staffing plan to pick up the first ring. It can complete routine work immediately and reserve the human queue for calls that genuinely need judgment or escalation. The bigger gain is structural. It removes the sharpest mismatch between demand and capacity.
Companies can test this without replacing the rest of the contact-center stack. A narrow number, overflow queue, or defined slice of traffic can route through an AI agent first, then transfer to the existing CCaaS when necessary. The CX Leader’s Guide to Voice AI covers the questions buyers should ask about integrations, handoffs, security, pricing, and operating ownership.
Start by agreeing on the denominator. Some teams count all offered calls; others remove callers who hang up within a few seconds. Either method can be defensible, but a before-and-after comparison is useless if the definition changes during the project.
Then segment the number. Separate sales from service, and review abandonment by intent, hour, day, queue, and transfer outcome. A single blended rate can improve while a valuable sales flow gets worse. Omaha Steaks learned a related lesson when conversational routing showed that its phone tree had overstated sales intent for years. The details are in the analysis of why phone tree data misreads customer intent.
Finally, measure the whole journey. Track how quickly the AI answers, how many calls it completes, how many it transfers, how long transferred callers wait, and whether those callers ultimately buy or resolve their issue. A low abandonment rate is only valuable when customers reach a useful outcome.
Begin with the part of the queue where abandonment is most expensive. On a sales line, that may be a dedicated campaign number or a narrow ordering flow. On a service line, it may be after-hours intake or a high-volume status request. Choose a slice with predictable intent, enough traffic to measure, and a clear outcome.
Route a small share of calls through the AI answer layer and keep the existing queue as the fallback. Review recordings and transcripts daily. Watch abandonment alongside completion, transfers, latency, conversion, and repeat calls. If the overall rate falls but one intent starts producing bad handoffs, fix that flow before expanding traffic.
Omaha Steaks’ result came from removing dead time at the start of the call, completing more work before a queue was needed, and giving transferred callers enough context to keep waiting. The drop from 16% to 3% is the headline. The more useful lesson is that queue performance can improve without manufacturing more demand or rebuilding the entire contact center.
Every rescued call is a second chance to convert demand that marketing already paid for. That makes abandonment one of the clearest places to connect contact-center operations with the economics of AI-assisted sales.
", "url": "https://www.usesimple.ai/blog/how-omaha-steaks-cut-call-abandonment-from-16-to-3", "title": "How Omaha Steaks Cut Call Abandonment From 16% to 3%", "summary": "How Omaha Steaks cut call abandonment from 16% to 3% during a 12x holiday surge—and how to measure the revenue recovered from shorter queues.", "date_modified": "2026-08-13T00:08:52.544Z" }, { "id": "urn:sha256:da5d89da69a60d26e9ddecd34770dddf095856ecca6c3bdcc5b2f3001636accb", "content_html": "Most voice AI evaluations fail before the demo starts, because the buyer walks in without a list of questions that separate a really good product from a well run demo. This guide is that list: twelve questions you can ask your prospective solution providers, the point of each question, and what a strong answer sounds like.
Deterministic bots walk a tree: ask, capture, branch. They break the moment a caller says two things at once. Ask the vendor to demo a caller who interrupts, changes their mind mid-order, and gives information out of sequence. A strong answer is a live demo that survives all three, and an architecture explanation that doesn’t include the phrase “dialog flow.”
\"Agentic\" means the system takes actions: placing orders, updating records, booking appointments, processing claims. Ask exactly which systems the agent will write to on day one, and what a completed task looks like in your stack. A strong answer names APIs and shows a task finishing with no human touch.
Some deployments stall for months waiting on integration work. Get specific: what can launch with zero integration? Inbound flows often can, because the caller supplies the information the agent needs. One of our home-services customers went live while its CRM vendor kept the API closed, and Omaha Steaks exposed APIs from a homegrown AS400 stack in about a week with our engineers alongside theirs.
Six months is an answer you should question. Eight days is an answer you should verify. Omaha Steaks signed a proof of concept on June 2 and took first live calls June 10; ask any vendor for their equivalent story with dates and a reference customer you can call.
Voice tolerates far less delay than chat. Response latency around 800 milliseconds is where conversations start feeling natural; beyond that, callers talk over the agent and loops multiply. Ask what the vendor’s median and p95 response times are on real calls, and whether the turn-taking and voice-activity models are their own.
If the vendor is a thin wrapper on someone else’s transcription, inference, and voice APIs, their latency, cost, and peak-season capacity are all someone else’s decisions. Ask which pieces they host and auto-scale themselves. If they rent everything, ask how they handled last December’s peak, and pay attention to whose engineers did the work.
Every agent needs to escalate sometimes. The difference is whether the context survives to sets the human up for success. A strong answer includes a warm transfer, a screen pop showing the human agent everything gathered so far, and partial-containment reporting so you get credit for the half of the call the AI already did. The customer should never repeat themselves.
You want intent categorization across 100% of calls, containment and conversion by use case, and alerting on anomalies. Mid-peak last December, our monitoring caught a spike in callers saying the add-to-cart button was broken on the Omaha Steaks website; their web team shipped the fix before the QA vendor had transcribed a single call. Ask the vendor for the story their analytics caught that nobody was looking for.
Prompt edits, voice changes, and script experiments need versioning, a sandbox, and ideally A/B testing on live traffic. Ask how long a change takes to ship, and how you’d measure whether it helped. “Hours” is a good answer. “Submit a ticket” means every experiment waits in the vendor’s queue.
SOC 2 Type II at minimum. HIPAA if health information can come up. Real-time PII redaction in transcripts. Ask where audio and transcripts are stored, for how long, and who can access them. A strong vendor answers without checking with legal.
Per-resolution pricing puts the vendor in charge of deciding what “resolved” means. Usage pricing is more predictable but demands volume estimates. Whatever the model, ask for the invoice math on your actual call volume, peak month included, and get the overage terms in writing.
The deployments that hit 70% containment and hold it are the ones where vendor engineers meet with the customer team weekly and treat the roadmap as shared. Ask who your named engineers are, how often you’ll meet, and what the last customer shipped with them in month six. If the answer is “documentation and a support portal,” plan accordingly.
No vendor aces all twelve. Weight the ones tied to your economics: if your inbound line sells, questions 1, 2, 5, and 9 decide revenue. If you’re regulated, question 10 is a gate. If your stack is homegrown, question 3 will decide your timeline.
Make every vendor answer the same twelve questions in the same order. By the third demo, you will be able to sense which ones were rehearsed.
0–3: Run.
4–8: Worth a pilot.
9–11: A serious contender.
12: Simple.
For years, Omaha Steaks believed 70% of its inbound calls were sales calls. The evidence looked solid: the IVR said \"press one for sales, press two for support,\" and 70% of callers pressed one.
Then an AI agent replaced the phone tree and started asking callers, in plain language, what they needed. The real mix turned out to be only 45 to 50% sales.
A quarter of the traffic had been misclassified for years. The company had built its entire seasonal training program on that number, so thousands of temporary agents were drilled on selling holiday packages, then spent much of December fielding claims and delivery questions they weren't trained for.
Callers don't press buttons carefully. They want a human, and they've learned the fastest route is the first option, or zero, or saying \"representative\" until something gives. Menu order, wording, and how long the hold message runs all shape the numbers, so what the IVR records is escape behavior.
Every downstream decision inherits that error: staffing models, training curricula, skill-group ratios, and the business case for automation all get built on data that measures which button was easiest to press.
When the routing layer understands what the caller actually said, the numbers underneath it move. Misrouted calls carry a transfer cost and usually a repeat-yourself cost, and Omaha Steaks cut both by letting the agent classify intent from the caller's own words. Knowing the real sales-to-service split also lets you train and schedule against it, which matters most in peak season, when every misallocated trainee is money spent on the wrong skill.
Intent captured at conversational resolution shows you what a phone tree can't: which products drive calls, which policies confuse people.
If twenty points of your call mix can hide inside your IVR data, what else is hiding there? Most contact centers can't say, because the phone tree is the only intent instrument they have.
Fixing it doesn't require automating a single conversation. Replacing menu buttons with an agent that asks \"how can I help?\" and routes on the answer is the smallest AI deployment available, and it pays for itself in data quality. Omaha Steaks found a 20-point gap in week one. Yours will be a different number in a different place, and you won't know which until you measure it.
", "url": "https://www.usesimple.ai/blog/why-your-phone-tree-data-is-wrong-about-customer-intent", "title": "Why your phone tree data is wrong about customer intent", "summary": "Omaha Steaks' IVR said 70% of calls were sales. The real number was 45–50%. Why key presses lie, and how intent-based call routing fixes your data.", "date_modified": "2026-08-13T00:08:52.542Z" }, { "id": "urn:sha256:f7688d6d9fa8049309a04fc4511a84ee1a4b3aaf3cbb000f8d1619e15321c995", "content_html": "Two years ago, Omaha Steaks hired 5,000 seasonal agents to get through the holidays. This year, the plan is 692.
That number is the spine of the conversation our CEO Cat Li had on stage at Customer Contact Week in Las Vegas with Grant Young, Director of CEC Operations, and Rob Bradshaw, Quality Manager at Omaha Steaks. The full fireside ran 45 minutes, but we've added a 9-minute highlight reel for the masses.
Omaha Steaks does half its annual revenue in the few weeks around December, when call volume runs about 12x normal. Staffing for that meant hiring in September: recruit 5,000 people to net roughly 2,000 still on the phones by the time peak arrived.
Before Simple, the team spent six months building an IVA in-house on their CCaaS provider's tools. Grant, an analyst, and a group of developers got it to 20% containment on a single use case. It worked well enough to prove the idea and cumbersome enough to prove they shouldn't be the ones building it.
The proof of concept with Simple was signed on June 2. By June 10, the AI was taking real customer calls on a dedicated order line: full orders placed in the system, no human on the line. Containment on day one was 52%. Grant's reaction, verbatim from the stage: \"Holy shit, are we hanging up on customers?\"
Omaha Steaks named the agent Simone, after the Simon family that built the company. Callers hear the AI disclosure in the first sentence or two of the call. A few minutes in, plenty of them forget. They call her ma'am. One customer invited her to his daughter's birthday party.
December 13, right at the peak of peak season. Zach, Simple's CTO, noticed a spike in callers saying the add-to-cart button on the Omaha Steaks website wasn't working, and reached out directly. At holiday volume that bug was worth millions in exposure. Omaha's team had it fixed before their QA vendor had even finished encoding the calls that would eventually have surfaced it. Nobody's contract required that call. That's the point of the story.
Rob's QA team was about 20 people whose jobs the AI would clearly change. Omaha Steaks told them so on day one, then invested in training them toward more analytical work. Two took buyouts. Everyone else is still with the company a year later, in roles built around directing and improving the AI rather than manually reviewing calls.
Call abandonment went from 16% to 9% to 3%. Seasonal hires upsold on 22% of calls; the AI runs at 30%, a 36% higher rate and level with Omaha Steaks' most experienced reps. And seasonal hiring went from 5,000 to 1,500 to a planned 692.
Watch the nine-minute reel above for the story in Grant and Rob's own words. The full 45-minute conversation is here: https://www.youtube.com/watch?v=ZCmXLwly76c
", "url": "https://www.usesimple.ai/blog/key-takeaways-from-omaha-steaks-fireside-chat", "title": "Key takeaways from Omaha Steaks' fireside chat", "summary": "How Omaha Steaks cut seasonal hiring from 5,000 to 692: an 8-day launch, 36% more upsells, and a nine-minute highlight reel from Customer Contact Week.", "date_modified": "2026-08-13T00:08:52.541Z" }, { "id": "urn:sha256:e02f0a91c66ed891590bf0cb7ad24421a8a43f22c86a244386a3f9c6039cbae9", "content_html": "![\"\"](\"https://framerusercontent.com/images/PYqfymUNPRy1NN5uu8ynBL8l7A.png\")
The Bureau of Labor Statistics projects employment of customer service representatives to decline 5% between 2024 and 2034. According to the 2026–27 benchmarking report from CMP Research, the group behind CCW Digital, the frontline customer contact workforce fell below that projected 2034 level in 2025: 2,595,750 people, against a projected 2,673,300 for 2034. More than 218,000 frontline roles disappeared between 2024 and 2025 alone.
The official models gave the industry a decade to absorb this shift. It took about a year.
Numbers like that usually come with a wave of confident technology adoption behind them. CMP found the opposite. When it surveyed contact center executives, improving agentic and generative AI ranked among the most important issues facing their operations and among the hardest to solve. Managing the change of an AI-augmented workforce sat right beside it.
CMP reads the drop as a signal that automation may be changing the workforce faster than anyone projected. The people running contact centers apparently agree, and they still don't trust the current generation of customer-facing AI products to deliver. Every sale in this category has to cross that gap.
CMP's awareness data explains some of the skepticism. The best-known tool in the category is recognized by just 42% of the buyers it's sold to, and awareness of three out of four tools falls under 20%. The takeaway is this: no one understands what vendors are selling. After years of funding and noise, even the highest profile products still draw a blank from most of the market.
Vendors own a share of that fault, according to CMP: buyers and sellers often name the same thing differently. A vendor sells an \"intelligent virtual agent\" or \"agentic voice.\" The buyer is shopping for a \"voicebot\" to replace a tired IVR. One business function has been split across three product categories, so buyers end up guessing, and a buyer who has to guess usually stalls.
Ignore the category names. Evaluate what a system completes on your calls, in your stack. A product that finishes an order or books an appointment is worth paying for whatever its vendor calls it. A product that can't survive a live demo on your hardest call type isn't.
Treat the workforce shift as a management problem you have now. The teams handling it well tell their people early and retrain them toward the analytical work that automation creates.
And hold vendors accountable for the trust gap. The industry's skepticism is earned, which means the burden of proof sits with vendors. Ask for named customers and real numbers, and insist on a pilot measured in days. The vendors who can't produce those are part of the reason awareness tops out at 42%.
The future of the contact center workforce stopped being a forecast. Every planning assumption downstream of that number deserves a fresh look.
", "url": "https://www.usesimple.ai/blog/the-contact-center-workforce-just-hit-its-2034-forecast-nine-years-early", "title": "The Contact Center Workforce Just Hit Its 2034 Forecast, Nine Years Early", "summary": "The contact center workforce fell below its projected 2034 level in 2025 — 218,000 roles gone in a year. What CMP Research's new benchmark means for voice AI buyers.", "date_modified": "2026-08-13T00:08:52.540Z" }, { "id": "urn:sha256:e34967b905d5d1d867cbcdfac4bd5a51e2cf3c6efb49351b46429f1c08f4e2c3", "content_html": "The full 45-minute fireside chat with Omaha Steaks is available to watch on YouTube.
Omaha Steaks has been selling premium steaks and seafood for 109 years. Half of its annual call and chat volume arrives in a single month. From the week before Thanksgiving through mid-December, volume runs 12 times higher than steady state, because everyone wants to send steaks as a holiday gift.
For years, the company solved that spike with bodies. It hired around 5,000 seasonal workers starting each September to end up with roughly 2,000 actually taking calls by December. Training started in mid-September and ran as a revolving door: hire, train, quit, rehire. Supervision stretched from one supervisor per 20 agents to, at the worst point, one per 212.
Grant Young, who runs contact center operations at Omaha Steaks, describes the old holiday season simply: a disaster every year, and a disaster that produced half the company’s revenue. Getting it right was never optional.
Before working with Simple AI, the Omaha Steaks team built its own virtual agent on the tools inside its CCaaS platform. Grant, an analyst, and a group of developers spent six months on it. The model was deterministic, so every path had to be mapped by hand, and a caller saying “my address is” in the wrong spot could throw the whole flow off.
The result was 20% containment on a single use case. Useful, but nowhere near the seasonal problem, and expensive to maintain for a team whose members were not AI specialists.
When Omaha Steaks evaluated vendors, the bar was set by its customer experience standards: the agent had to hold a natural conversation rather than walk a script tree, it had to complete real work end to end, and it had to sound good enough to represent a premium brand.
The team started narrow on purpose. Omaha Steaks runs dedicated phone numbers for its mailers, so a caller on one of those lines is almost certainly ordering the package from the mailer. That system offered Simple a contained first use case that still finished the job: take the order, place it in the order system, no human touch.
Simple flew to Omaha and worked alongside Grant’s team for two weeks. The proof of concept was signed on June 2. First live calls happened June 10. That eight-day window included new APIs on the Omaha Steaks side, since the company runs their business on a homegrown order and CRM stack rather than an off-the-shelf platform.
Day one containment was 52%. Grant’s first reaction was alarm: was the new system was hanging up on customers? (It wasn’t.) Three weeks in, containment held steady at 60%.
From there the rollout followed a waterfall: while one use case moved to production, the next was in configuration. The single-package line expanded to roughly 100 products, searchable by item number or name. A month of testing later, in September, the team turned the agent loose on 100% of traffic.
The agent, named Simone after the founding Simon family, now greets every caller, understands why they’re calling, and either handles the call or routes it with full context. Sales came first. Service calls that need a more careful touch, like claims and delivery problems, are rolling out now.
Containment: 60% across all calls, 70 to 75% on the dedicated sales lines, and 75% on chat.
Abandonment: 16% two years ago, 9% a year ago, 3% today.
Upsell rate: Simone runs an upsell rate of 28% to 30%, compared to the 22% of seasonal agents.
Seasonal hiring: from 5,000 hires down to 1,500 trained last holiday, with 692 planned for this year.
Average order value tells the same story. Callers who complete an order with Simone match the numbers of tenured agents, and because far fewer calls abandon, more calls become orders at all.
Three decisions mattered more than any feature.
First, Omaha Steaks framed the project around revenue rather than deflection. The inbound line is where the company sells, so the goal from day one was completed sales rather than contained calls.
Second, both teams treated it as a partnership. A year in, the teams still meet two to three times a week, and each side has flown to visit the other. Deployment in ten days only happened because Omaha Steaks gave the project engineering resources and decision-makers, and Simple put dedicated agent engineers on their account.
Third, the rollout was incremental. One contained use case, proven end to end, then the next. Nobody just flipped a switch on day one.
If your busiest month is also your most error-prone month, the Omaha Steaks playbook is worth copying: pick the narrowest use case that still finishes a revenue task, prove it in weeks, and expand from there.
", "url": "https://www.usesimple.ai/blog/omaha-steaks-case-study", "title": "How Omaha Steaks turned its inbound line into a sales engine", "summary": "Read how Omaha Steaks' voice AI contact center agent hit over 70% containment, cut call abandonment from 16% to 3%, and dropped seasonal hiring from 5,000 to 692.", "date_modified": "2026-08-13T00:08:52.539Z" }, { "id": "urn:sha256:07835669325ab2a2c630bfd621a76d31c7dc5860f29b8b3a679ff29355f6a564", "content_html": "Customer Contact Week Vegas ran on a contradiction this year. Companies are more open than ever to implementing AI, but are also more skeptical than ever of the solutions on the market. Nowhere was this more apparent than in the voice AI category. According to a CMP survey, 4 out of 5 CX leaders say they're open to investing in voice AI, but only 37% are prioritizing it — a 10% decrease from the previous year.
The exception was on our own stage: Grant Young, Director of CEC Operations at Omaha Steaks and Simple AI customer, shared a containment number most of the audience could only dream of: 75% on their highest-value calls, compared to 20% with the system they used a year ago. Back at the booth, almost every conversation circled the same gap between what voice AI buyers were promised and what most of them got.
Rather than shopping for their first voice AI, the buyers we spoke to were most often looking to replace their existing solution. These companies had bought an early agentic voice product, as a module within a legacy platform or one of the first-generation voice vendors, and the results fell short of expectations.
We heard the same two things all week: hopeful about voice AI, unhappy with their current setup. These buyers have been burned, and they show up expecting the next vendor to miss the nuances of their business too. They won't be won over by another pitch, only by results.
Results are exactly what Omaha Steaks brought to their session.
![\"\"](\"https://framerusercontent.com/images/gU6CvuRSbS2S1C39lPYXWi6mEM.png\")
Grant Young and Rob Bradshaw of Omaha Steaks told their story on stage Thursday. They shared their containment on inbound sales calls: 55% in the first week, around 60% across the board now, and peaking near 75% on certain use cases.
When they dropped those numbers, the room gasped. Grant and Rob also could not believe it when the first numbers using Simple's voice agent came in:
Some of the other contact centers in the room bought voice AI hoping to achieve a fraction of that result. For example, Omaha Steaks' earlier in-house build from Five9's IVA studio had stalled at 20%, and it took six months to get even that far. This time, the timeline looked very different:
What they have now is an agent customers are happy to stay on the line with, one that solves whatever they called about in the first place.
The companies that sell on the phone came looking for one more thing and mostly could not find it: an agent that captures revenue on the call instead of only containing it. Real upselling at volume is still rare across the industry, and it is the clearest place older solutions leave money on the table.
Omaha Steaks put numbers to it on stage:
That puts the Simple on par with their most experienced reps and roughly 30% ahead of the seasonal staff who carry the holiday load. This difference in seasonal performance translates into a huge amount of revenue for a company that does 50% of its business in the month of December alone. On Father's Day, their biggest sales day of the year, the AI outperformed even their steady-state agents on the most expensive upsell.
Grant and Rob also raised an issue that gets less attention than it deserves. When a reliable voice agent takes the highest-volume, most repetitive calls, the work left for your people is the work that needs judgment. Reps handle harder problems and less of the routine grind, and the role starts to look like an engaging career instead of a volume quota. The QA team stops spot-checking a thin sample and starts working from every call, which turns them into the analysts who tell the business what its customers are really saying.
The fear underneath all of this is that automation just means layoffs. At Omaha Steaks, that isn't what happened:
\"We challenged them with additional training … giving them mentored opportunities to show us that they could work in that more analytical sort of capability rather than just checking forms while they listen to calls. They were more engaged because they recognized the investment that we'd made.
And the people that really didn't have the propensity to work in that capacity, we found other places for them.
Of about 20 people impacted, two took buyouts and left. Everybody else is still with the company and still productive a year later.\" — Rob Bradshaw, Quality Manager, Omaha Steaks
Their bottom line improved, and so did the experience of the people doing the work.
One thing was certain by the end of the week. Buyers are done grading voice AI on a curve. They are judging its impact on the metrics they have always cared about—CSAT, self-service, first contact resolution, containment—, and are ready to leave vendors that try to get them to expect less.
To see what this means for your contact center, book a demo at usesimple.ai.
", "url": "https://www.usesimple.ai/blog/ccw-vegas-2026-buyers-are-done-with-mediocre-voice-ai", "title": "CCW Vegas 2026: CX Buyers Are Done With Mediocre AI", "summary": "At CCW Vegas 2026, contact center leaders made it clear they're done grading voice AI on a curve. Omaha Steaks’ containment miracle, for new voice AI standards, and what it all means for buyers.", "date_modified": "2026-08-13T00:08:52.538Z" }, { "id": "urn:sha256:d00e7216cb97055f09849a05be329acb0256e5afd09f0afd1901f66d5ad89a32", "content_html": "Those are the metrics the business judges them on. A function under constant cost scrutiny optimizes for the numbers that keep it funded.
That framing keeps most AI voice agents anchored to cost avoidance and out of the revenue conversation entirely.
It's the wrong scorecard. An inbound contact center is one of the few places a customer chooses to talk to you at the moment they're ready to buy, cancel, or walk. That makes it a revenue engine sitting inside a cost center. The shift that matters is treating it as one: measuring it on the dollars it moves, not the calls it deflects. Get the scorecard right and what you expect from your AI vendor changes with it.
The average inbound call costs $7.20 to handle, per ContactBabel's 2025 US Contact Center Decision-Makers' Guide. Labor accounts for roughly 60-75% of that; the rest goes to infrastructure, QA, routing technology, and management overhead.
At significant volume, the contact center is a seven- or eight-figure line item for a mid-sized company and a nine-figure one at enterprise scale. The business treats it as fixed overhead. Finance knows the number. Operations manages around it.
AI got sold as cost avoidance because it's the easiest ROI to model. Automate 30% of call volume, multiply by $7 per call, and you have a projected savings figure before the meeting ends. The math works without any attribution model or coordination with the CRO.
Vendors built their pitches around it. Contact center leaders bought on that basis. The KPIs in most deployments were designed around it.
Vendors set the bar low, and buyers have had no better alternative. Across the market, the same figures get quoted as good: early deployments run 20-40% containment, reaching 40-70% only once they mature. At the low end, 80% of calls still route to a human. With cost avoidance as the only target, there's no pressure to do better, so the CFO saw savings, the project got renewed, and the AI agent stayed mediocre.
(By the way, our deployments run well above that range.)
Going from 20% to 65-70% containment is a capability change, not just a cost improvement. At 20%, the AI handles routine queries and routes everything else. At 65-70% on a high-value call type, it handles complex, conversational interactions well enough that customers complete their task without asking for a human. Those two agents have different business cases.
Plenty of inbound calls aren't sales. The ones that are (new orders, renewals, cancellations, upsells) carry the revenue, and a deflection-first deployment routes exactly those to a human or loses them to abandonment.
A major national consumer brand is what that looks like in practice. They ran a proof-of-concept with Simple AI and went from signed agreement to live in production in 10 days. Containment hit 50% in week one, 61% by the end of a three-week POC, and 70% on inbound sales, their highest-value call type. Their own in-house build had stalled at 20% after six months. Less than a year in, the program has saved $2.6M and cut queue abandonment from 9% to 2%, and at their December peak each point of abandonment is worth roughly $1M in recovered sales.
Human agents on that same sales line run a 13:1 revenue-to-cost ratio. The AI runs 19 to 22 to 1. That gap comes from two structural advantages no rep can match, however good they are.
Some of that is availability: a call that abandons in queue is a lost sale, and cutting abandonment from 9% to 2% recovers revenue you can put a number on. The rest is consistency: the AI runs the same play on call 50,000 as on call one, including during the holiday surge, when seasonal and temp staff handle most of the volume and upsell performance usually drops off.
A national home-services brand deployed Simple AI for outbound and generated six figures in two days of limited testing. Their operator focused less on the volume than on the range: the AI handled inbound leads, service calls, cancellations, and rescheduling without rigid scripting.
\"Unlike some bots that follow a rigid, straight-line script, this bot can pivot based on the customer's responses. One bot can handle multiple call types, including interested inbound leads, service calls, cancellations, and rescheduling.\"
Agents built to hit 20% containment don't behave this way. The target shapes the design.
In most organizations, contact centers report into Operations or Customer Service, where the mandate is to control cost. Sales and the CRO own the revenue number. When an AI voice agent lifts revenue on inbound calls, that lift lands on someone else's scorecard, not the contact center's.
So the team running the AI optimizes for the one thing they're measured on, which is cost. Anything that carries revenue risk with no revenue upside gets cut from scope. The AI handles low-stakes calls; everything complex or high-value routes to a human.
Marketing makes it harder. One customer told us their marketing team was among the biggest internal adversaries of the rollout, and the reasoning held up: marketing is measured on CAC, they paid to generate those inbound leads, and they didn't want hard-won demand routed to an AI agent that might lose the sale or fail the upsell. A cost-first deployment proves that fear justified; an agent built and measured for revenue is what removes it.
The fix is governance. Operations, Sales, and Marketing need shared accountability for what happens on AI-handled calls. That conversation is harder than buying a platform, and most companies haven't had it. The ones that do end up with an advantage that compounds.
If you're running or evaluating AI voice agents and only tracking containment rate and average handle time, you're measuring inputs. Revenue metrics to add:
Revenue per AI-handled call vs. revenue per human-handled call
Upsell rate across AI agents, trained human agents, and seasonal workers
Conversion rate on AI-handled sales calls
Revenue recovered from calls that previously abandoned in queue
Cost metrics that mean more alongside revenue:
Cost-to-revenue ratio by call path
Queue abandonment rate before and after AI deployment
Seasonal hiring reduction and its effect on annual labor spend
Cost metrics matter, but running them alongside revenue metrics makes the full picture visible to the executives who need to act on it.
The deflection framing skips what happens to human agents when the deployment is genuinely effective.
When AI voice agents handle high-volume, lower-complexity calls, human agents get more complex work: calls that require judgment, situations where experience and empathy matter.
\"The average live call now requires a higher skill level.\"
That's from the customer referenced above. First-year attrition at contact centers runs as high as 69-73%, and most churn happens before an agent is fully productive. Give agents work that requires judgment instead of script-reading, and tenure tends to improve.
The deflection scorecard does more than undersell the contact center. It lets vendors win by clearing a low bar. When 20% containment counts as success, nobody has to build anything better, and plenty of vendors are happy to wrap a mediocre model in a dashboard and call it AI.
That changes the moment a contact center stops measuring itself as a cost to be minimized and starts tracking revenue per call, recovered abandons, and upsell rate. Goals that reflect the revenue moving through the phone lines raise the bar for the vendor too. An agent good enough to trust with a high-value sales call is a different product than one built to deflect, and you get it by demanding it.
The mindset shift comes first: treat the contact center as a revenue engine, hold the AI to that standard, and expect your vendor to clear it. The operators already working this way are pulling ahead, and the gap widens every quarter.
Cat Li is co-founder and CEO of Simple AI, which builds AI voice agents for enterprise contact centers. To see how the revenue math applies to your operation, book a demo at usesimple.ai.
", "url": "https://www.usesimple.ai/blog/stop-counting-deflections-start-counting-dollars", "title": "Stop Counting Deflections. Start Counting Dollars.", "summary": "The contact center AI industry has been selling deflection. That framing is costing you revenue. Here's why AI voice agents should be measured on dollars, not containment stats.", "date_modified": "2026-08-13T00:08:52.537Z" }, { "id": "urn:sha256:c07807239b449ecc8e694c0ed0e0ec3872cb1d0710e67e12a77287c6e4f4db6b", "content_html": "“We just finished a CCaaS migration. We are not doing another one.”
That is a reasonable response. A contact-center migration touches phone numbers, routing, workforce management, recordings, reporting, agent desktops, security reviews, and training. Adding voice AI should not force you to reopen every one of those decisions.
It usually does not have to. You can add AI to an existing contact center as a new call-handling layer. The CCaaS still routes calls to people, manages the workforce, records human conversations, and runs the agent desktop. The AI answers selected calls first, completes the work it is allowed to complete, and sends the rest to an existing queue.
This is an overlay deployment. It changes the path of a call without replacing the platform your agents already use.
Start with a phone number or a defined slice of traffic. The carrier routes those calls to the voice AI agent before they reach the current IVR. The agent greets the caller, identifies the reason for the call, and attempts the approved task.
If the agent can finish the task, it confirms the outcome and ends the call. If the caller asks for a person, the task falls outside the agent’s scope, or a system fails, the agent transfers the call to the appropriate queue in the CCaaS.
The connection can use ordinary telephony components: number forwarding, PSTN transfer, or SIP trunks, depending on the environment. SIP REFER is a standard way to move an active call from one endpoint to another; Twilio documents the transfer flow, including transfers to SIP and PSTN destinations. Existing CCaaS products also support third-party telephony connections. Genesys, for example, documents BYOC options for connecting external carriers and SIP trunks.
The exact design depends on your carrier, CCaaS, geography, compliance requirements, and whether the AI needs to remain on the call during a handoff. None of those choices inherently requires a CCaaS replacement.
A phone transfer moves the caller. It does not automatically move the information collected during the conversation.
To give the human agent useful context, the AI also needs a data path. That might create a CRM activity, add a note to the customer record, populate a screen pop, or send a structured summary to the agent desktop. The handoff can include the caller’s intent, authentication status, information already gathered, actions completed, and the reason for escalation.
This distinction matters during vendor evaluations. A vendor may demonstrate a successful transfer while leaving the human agent blind. Ask to see the full handoff: the call arrives in the right queue, the context appears before the agent answers, and the caller does not repeat the opening five minutes.
Warm transfers need their own failure logic. What happens if the queue is closed, the target does not answer, or the transfer request fails? The AI should tell the caller what happened and follow a defined fallback, such as another queue, a callback request, or voicemail. A dropped call is not an escalation strategy.
Your current platform can keep the jobs it already performs well:
human queue routing and skills-based distribution
workforce management and scheduling
the agent desktop and supervisor tools
recordings and quality workflows for the human leg
existing reports on queue performance, handle time, service level, and agent activity
The AI platform adds a different set of operating data: why people called, which tasks the AI attempted, completion and escalation rates by use case, where conversations failed, and what happened before a transfer.
Do not assume both platforms will show the same interaction from end to end. The CCaaS will usually have detailed telemetry for the transferred leg. The AI platform will have the full AI conversation and transfer outcome. Decide before launch which system owns each metric and how the teams will reconcile call IDs. Otherwise, the first business review turns into an argument about denominators.
This division of labor is consistent with the role of a CCaaS. Gartner describes CCaaS as the platform that orchestrates self-service and employee-assisted engagement across channels. An overlay changes one part of that engagement model without asking you to discard the rest.
The answer depends on the task, not the existence of AI.
A first deployment can run with little or no CRM integration when the caller supplies the necessary information and the outcome can be handed to a person or delivered through an existing channel. Common examples include routing, lead qualification, after-hours intake, basic FAQs, and callbacks.
Read access lets the agent personalize the conversation or check information such as an order status, appointment window, or account detail. Write access lets it finish transactions: book an appointment, update a record, place an order, or process a payment through an approved workflow.
More integration expands what the agent can complete, but it also expands the security review, test surface, and consequences of an error. Start with the minimum access required for a valuable outcome. Add write permissions only after the team has defined validation rules, rollback behavior, audit logs, and human escalation.
This is especially useful in environments with closed or heavily customized systems. A home-services operation can begin with after-hours intake or speed-to-lead while deeper CRM work continues. The launch and the ideal end state do not need to happen on the same day.
Choose one entry point where intent is predictable and success is measurable. A dedicated campaign number, after-hours line, overflow queue, or narrow service flow is easier to test than the main support number.
Omaha Steaks followed that pattern with a dedicated mailer line. Callers were usually ordering the package shown in the mailer, so the first flow was narrow but commercially meaningful. The team proved the transaction end to end, then added products and use cases. The full Omaha Steaks case study covers the rollout and results.
Before the first live call, document five things:
Which calls reach the AI, and how traffic can be returned to the old path.
Which tasks the AI may complete and which require a person.
Where each escalation goes during business hours and after hours.
What context reaches the human agent and where it appears.
Which metrics decide whether the pilot expands, changes, or stops.
Run a small share of traffic first. Review recordings and transcripts daily, then inspect completion, transfer, abandonment, latency, and customer outcomes by intent. An average containment number can hide a broken high-value flow, so segment the results.
Keep a rollback path that the contact-center team can use without waiting for the vendor. If the deployment began by pointing one number or changing one routing rule, reversal should be equally bounded.
Agents keep their desktop, queues, and QA process. The work arriving in those tools changes. Routine calls may disappear; escalations should arrive with more context and a clearer reason for transfer.
That shift affects staffing forecasts and coaching. A lower volume of easy calls can raise average handle time because the remaining calls are harder. It can also change quality scores if supervisors compare the new call mix with the old one. Track complexity and intent alongside handle time so the team does not mistake a healthier queue for declining agent performance.
Supervisors also need visibility into the AI’s queue. Someone should own failed intents, escalation spikes, incorrect actions, and prompt or workflow changes. Adding an AI layer does not remove operational work; it moves part of that work from scheduling people to inspecting automation.
A replacement may be sensible when the CCaaS contract is already ending, the platform cannot support required routing or data access, the telephony vendor is sunsetting a critical product, or the organization wants to consolidate channels and reporting anyway.
Treat that as a platform decision with its own business case and timeline. Bundling it into the AI launch increases scope and makes it harder to tell which change produced the result.
An overlay is also a poor fit if the vendor cannot explain call ownership, failover, data retention, recording responsibilities, emergency routing, and transfer behavior in your environment. “We integrate with everything” is not an architecture.
Ask the vendor to draw the proposed call path using your carrier, numbers, CCaaS queues, CRM, and reporting tools. Then ask them to show the failure paths.
Can we start with one number or a small percentage of traffic?
Who controls routing, rollback, and after-hours behavior?
Does the transfer use SIP, PSTN, or another method?
How does context reach the human agent?
Which system records each leg of the call?
What appears in our existing CCaaS reports?
What can launch before the CRM integration is complete?
Which actions require read or write access?
What happens if the AI platform, an API, or the transfer target is unavailable?
These questions belong beside the broader commercial and operational checks in The CX Leader’s Guide to Voice AI.
You should be able to add voice AI without reopening the entire contact-center stack. A narrow routing change, a clear handoff, and a reversible pilot are enough to learn whether the agent can complete useful work. If a vendor cannot describe the deployment while leaving your CCaaS in place, the migration is part of their product strategy, not a technical requirement of voice AI.
", "url": "https://www.usesimple.ai/blog/add-voice-ai-without-replacing-ccaas", "title": "How to Add Voice AI Without Replacing Your CCaaS", "summary": "Add voice AI to your existing contact center without replacing your CCaaS. See how overlay routing, contextual handoffs, integrations, reporting, and rollback work.", "date_modified": "2026-08-13T00:08:52.536Z" }, { "id": "urn:sha256:4fa9f7a2cb8fd2963a03066da8b38dbd312fa105e6f94694461bb9af9d589cda", "content_html": "We will also be at NiCE World Orlando June 8-10 to introduce ourselves to the NiCE ecosystem. More details can be found here if you want to meet with us and learn about our cutting edge solution.
NiCE CXone is the contact center platform for enterprise operations worldwide. DEVone is how AI companies build into it and reach CXone customers through the CXexchange marketplace.
Simple AI will be listed on CXexchange. If your contact center runs CXone, you will soon be able to ad our agents seamlessly without any changes to your existing infrastructure.
Simple AI agents handle conversations from start to finish without scripts or decision trees. They carry context across the full call and across the customer's entire history with your business. When a caller mentions their freezer is full, that doesn't get lost. The next time a relevant promotion runs, they hear about it. Outreach triggers on what customers actually say, not on demographic buckets or purchase history.
Human agents get the same intelligence. Agent Assist surfaces the right context in real time, so your reps handle calls without holding callers to find an answer or loop in a supervisor. 100% of calls get reviewed and scored. Your QA team stops pulling clips and starts acting on what the data shows.
We're not live on the store yet, but we'll be at NiCE World August 8–10. Come find the team for a live demo of our latest voice agent.
More details at usesimple.ai/nice-world-2026
", "url": "https://www.usesimple.ai/blog/simple-ai-joins-the-nice-devone-partner-program", "title": "Simple AI Joins the NiCE DEVone Partner Program", "summary": "Simple AI is now a NiCE DEVone partner. With this change, CXone customers will be able to add our AI agents to their existing setup without changing their infrastructure.", "date_modified": "2026-08-13T00:08:52.535Z" } ] }