A customer calls with a problem that has already consumed part of the morning. The agent listens, opens a search window and starts hunting: the policy page, the account history, the exception that may or may not apply. Somewhere, a clock is running. So is the customer’s patience.
Now replay the same call with a quiet change. The conversation is transcribed as it happens. Relevant knowledge appears without a scavenger hunt. A prompt recommends the next best action. Sentiment flags the moment frustration begins to rise. When the call ends, the summary is drafted. The agent is still the person doing the listening, deciding and reassuring. But the empty seconds—the small delays that accumulate into hold time, repeat calls and overtime—have begun to disappear.
That is the practical promise behind AI-assisted agent performance. It is also where marketing language can outrun evidence. So the more useful question is not whether an AI demo looks impressive. It is whether contact centers using these capabilities improve differently from those that do not. Aberdeen designed a comparison that gets much closer to that question.
The comparison
Two Groups, One Scoreboard
In June 2020, Aberdeen surveyed 307 contact-center leaders at companies of different sizes and across industries around the world. It separated respondents into contact centers using AI capabilities and non-users, then compared their year-over-year changes. The definition was broad but operational: artificial intelligence, machine learning, prescriptive guidance, predictive analytics and automation used to analyze interactions or guide work.
This matters because the figures are not a UJET customer sample and they are not UJET’s own performance claims. They come from third-party Aberdeen research. Nor are they a randomized experiment proving that a single tool caused every point of improvement. They are an observed two-group performance comparison. That makes the right reading both more disciplined and more interesting: organizations using AI capabilities were improving markedly faster on the measures contact-center leaders care about.
The headline number is 11.5x. AI users reported a 4.6% annual improvement—a decrease—in average cost per customer contact. Non-users reported 0.4%. This is not a claim that an assisted center costs one-eleventh as much to run. It is a comparison of the pace of improvement, and the pace was eleven and a half times greater.
The Aberdeen gap
AI users vs. non-users
Four Numbers That Change the Conversation
The cost result is the attention-grabber, but the pattern is broader. Customer satisfaction improved 10.1% among AI users and 2.9% among non-users: a 3.5x gap. First-contact resolution rose 6.3% versus 2.8%, or 2.3x. Agent productivity improved 7.4% versus 3.1%, or 2.4x. Average cost per contact improved 4.6% versus 0.4%, producing the 11.5x difference. Every figure is a year-over-year rate of change reported in Aberdeen’s study.
Put them side by side and a chain appears. An agent who finds the right information sooner can resolve more issues on the first attempt. Fewer repeat contacts mean less demand flowing back into the queue. Lower demand releases capacity. Customers spend less effort explaining the same problem again. What begins as help inside one conversation can show up later as productivity, satisfaction and cost performance.
There is a reason to resist treating each bar as a separate victory. Contact-center metrics share plumbing. A missed resolution today becomes tomorrow’s inbound volume; extra volume stretches response times; longer waits erode satisfaction; pressure sends supervisors from coaching into firefighting. Reverse the sequence and one well-supported interaction can remove several downstream costs. The ratios do not prove that exact path in every center, but they reveal the shape of a system moving together rather than four unrelated scorecards flashing green.
Aberdeen found the same direction elsewhere. AI users recorded 3.3x greater annual improvement in customer retention and 8x greater improvement in customer effort scores. Service-level agreement attainment improved 4.5% among users and 1.0% among non-users. The study is not a tale of one isolated KPI. It is a cluster of related gains.
“What begins as help inside one conversation can show up later as productivity, satisfaction and cost performance.”UJET Newsroom
The Hidden Tax of Looking Back
Traditional quality assurance is built around hindsight. A supervisor samples a fraction of recordings, scores them and coaches the agent days or weeks later. The method can find problems, but only after customers have experienced them. Aberdeen noted that periodic reviews often happen monthly or quarterly. A three-month learning loop is a long time to repeat a preventable mistake.
Volume creates another blind spot. Human reviewers cannot listen to every call and read every chat, so they sample. A sample may be representative; it may also miss the new issue spreading quietly through the queue. AI changes the unit of analysis. In Aberdeen’s account, machine learning and automation can examine 100% of interaction data in real time, surface patterns and turn those findings into contextual guidance while an agent can still act.
The labor math is equally revealing. Aberdeen found agents spent an average 14% of their time searching for information needed to help customers. That is nearly one hour and seven minutes in an eight-hour day. Reduce even part of that search, and productivity gains stop looking mysterious. They look like recovered minutes. The old contact center studies yesterday. The assisted one can change the conversation now.
Assistance Is Not Autopilot
The most important word in agent assist is not artificial. It is assist. Customer service is full of judgment calls that do not fit neatly into a script: when to slow down, when to apologize, when a policy should bend and when a vulnerable customer needs a human being to take ownership. Aberdeen’s report specifically points to soft skills such as active listening, empathy and ownership as drivers of satisfaction.
The machine’s useful role is narrower and more concrete. It can transcribe the exchange, retrieve relevant knowledge, suggest a response, detect sentiment, recommend an action and prepare a summary. UJET describes this current category in similar operational terms: real-time transcription, smart replies, knowledge, coaching, sentiment analysis and summarization. Those are ways to reduce cognitive clutter around the agent, not reasons to erase the agent from the scene.
That distinction also explains the supervisor result. Contact centers using AI reported a 2.9% annual improvement in time supervisors spent assisting agents, while non-users worsened by 0.1%. A supervisor with wider, faster visibility can reserve one-to-one attention for the moments that actually require experience. The software handles repetition; the manager handles nuance.
“The software handles repetition; the manager handles nuance.”UJET Newsroom
What to Ask Before You Buy
A large multiplier can tempt a buyer to start with the product. The research suggests starting with the workflow. Where do agents lose time? Which questions trigger repeat contact? How much of quality assurance is based on a thin sample? How quickly does a discovered problem become guidance at the desktop? A system should be judged by whether it closes those loops.
The cleanest evaluation mirrors Aberdeen’s scoreboard. Establish a baseline for customer satisfaction, first-contact resolution, agent productivity and average cost per contact. Define each metric before deployment. Compare year-over-year change, not a flattering snapshot. Segment results by queue and issue type. Then examine whether guidance is accurate, whether agents use it and whether faster work preserves the human qualities customers notice.
The lesson in Aberdeen’s data is not that four ratios guarantee a result. It is that assistance changes the speed of learning. A center that sees more interactions, recognizes trouble earlier and brings the right knowledge into the live moment has more chances to improve before the next scorecard arrives. That is how a few reclaimed seconds become a resolved call, how a resolved call avoids a repeat, and how thousands of avoided repeats begin to look like 11.5x.
Read the research
The performance figures come from Aberdeen’s The ROI of Real-Time Agent Guidance. Explore how UJET approaches AI-powered contact-center work.