Seventy-nine percent of contact centers use AI, but only 22% measure its results.
Here’s what CX leaders shared about the gap between adopting AI and seeing real benefits.
Nearly all contact center leaders say they use AI, but few can explain the results it delivers.
COPC’s 2026 global study found that the real problem is a lack of information. To understand how AI is used in customer care, we spoke with over 200 contact center and CX leaders from various industries. Seventy-nine percent already use AI, and only 6% have no plans to adopt it. So, the adoption debate is settled.
But measuring AI’s impact is still new. Fifty-two percent track AI resolution rates; 41% monitor escalations to humans, and just 22% directly measure AI’s return on investment.
When asked what would speed up AI adoption, 59% of leaders said proven return on investment mattered most, more than integration tools, governance, or training.
The main driver for the next wave of investment is something most companies still can’t deliver.
Adoption has happened, but integrating AI into daily work is still in progress.
A key trend stands out: among organizations using AI, 61% are expanding or upgrading their systems. Some want better features, while others are rethinking earlier choices. Most are not sticking with their original setup.
Features once seen as special are now basic needs. Chatbots, knowledge management, and sentiment analytics are standard, with adoption rates over 80%. Currently, 51% of organizations have a dedicated AI team or center of excellence. This shows AI is now a core business function with clear ownership: 54% say this, followed by 45% citing integration capability and 43% mentioning cost. It’s positive that customers prefer reliable options over cheaper ones. Still, it’s concerning that 23% of organizations don’t know their annual AI spending for customer care.
You can’t call it real governance without financial transparency. Right now, AI is bought with controls that most organizations wouldn’t accept for other enterprise systems. Generative AI makes up 73%; predictive AI 51%, and agentic AI (systems that handle multi-step tasks without human help) accounts for 42%. One agentic pilot is enough for a company to say yes to using it. This number reflects deployment, not how much customer care work agentic AI does.
Most organizations have started with limited, specific uses of agentic AI. The main question now is how much work it handles and whether it’s managed and monitored like other production systems.
Agentic work is reviewed in five areas: attracting and developing talent, helping during live interactions, empowering customers, using data and insights, and handling security and operations. Agentic AI looks different across areas, so combining them into a single percentage hides where real adoption is happening and where it isn’t. It was a positive return on investment, but the results were very different.
Forty-five percent of organizations have AI leaders focused on customers, 43% on operations, and 40% on talent management. For workforce management, it’s 37%; analytics, 34%; and agent assist, 33%. Meanwhile, 42% think it’s too soon to judge the results. This makes sense, as containment rates, deflection volumes, and handle times all vary. Even if agent assist works perfectly, it can still be hard to prove.
There’s a clear pattern: when building your business case, sort ROI by functional area and time in use.
| Functional Area | Year 1 | 2+ Years |
| Workforce management | 19% | 67% |
| Talent management | 31% | 69% |
| Customer-facing | 36% | 56% |
ROI for workforce management more than triples after two years, and for talent management, it more than doubles. This matches what we’ve seen with our clients. Forecasting, scheduling, and real-time models need historical data and fine-tuning, and the benefits only show up when these models are part of the planning process.
Keep in mind two things: the two-year results for workforce and talent management are based on smaller samples, so treat the multipliers as general trends, not exact numbers. Also, don’t set a single ROI target for all AI projects. Expect customer-facing tools to show returns in the first year, but workforce and talent management tools may take longer. Review your targets each year as you get more data. While most leaders believe AI matches or outperforms humans in interactions, in 64% of cases, less than 40% of interactions are handled autonomously.
At first, this might seem contradictory, but it’s intentional. Companies give AI the tasks it does best, while people handle work that needs judgment, empathy, or exception handling. There’s no single right level of autonomous handling; it depends on risk, regulations, and the cost of mistakes. The right balance is when AI takes on suitable tasks, and routing and escalation are reviewed from both the AI and human perspectives. Both AI and human performance should be measured together, not in separate, competing ratings.
The workforce impact is more positive than most headlines suggest.
Thirty-three percent of organizations have seen employee satisfaction rise after introducing AI, while only 4%have seen a decrease. That’s an eight-to-one ratio rise after introducing AI, while only 4% have seen a decrease.
Thirty-five percent of organizations have reduced their workforce, but most cuts are small. About 20% reduced staff by less than 10%. Another 18% moved employees into more valuable roles, and large-scale job losses remain rare.
Seventy-six percent of leaders view AI’s impact on the contact center industry as positive, which contrasts with what outside commentators often claim.
Organizations that see the biggest gains in satisfaction use AI to reorganize their workforce, not just cut jobs. They put freed-up resources into more valuable work, invest in reskilling, and clearly explain job changes. If they skip these steps, employees see AI as a threat and resist it. For genuine and substantial cuts, you should be honest about that; attempting to make it seem as though it’s just an augmentation does more harm to trust than the cuts do.
Start by addressing the measurement gap.
In short, you can’t manage or measure AI effectively if you only look at deflection rates or CSAT. That alone won’t make a real difference.
We recommend measuring AI at three levels. For outcome quality, check whether the problem was solved using criteria such as confirmed resolution, repeat contacts within seven days, and how escalations are handled. Process quality means reviewing each step of AI’s decisions, such as identifying intent, following the decision path, and checking confidence in any policy changes. Oversight includes human checks such as sampling edge cases, reviewing handoff quality, and spotting model drift.
Before proceeding with any of the following steps, complete the one that most programs fail to do. Set your behavior policy before deploying AI. Clearly define what your AI can and should do, build this into the system, and use measurement to keep it within those limits. The quality of the process at each stage of decision-making is frequently overlooked. While outcome metrics indicate what has taken place, process quality tells you whether your AI is reasoning as it should, serving as the sole early warning sign.
Over the next 24 months, success in CX won’t depend on who buys the most AI, but on who can show what their AI is actually doing.
If you need help building that measurement layer, COPC Inc.’s CX consulting team works with organizations to design AI governance and performance frameworks that hold up under scrutiny.
This COPC Inc. research is based on responses from 219 CX and contact center leaders worldwide. For questions about this research, email [email protected].
