Most customer experience (CX) organizations are no longer experimenting with artificial intelligence (AI). It is embedded in contact center workflows, powering self-service tools, and supporting agents in real time. The question now is how effectively it is performing once it is in place.
Across industries, CX leaders are now facing a familiar tension: strong momentum in adoption, uneven results in execution. AI is embedded in more contact center workflows than ever before, yet achieving consistent, enterprise-wide impact remains difficult. The technology is advancing faster than most operating models can absorb it.
According to our 2026 CX Leaders Trends & Insights: Corporate Edition Report, two-thirds of contact centers now operate with some form of AI in their workflows, more than double the rate in 2022. But only 7% of CX leaders describe themselves as very effectively meeting their AI objectives. Nearly half say they are only somewhat effective.
That gap tells its own story. This is a question of translation, moving from isolated use cases to reliable, repeatable performance inside complex service environments.
Turning AI into Operational Impact
The reality is that AI in CX is no longer a pilot conversation. It is an operational one. And in operational terms, success depends less on what the technology can do in theory and more on how it behaves under real-world constraints: legacy systems, fragmented data, shifting customer expectations, and frontline teams who must integrate new tools without losing service quality.
The same research underscores the tension:
- 67% of contact centers now use AI (up from 31% in 2022)
- 49% cite customer frustration with AI quality as a growing concern
- AI virtual assistants still resolve only ~60% of interactions, compared with 87% for phone-based service
These are not signs that AI is failing. They are signals that scale is harder than adoption. What separates progress from stagnation is rarely the model or platform itself. It is the clarity of the use case, the discipline of implementation, and the willingness to confront what happens after deployment when customers begin to interact with the system in unpredictable ways.
In many organizations, early AI wins are real but isolated. A reduction in handle time here, a deflection improvement there, or a successful pilot that performs well under controlled conditions. The challenge emerges when leaders try to extend those results across channels, geographies, and volumes without losing performance and customer trust.
That is where the conversation is shifting now: from “what can AI do?” to “what does it take to make it reliable and scalable?”
From AI Possibility to AI Proof
This is precisely the value of examining AI through structured use cases, not as abstract innovation stories, but as operational decisions. What problem was being solved? What trade-offs were made in design? What changed in the workflow? And what happened when the system met real customer behavior?
Our upcoming event, From Investment to Impact: Rapid-Fire AI Use Cases for Customer Experience, is built around that premise. It brings together real implementations from across CX environments, each grounded in a defined business challenge, a specific execution approach, and measurable outcomes.
Facilitated by Chad McDaniel, President of Execs In The Know, the format is intentionally direct. Solution providers present concrete use cases, and CX leaders respond in real time with questions grounded in their own operational realities: what works, where it works, and why.
For leaders navigating AI strategy today, the value is not in another overview of what is possible. It is in seeing what is already working under real constraints, and understanding what it actually takes to replicate it.
This session is exclusively for end-user corporate CX leaders responsible for customer experience strategy, operations, and technology decisions.
If you are a vendor interested in participating as a solution provider, please contact Scott Moberly at [email protected].
