There has never been more confidence in AI's potential to transform the contact center. Gartner estimates that by 2026, conversational AI deployments will reduce contact center agent labor costs by $80 billion globally, and by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention. With predictions like this, it is no wonder investments in the technology are surging.
And yet the results are not matching the ambition. Implementations that looked compelling on paper are stalling in production. ROI cases that seemed straightforward at the pitch stage are proving difficult to quantify once the technology is live. In the worst cases, the customers organizations set out to serve are beginning to notice declines in quality.
Something is going wrong. But understanding what it is requires looking beyond the technology and beyond the assumption that trying harder will fix it.
The Pressure to Deploy Is Part of the Problem
In many organizations, the directive to deploy AI is arriving from boards and executive teams responding to competitive pressure and the sense that standing still is no longer an option. But there is a critical difference between deploying AI because leadership expects it and deploying AI to solve a specific, well-defined operational problem.
"Sometimes AI gets put out because there's a push from above to use AI and not a focus on a problem to be solved," says Chris Scimone, Director, Solutions Architecture & Engineering at New Era Technology. "And that can make it a little tricky to justify the ROI once those questions inevitably follow."
Gartner's April 2026 survey found only 28% of AI use cases fully succeed and meet ROI expectations, with 20% failing outright. Looking at the reasons behind this, Gartner executives found the 20% failure rate is largely driven by AI initiatives that are either overly ambitious or poorly scoped.
The organizations seeing early, sustained traction tend to share one trait: they started smaller and more deliberately, identifying a contained process where AI could prove its value before expanding further.
What Getting This Right Actually Requires
Identifying which process to start with, and knowing whether it is genuinely the right one, is a decision most organizations cannot afford to get wrong. But for many, they lack the depth of experience to know exactly how to get it right.
Often, this means the deployment is compromised before it goes live. The foundational requirements, clean data, integrated systems, and aligned teams working toward a shared objective, are treated as prerequisites to address later rather than conditions that determine success from the outset.
For those that do find the right starting point, it increases the chances of success. But that is only the first challenge. What comes next is less exciting, and, according to Scimone, just as easy to get wrong.
"Companies approach AI with excitement about the feature and then they're hit with the reality of the boring stuff, like the process, the methodology. How do we validate? How do we test?"
Without those disciplines, even the most carefully chosen starting point will drift.
