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.
The complexity does not ease once the deployment is live. Responsible AI requires continuous testing, structured monitoring, and clear processes for identifying when something starts to go wrong.
That discipline is made harder by the reality of most organizations’ technology stacks. AI does not sit in isolation. It sits inside an environment of CCaaS platforms, CRMs, and integrations that each evolve independently, and a change in one can quietly break something in another.
Without the expertise to monitor across that complexity, organizations have no reliable way of knowing whether their AI is still performing as intended or what it is costing them to do so, and by the time either problem surfaces it has often been compounding for weeks.
The Business Cost of Getting This Wrong
When AI underperforms in the contact center, customers experience it directly. Research from PwC found that 32% of customers will walk away from a brand they love after a single bad experience, and in a contact center environment, tolerance for AI-generated friction is particularly low.
“The worst of it is if you deploy something and it doesn’t work, not only are you not getting the business outcome, you’re impacting your customer base in a way that drives churn,” Scimone says.
“Churn is not only a customer that you lost, it’s a customer that your competitors gained.”
A poorly implemented AI deployment does not just fail to create value. It actively destroys it. The organizations carrying the heaviest cost are not always those that moved carelessly. Many moved thoughtfully, but without the depth of knowledge and operational focus needed to sustain what they built.
Most Organizations Cannot Do This Alone
What they were missing was not effort. It was the kind of accumulated knowledge that only comes from having built, monitored, and recovered these environments repeatedly, across different industries, different tech stacks, and different points of failure. That knowledge does not arrive with the software, or with a vendor implementation guide. It exists in the people who have done it before.
The difference between a contact center AI deployment that delivers and one that quietly drains value is rarely visible at the point of decision. It shows up months later, in a knowledge base that has drifted, a cost line that nobody anticipated, or a customer experience that has gradually degraded without anyone noticing why.
Understanding that is the beginning. Knowing where to find it is the next step.
