Every contact center leader wants to be part of the 5% AI pilots that make it into production and deliver meaningful value. But most organizations make the same mistake again and again; they throw more and more technology at their problems, hoping it will improve customer experience.
An Avaya study by Forrester Consulting indicates that nearly half of organizations adopt AI in the contact center for financial reasons alone, turning it into a short-term fix rather than a long-term strategy.
These AI pilots are often launched without a clear purpose; no defined objective, no shared definition of success, and no plan for capturing what’s learned. Instead of being treated as a capability that reshapes how work gets done, AI is introduced as just another tool layered into an already complex environment.
The results? Agents revert to manual processes due to lack of trust and struggle to incorporate AI into their workflows. Customers encounter inconsistent, confusing experiences, and instead of improving efficiency, the experiences slow down.
This is where most AI pilots stall. They fail due to brittle workflows, lack of contextual learning, and zero alignment with day-to-day operations.
So how do you actually use AI to its full potential—and drive measurable outcomes in CX?
Many AI pilots are flawed from the start.
As Martin Hill-Wilson points out, one of the primary reasons why AI pilots are failing is due to the “Technology push”. This refers to a technology-first approach where AI is introduced before a clear problem or use case exists. Instead of defining a challenge, many organizations are left to justify the technology after the fact.
Research from the RAND Corporation identifies a lack of alignment between AI initiatives and core business objectives as one of the leading causes of failure. In many cases, the strategic fit was never there to begin with.
The structural issues run deeper, though. RAND also finds that organizations often lack the data required to effectively train AI models, or the infrastructure needed to manage, deploy, or scale them.
Similarly, there’s a tendency to chase the latest technological trends rather than focus on the actual problem. Issues that are too complex for AI to solve or that require more human-driven solutions get AI thrown at them, and nothing gets fixed.
And when these misaligned pilots are deployed into already fragmented workflows and journeys, the gap between potential and reality only widens.
A successful AI pilot can be misleading. It often shows strong performance in a controlled environment. But once deployed into real operations, business metrics barely move.
Why? Because the system built around it wasn’t designed to support it.
The EY 2025 Work Reimagined Survey found that while 88% of employees use AI at work, only 5% use it in ways that fundamentally reshape how they operate, leading to 40% of potential productivity gains unrealized.
There are four common reasons AI pilots fail to drive real business value:
AI pilots often create an illusion of progress because they prove the technology can perform. But impact will only occur when AI is embedded into the full customer journey, where decisions, context, and human interactions come together.
The small percentage of successful pilots follow a different pattern: a “problem pull” approach. These organizations define a single pain point, establish a baseline that allows for before-and-after performance comparison, and align on what success looks like before AI is introduced.
As noted in research from MIT NANDA, the framework is simple: “Pick one pain point, execute well, and partner smartly.”
But execution can’t happen in isolation, because even when AI is great, there is one thing it can’t fix: a fragmented customer journey. If your CX is reactive, disconnected, or inconsistent, no amount of AI deployment will make it successful. The only path forward is to redesign the journey itself.
The customer experience journey isn’t just a series of touchpoints; it’s the end-to-end path a customer takes to achieve a goal; from the moment a need arises to the moment it’s resolved.
It typically moves through 4 stages: awareness, consideration, decision, and action. In a well-designed journey, each stage feels seamless, context-aware, and effortless. In a broken one, it feels like starting over Every. Single. Time.
Think of a simple example: you visit your favorite shoe brand’s website, browse a pair you like, and leave. A few days later, you return, and the experience picks up exactly where you left off. The system remembers your interest, brings you back to the same product, and makes it easy to complete your purchase. There’s no need to search again, retrace your steps, or rebuild intent. Just checkout and order.
That’s what a well-designed journey does; it reduces effort and keeps momentum moving forward.
If we compare that to a broken journey, customers will be forced to repeat themselves and navigate multiple disconnected touchpoints, putting extra effort into resolving a single issue. Routing would be based on availability rather than intent. Channels would be disconnected, leaving agents without context from previous interactions. And agents are left to fill the gaps, handling avoidable escalations and carrying the emotional weight of broken processes.
In a broken journey like that, even the best technology will struggle to deliver meaningful impact.
To move beyond experimentation, AI pilots must follow a problem-pull approach and be evaluated across a broader set of dimensions.
Guidance from Google Cloud emphasizes three key factors to anchor metrics around:
Organizations must rethink AI, not as a tool, but as a capability embedded across the entire customer journey. Once you recognize that AI reshapes how work gets done, it becomes clear that governance must be built in from the start. That means designing for accountability, establishing performance management, and maintaining human oversight.
Ultimately, AI isn’t something layered on top of CX; it’s what shapes how the experience works.
At its best, AI will operate in three core areas:
When these capabilities are integrated into the journey, AI reduces friction, accelerates resolution, and creates more seamless, connected experiences.
So, if you want to reshape your CX journey, start by aligning workflows with AI, empowering your employees, and building governance in from day one. Measure success by well-defined outcomes, not activity. Treat AI as an ongoing discipline, one that evolves, learns, and improves over time.
Because AI doesn’t drive transformation on its own; it progresses alongside your organization, and the customer journeys you create.
Ready to keep learning about the impact of AI?