Artificial intelligence has become the centerpiece of modern contact center strategy. From agent assist to conversational bots, leaders are racing to deploy AI in the name of efficiency, cost reduction, and improved customer experience. The pressure is real and intensifying.
But despite the urgency, many AI initiatives are quietly falling short. Not because the technology isn't capable, but because leaders are repeating the same foundational mistakes. The result? Low adoption, disappointing ROI, and in some cases, the need to undo workforce decisions made too quickly.
Based on recent research into AI-enabled contact centers, three mistakes consistently separate organizations realizing value from those stuck in pilot purgatory:
Mistake #1: Assuming Adoption Will Take Care of Itself
Contact center leaders often believe that if AI tools are powerful enough, agents will naturally embrace them. In practice, the opposite is happening. Fewer than half of agents say they trust the systems they are asked to use, and only slightly more trust the accuracy of the information those systems provide. When trust is low, usage is inconsistent, and unused AI delivers exactly zero value.
Adoption breaks down when tools are layered onto broken workflows, introduced without sufficient training, or deployed without agent input. Many organizations still treat adoption as a downstream issue rather than a core design requirement.
The fix: Leaders must treat adoption as a business outcome, not a change-management afterthought. That means involving agents in tool design, identifying friction in day-to-day workflows, and using performance and sentiment data to guide improvements. AI should reduce effort and cognitive load, not add to it.
Mistake #2: Hiring Technologists While Ignoring Translators
When AI struggles to deliver results, many organizations respond by hiring more technical talent. But technical expertise alone does not bridge the gap between AI capability and front-line reality.
What's missing is a role that understands both sides of the equation: how work actually gets done and how AI should be applied to improve it. That role is often referred to as the business solution architect (BSA)—a process expert who can redesign workflows, guide responsible automation, and ensure AI supports real operational goals. Without this capability, organizations risk fragmented deployments, overautomation, and tools optimized in isolation rather than in context.
This gap helps explain why so many AI investments fail to pay off. Productivity gains disappear when workflows aren't redesigned, coordination costs rise, and agents are left to compensate for poor system integration.
The fix: Successful contact centers invest in people who can translate strategy into execution. These enablement leaders ensure AI is applied deliberately, aligned with business priorities, and embedded into how work actually happens.
Mistake #3: Measuring the Future With Yesterday's Metrics
Perhaps the most damaging mistake is continuing to manage AI-enabled teams with pre-AI performance metrics.
Average handle time and after-call work still dominate scorecards, even as leaders say they want agents to handle more complex, value-added interactions. The contradiction is obvious: Agents are encouraged to do more and then penalized for taking longer to do it.
Outdated metrics make AI impact invisible. They fail to capture how effectively agents collaborate with AI, whether self-service is being promoted appropriately, or how customer value is being created during interactions.
The fix: Performance measurement must evolve alongside the workforce. Leading organizations supplement traditional metrics with measures such as AI utilization, quality of human–AI collaboration, digital adoption, resolution confidence, and customer value delivered. These metrics not only reveal ROI, they guide coaching, process improvement, and smarter investment decisions.
The Bottom Line for Leaders
AI will not transform the contact center on its own. Technology is only the accelerant. The real drivers of success are adoption, role clarity, and measurement discipline. Leaders who slow down long enough to redesign workflows, invest in the right capabilities, and update how success is measured will realize AI's promise. Those who don't might find themselves with expensive tools, frustrated agents, and customers who never experience the benefits AI was supposed to deliver.
The future of the contact center is not AI replacing people. It's people and AI working better together by design.
Kathy Ross is a senior director analyst in the Gartner Customer Service & Support Practice. Emily Potosky is a senior principal for research and advisory at Gartner.