Contact Center Leaders Flag Four Core AI Implementation Pitfalls

The reality check on contact center AI

Customer service leaders broadly agree that AI will transform their operations, but the path from investment to value remains riddled with obstacles. At Customer Contact Week Las Vegas this week, four executives and analysts outlined distinct implementation challenges their organizations face—and the strategies that help teams navigate them.

The consensus: AI's promise is real, but success demands more than technology purchases. It requires executive alignment, employee support, clean data infrastructure, and careful rollout planning.

Why it matters

Contact centers represent a high-volume, high-stakes testing ground for enterprise AI. The challenges surfacing here—misaligned expectations between technical teams and leadership, worker burnout from rapid change, inadequate data foundations—mirror issues playing out across industries. How customer service organizations solve these problems will inform AI deployment strategies well beyond the contact center.

When C-suite vision loses touch with reality

Jessica Gupta, COO at InfoPay, emphasized that AI education cannot stop at the frontline. Senior executives need equal grounding in what the technology can and cannot accomplish.

"The higher you go in the C-suite, sometimes the less connection you have to the practical application of a tool," Gupta explained. "That's true of literally every tool, and so really working together to educate, to be on the same page, and to understand what you're going to do."

CX teams struggle to communicate realistic timelines and explain how AI changes costs and customer journeys. Without shared understanding, organizations risk investing in use cases that fail or require expensive rework. Gupta stressed that open communication helps teams identify when risk is too high or implementation simply isn't feasible.

AI amplifying existing worker fatigue

Neville Letzerich, CMO at Talkdesk, pointed to a different pressure point: employee burnout. Organizations have asked workers to do more with less since the pandemic, and AI has intensified rather than relieved that strain.

"People are feeling overwhelmed, feeling left behind," Letzerich said. "Even for people who love AI, trying to stay on top of it can be a 24-hours-a-day job."

The solution requires setting boundaries not just for the AI itself but for how teams will adopt it. Leadership must be transparent about workload expectations, implementation scope, and what remains off-limits for now. Letzerich warned that companies rushing to deploy AI everywhere risk organizational collapse: "I think you're going to see companies blow apart when they're just trying to throw AI at everything and go so fast that they lose their way."

The unglamorous data governance problem

Nicole Kyle, managing director and co-founder of CMP Research, identified a more fundamental barrier: most contact center organizations lack properly structured data to support AI tools.

"It sounds so boring, but I think data governance is so important and knowledge management is so important," Kyle said. "Those are not the sexy topics in CX operations and customer service, but they are the root cause of slow time to ROI and poor customer experiences with new tech investments."

The issue extends beyond customer service. Sales, marketing, and other functions all generate data that needs coordination. Kyle noted that getting these teams aligned is difficult but represents a genuine opportunity to mature the contact center function.

Overcoming deployment anxiety

John Finch, global VP of product marketing at RingCentral, addressed a simpler but significant hurdle: many companies aren't yet comfortable putting AI in front of customers.

Finch recommended internal proof-of-concept phases before making AI "the front door of their organization." The fear is understandable—turning on live AI and potentially losing calls, sales, or inquiries creates real business risk.

"Turning something on live and handing it over, and suddenly not having calls come through, not having sales come through, and not having inquiries come through is a little bit scary for some people," Finch said.

His advice: develop end-to-end blueprints that map customer journeys and expectations before activation. Understanding where AI fits helps teams deploy with confidence rather than anxiety.

These insights were shared at Customer Contact Week Las Vegas and first reported by CX Dive.