Contact Center AI Risks Rise When Speed Outpaces Strategy

Contact center leaders face mounting pressure to deploy AI, but field research from UJET suggests that speed without strategy is creating new operational risks rather than solving existing problems.

Kristin King, Chief Customer Officer at UJET, and Tena Curic, Senior Customer Success Manager, shared findings from recent customer site visits where they observed agents, supervisors, and workflows firsthand. Their conclusion: many organizations are rushing into AI adoption before establishing the foundational elements needed for success.

Why it matters

Boards and competitors are driving AI expectations, but contact centers that deploy tools before defining outcomes, cleaning data, or involving frontline teams risk adding complexity instead of removing friction. The gap between executive pressure and operational readiness is widening, and poorly implemented AI can damage both customer and agent experiences.

The pressure to move fast

King described a familiar pattern across customer conversations: leadership teams feel compelled to "do AI" because of board expectations, competitive moves, and market momentum. However, this urgency often precedes clarity about which business problem AI should solve.

"They're getting pressure from the board, they're getting pressure from competitors, they're getting pressure from the market, and they want them to add AI," King explained. "Teams are getting stuck when the data isn't clean, the data is not connected, they don't have a clear understanding of how AI should be used."

The result is technology adoption that looks ambitious but fails to address defined friction points for customers, agents, or supervisors.

Data fragmentation blocks AI effectiveness

One of the most common barriers UJET identified is data readiness. Contact centers typically hold large volumes of interaction data, but that information often lives across disconnected systems—CRM platforms, quality management tools, workforce systems, ticketing databases, and survey platforms.

When AI is layered onto fragmented data, it can expose problems rather than solve them. King warned that weak foundations turn AI into a source of friction: "AI can actually cause more friction and a poor customer experience. But conversely, when it's well thought out, when you know the outcome that you're trying to drive to, the goal that you're trying to drive to, you can then create a best-in-class experience for your customer and for your agent at the same time."

Start with the work, not the tool

Curic's onsite observations revealed a critical operational insight: leaders need to understand what actually slows teams down before selecting AI tools. This requires sitting with agents and watching how they navigate multiple systems, manual lookups, and workarounds during customer interactions.

"Before asking what tool should I buy, try asking what is actually slowing our people down every day, because the agents know exactly where the friction is," Curic said.

This approach reframes the AI conversation. Instead of starting with a product category, leaders begin by identifying repeated friction points—system-hopping, manual note-taking, status checks, routing delays—where AI can deliver measurable value without automating the wrong process.

Agent involvement is non-negotiable

AI projects struggle when agents feel excluded from implementation decisions. UJET's research suggests frontline teams are not rejecting AI outright—they want help with repetitive tasks, context gathering, and routine checks. However, they remain cautious when AI touches sensitive or emotional customer interactions where trust is critical.

Without agent buy-in and understanding, teams may work around new tools, undermining deployment investments.

Move with direction

King offered a clear warning for leaders under pressure: "Don't confuse speed with progress. It's important to move fast on AI, but not to move in the wrong direction."

Progress appears when customers experience less effort, agents receive useful support, and leaders can connect AI investment to measurable outcomes. That requires a disciplined sequence: define the problem, understand the workflow, validate the data, involve the people doing the work, and deploy AI where it improves the experience.

These findings were first reported by CX Today based on interviews with UJET leadership.