UJET's field research into contact center operations has surfaced a critical tension in the current AI adoption cycle: speed is being mistaken for progress. Kristin King and Tena Curic, speaking from direct observation inside customer contact centers, identified a pattern where boards, competitors, and vendors are driving rapid AI deployment without corresponding clarity on business outcomes, data readiness, or operational intent. The pressure is real—CX leaders face simultaneous demands to improve quality, reduce cost-to-serve, and modernize operations—but the result is often technology adoption that adds friction rather than removing it. When AI is layered onto fragmented data systems, unclear workflows, and teams that haven't been consulted on the problem being solved, the technology becomes another source of operational drag. This raises an immediate question for teams already mid-implementation: if your AI deployment was driven by competitive pressure rather than a defined operational problem, how will you know whether it's actually improving customer or agent experience?
The data foundation problem sits at the heart of this risk. Contact centers typically hold vast volumes of customer interaction data, but volume does not equal readiness. Customer conversations, CRM records, quality data, workforce metrics, and escalation histories often live in separate systems, creating the fragmentation that AI can expose rather than solve. Poor routing decisions, incomplete answers, inaccurate summaries, and agents forced to correct AI output after the fact are symptoms of this underlying problem. UJET's research suggests the solution requires reversing the typical vendor-led sequence: start with the floor, not the tool. Before selecting an AI platform, leaders need to sit with agents, observe actual workflows, and identify where repeated friction occurs—system-hopping, manual lookups, workarounds, and routing gaps. This operational grounding matters because it prevents organizations from automating the wrong process or deploying AI where it will undermine agent confidence rather than build it.
Agent buy-in emerges as a non-negotiable adoption requirement, yet it is often treated as a change management afterthought. UJET's conversations with frontline teams revealed that agents are not rejecting AI; they are asking for it to be aimed at the right work—repetitive tasks, context gathering, summaries, and routine checks. They are more cautious when AI touches sensitive or emotional interactions where customer trust is at stake. This distinction matters for teams evaluating tools like Agentforce or similar agentic AI systems: if your agents feel acted upon rather than included in the decision, they will work around the technology, leaving leaders with the illusion of deployment while frontline teams continue old habits. The practical sequence King and Curic advocate is straightforward—define the problem, understand the workflow, validate the data, involve the people doing the work, and deploy AI where it can improve experience—but it requires discipline in an environment built for speed. Progress is not measured by the number of AI tools deployed or workflows automated; it shows up when customers experience less effort, agents gain useful support, and leaders can connect investment to measurable outcomes.
UJET Says Contact Center AI Adoption Is Moving Too Fast, Which Creates New Risks CX Today
Contact center AI adoption is accelerating, but UJET’s recent customer visits suggest speed alone is a weak measure of progress. This emerged as a central theme during CX Today’s interview with Kristin King, Chief Customer Officer at UJET, and Tena Curic, Senior Customer Success Manager at UJET. Th