Douglas Kramon has run fan support at ESPN since 2005, back when the job meant helping people with fantasy games and mobile apps. His teams now field failed streams in the middle of live games and login problems for a fan base in the tens of millions.

When his organization got ready to push further into generative and agentic support, the first big project was the help center. Kramon's team overhauled more than 25,000 knowledge documents to build a source of truth the AI could safely draw from.

Kramon has talked about why. On The Bridgecast podcast, he pointed out that generative AI can search a disorganized knowledge base and still come back with an answer. The overhaul was about making sure the answer a fan gets is the right one.

Kramon is one of the leaders on CX Current's Top 50 Leaders Transforming CX '26, released this week. Several other honorees describe the same kind of work in their profiles, most of it in systems customers never see.

Customers give AI one shot

Gartner reported in September that only 27% of customers would try a chatbot again after a bad experience. Its advice to service leaders was to tell customers plainly what a bot can do and widen its scope only once it performs reliably.

Leaders are already planning for the upkeep. In an earlier Gartner survey of 321 service and support leaders, 58% said they plan to upskill agents into knowledge management specialists to keep the content behind AI and self-service accurate. The same survey found 91% under executive pressure to implement AI this year.

Choosing an AI agent happens once. Keeping it accurate, testing changes before customers see them, and reviewing its conversations goes on for as long as the agent is live. Writer found something similar when it tested six models on 22 tasks: the model barely mattered.

Fix what the agent reads first

Burgoyne Hughes, who runs GE HealthCare's customer service centers, rebuilt complex procedures into decision trees reps could follow under pressure. He says the error rate fell from 1.6% in 2018 to 0.04%. Charl Lombard, chief client experience officer at Goosehead Insurance, is more relaxed about messy insurance data. "The teams that win aren't waiting for perfect data lakes," he says. He'd rather ship models that work with about 80% accuracy and improve the data underneath them as he goes.

Test it small before customers see it

John McCahan, now chief CX officer at Fellers, works by the rule "fail fast, fail early, fail small." New capabilities go into contained environments first, and only the ones that prove useful get scaled. At HP, Satish Bettadapur, global head of customer care centres, wants strong data, security, governance and testing in place before any new AI capability reaches a customer. Jennifer Baker's team at The Cincinnati Insurance Companies has been testing new approaches to caller authentication, some of it using AI.

Learn from every conversation

Kramon's "catharsis scoring" uses AI to track how a fan's conversation moves from frustration to relief. The best of those calls become training material, and he says conversations shaped by them score 20% to 40% higher on CSAT in situations that are usually hard to resolve.

At Achieve, Brandon Peterson uses conversation intelligence so managers coach against the same frontline behaviors and reps can see their own numbers. Eric Strom, who runs bswift's service center for more than six million participants, applies the idea to the software. When an interaction goes badly, his team traces it back to see what the agent-assist tools surfaced, whether the rep used it, and where the knowledge base came up short.

Measure what the customer got

Several honorees are wary of the usual efficiency numbers. McCahan puts resolution, loyalty, and lifetime value ahead of handle time. Merrilee Matchett, who runs global customer service and operations at MetLife, pushes her teams to look past handle time and self-service adoption to the friction driving them. Strom tracks customer effort, repeat contacts and employee engagement alongside satisfaction, and asks whether the technology helped produce the right outcome.

A second workforce

Customer experience leaders have done this kind of work for human agents for years. Now it has to cover AI agents too. The theme runs through our enterprise coverage, from banks deciding that even a 99% accurate agent shouldn't move money on its own to the case for context engineering and for sorting out data, people, and process before worrying about the model.

The full list of CX Current's Top 50 Leaders Transforming CX '26, with each honoree's profile, is on CX Current.

The Top 50 Leaders Transforming CX '26 awards are presented by CX Current in partnership with Cresta.