The AI Champion Isn't A Personality. It's A Job.

Before you go looking for an AI champion, go find whoever your team already asks when something breaks with a tool nobody officially rolled out. That person is doing the job already. Forget the title, forget whether they’d call themselves an evangelist, a super-user, or just “the person who’s good at this.” What makes someone an AI champion is narrower and more checkable than any of those labels: a working employee, often outside IT, who has rebuilt at least one real piece of their own job around an AI tool, still does that job, and is open enough about how they did it that a coworker asks how instead of waiting to be told.

Here’s how to tell whether you’ve actually found one, or just someone who talks about AI a lot. Ask three questions. Can they defend the workflow they rebuilt when it breaks, or have you only ever seen them demo it when it works? Are they the first call when a teammate on the same job gets stuck, ahead of the help desk? And if you asked them right now which “AI win” from the last all-hands is still running every week and which one was a good demo six months ago that nobody’s opened since, could they tell you the difference on the spot? Whoever can answer all three without having to think about it is your real champion.

Adoption spreads by watching a peer, not by policy

Gartner’s July 2025 survey of 2,986 employees, published in December 2025, found 37% of workers with AI access still don’t use it, and the reason most gave wasn’t the tool. It was that their coworkers weren’t using it either. That’s the mechanism a champion exists to interrupt. People don’t start working a new way because an email announced it was live. They start because they watch someone doing their own job get somewhere faster, and decide the risk of falling behind is worse than the risk of looking new at something. A champion is that visible proof.

You can check whether this is actually happening at your company. Watch what happens in the two weeks after someone rebuilds a workflow. If a teammate starts asking questions about it unprompted, the proof is spreading the way it’s supposed to. If nothing happens outside that person’s own desk, you don’t have a champion yet, you have someone quietly using a tool well, and the other 37% still haven’t seen a reason to change.

The volunteer version is why most companies think they’ve solved this

You likely already have this person, you just haven’t gone looking. Check accounts payable for whoever worked out a faster reconciliation on their own laptop. Check your account managers for whoever’s been quietly rewriting proposals with a tool nobody approved. Find that person and you’ve found the gap: they’re doing an AI champion’s job already, for free, off the side of their desk, with no protected hours, no mandate to show anyone outside their own team, and no backup if they get promoted, poached, or just get tired of answering the same question.

Here’s a second check, and it takes ten minutes. Ask your leadership team how far along AI adoption is at the company. Then ask ten employees outside that volunteer’s own department the same question. WRITER’s 2026 enterprise AI adoption survey, fielded with Workplace Intelligence between December 2025 and January 2026 across 2,400 knowledge workers, found 75% of C-suite executives believe their organization has successfully adopted AI, while only 45% of their own employees agree. Land anywhere near that gap with your own numbers and it means the same thing it means at those companies: the only real adoption happening is confined to whichever pocket the volunteer already works in, and leadership has been reading that pocket as the whole company.

What we mean by internal champions

We’ve written before about naming one person on staff as the owner of a rebuilt workflow. Internal champions build on that by fixing the actual weak point: a program built around one volunteer collapses the moment that person leaves, gets promoted, or just stops answering questions for free. In the AI Adoption Program, we train up to 10 people per cohort instead of one. A two-week Setup & Integration phase gets the tools, access, and governance right first. Then a four-week Pilot Training Cohort runs one hour of live training a week, plus homework built around the company’s own files instead of a generic curriculum.

Training 10 people instead of one is itself the fix: spread the role across functions and it survives someone changing teams. A standing hour of live training each week, with homework tied to a real workflow instead of a sample exercise, gives the work actual time on the calendar instead of competing with the day job for attention. And because people are rebuilding their own workflows rather than a demo project, what they walk out with is the kind of visible, repeatable proof that pulls coworkers out of Gartner’s 37%.

The test for whether you have a real AI champion, or just an unusually generous volunteer, comes down to three checks. Can you name them in writing? Do they have protected hours set aside for the work? And if they left tomorrow, is there a second person who could pick up exactly where they left off? Most companies can answer yes to the first one, if that. Run all three before you find out the hard way. If the answer’s no on any of them, that’s the gap the AI Adoption Program closes: it trains enough people, on purpose, that the answer stays yes even after someone leaves.