An AI champions program names a few respected peers — not IT, not a top-down mandate — to use AI on their own real work and show colleagues what it does. To build one: pick volunteers close to the work, give them a one-page charter and protected time, point them at a concrete task, and back them with visible leadership support.
A rollout gets people a login; a champion gets them a habit. That gap — between access and use — is the part no purchase order closes, and the most reliable way across it is a handful of peers who already have the team's trust. This is how to stand that program up without it becoming a committee or a job title no one wanted.
What an AI champion actually is — and isn't
A champion is a trusted colleague who experiments with AI, shows what it does on real work, and makes the tools approachable for their team. Microsoft's Define your AI Champion role and charter guide is blunt about the boundary: champions complement IT, compliance, training, and the adoption program — they don't replace them. They translate central guidance into the realities of day-to-day work; they are not a help desk, and they don't own the rollout.
The reason the role exists at all is that tool access doesn't change behavior on its own. OpenAI frames its own AI Champion role around exactly that: transformation happens when behavior changes, not when a license is assigned, and champions supply the credibility and the repeated examples that make a new habit stick. A champion is the person who answers "but how would I use this?" with a specific, familiar example — which is something no company-wide email can do.
Pick people close to the work, not the most technical
The best champions volunteer. Microsoft's Build your Champions and Early Adopters programs training describes them plainly as the employees who like helping others and are genuinely excited about the tools — a disposition, not a skill set. You want respected peers embedded in each team, because adoption spreads along existing lines of trust, and the point is relevance: a champion inside the finance team knows which recurring finance task is worth automating first.
So look for enthusiasm and standing, not seniority or coding ability. Keep the group small and spread across functions rather than concentrated in one. A single champion per team, with the air cover to spend time on it, beats a long list of names who were volunteered by someone else and never asked for the role.
Give them a charter and protected time
Vague goodwill fades; a written remit lasts. Microsoft's guide recommends a one-page charter covering why the role exists, what the champion gains (AI fluency, visibility, recognition), which team they support, and where to escalate questions that fall outside the role. That single page is what gives managers a shared expectation and lets you recruit the next champions without reinventing the ask each time.
It also helps to name what kind of champion you need. OpenAI's champion model splits the work into enabling fluency, handling rollout and change management, designing workflows, and building practical agents with measurable impact — one person rarely does all four. Whatever the split, the non-negotiable is protected time: a champion asked to drive adoption on top of a full workload will quietly drop it first. Budget the hours the same way you budget the licenses.
Point them at a real task, not "promote AI"
"Champion AI" is as unactionable as "use AI." Give each champion one concrete, recurring job their team already dislikes and let them show the tool handling that — the same discipline behind every build on this site. Issue #001 didn't start with "let's use AI," it started with a specific inbox that took too long to triage every morning and a test of whether that one job got easier. A champion armed with one proven, familiar win converts far more colleagues than a champion armed with a feature tour.
Pair the task with a clear safety line so people will actually try it: early outputs are expected to be rough, and a human still approves the result before anything ships or gets sent. The champion's job is to model good use inside that gate — AI drafts, a person decides — not to push autonomy the team isn't ready for.
Back them with leadership, then measure
Champions stall without air cover. Zapier's own account of its rollout, How Zapier rolled out AI org-wide, credits leadership-driven, structured experimentation — a company-wide pause for a hackathon and early power-users brought in to build and teach — with taking internal AI usage from under 10% to a near-universal share of staff. The lesson isn't the hackathon specifically; it's that the champions worked because the top of the company made room for them, not despite it.
Then watch the right number. A champions program is working when active use rises and the task each champion targeted measurably got easier — not when the roster of champions grows. Microsoft's Copilot Scenario Library exists precisely so a champion can pick a use case relevant to their department instead of inheriting a generic one; start there, prove one task per team, then let each champion pick the next.
New to how these tools fit together in the first place? The agent basics primer starts from scratch, and if you're still deciding whether to buy seats at all, the Copilot rollout guide covers the mechanics a champion program sits on top of.
FAQ
Should we appoint AI champions on our team? Usually yes — a small number of them. Champions are the most reliable way to close the gap between buying AI access and people actually using it, because adoption spreads through trusted peers, not policy. Keep it lightweight: ask for volunteers who already like helping others, give them a one-page charter and protected time, point each at one real task, and back them with leadership support. Appointing a long list of reluctant names, or treating "champion" as an unpaid second job, is the failure mode to avoid.
Who makes a good AI champion? Respected, enthusiastic peers embedded in each team — not necessarily the most technical people or the most senior. Microsoft's own guidance describes champions as the employees who enjoy helping others and are excited about the tools. You want someone close enough to the daily work to know which recurring task is worth automating first, and trusted enough that colleagues will try something because that person vouched for it.
How many AI champions do we need? Start small — roughly one per team or working group, spread across functions rather than concentrated in IT. The goal is coverage of the lines of trust people already follow, not a big roster. A few well-supported champions with protected time move adoption far faster than a long list of names who were volunteered and never asked for the role.
How is a champions program different from AI training? Training teaches the tool; a champions program changes the habit. A single training session rarely sticks on its own, because skill with these tools builds through repetition on real work. Champions provide the ongoing, in-context nudge — the colleague who shows you how they used it on a task like yours, this week — that turns a one-time class into a daily behavior. The two work best together: train, then let champions carry it.
What should an AI champion charter include? Microsoft recommends keeping it to one page: why the role exists and how it supports responsible adoption, what the champion gains (fluency, visibility, recognition), which team or community they support, and where to escalate questions outside their remit. That page aligns managers on expectations, makes recruiting the next champions easier, and keeps the role from sprawling into an unofficial help desk.
Every post on this site is one real, documented AI build — the task, the setup, and the honest result, including what didn't work. That's the fastest way to give a champion something concrete to show. Want one in your inbox each week? Subscribe free.