How to Build an Internal AI Champions Program

One of the most effective and underused tools for driving AI adoption costs almost nothing: a network of internal AI champions. An AI champions program identifies a few enthusiastic, capable people in each team and supports them to lead AI adoption from within — helping colleagues, answering questions, sharing useful use cases, and keeping momentum going long after the formal training ends. It works because adoption is fundamentally a human, social process: people learn and change more readily from trusted peers than from corporate mandates or one-off training sessions. A well-run champions program turns the natural enthusiasm of a few into the steady, distributed engine of capability across the whole organisation.

Put plainly, top-down AI mandates create compliance; peer influence creates adoption. Most teams already have someone naturally curious about AI and trusted by colleagues, and that person, supported properly, is worth more than any all-staff webinar. A champions program is simply the deliberate cultivation of that dynamic across the organisation.

Why champions work when mandates don't

Top-down AI initiatives have a consistent weakness: they push from the centre, but adoption happens at the edges, in the daily reality of teams. A leadership mandate to "use AI" or a single company-wide training session reaches people once, from a distance, and then leaves them alone with their questions and their scepticism. Champions solve this by embedding support inside the teams themselves. When a colleague is stuck, unsure or doubtful, there is someone right there — a trusted peer, not a remote authority — to help.

This matters because the research is clear that access and even training are not enough. Microsoft and LinkedIn's 2025 study found large gaps between AI availability and effective use, and the Digital Education Council identified weak adoption support as a top barrier. Champions are precisely the ongoing, local, credible support that converts availability into actual capability. People trust their peers, learn from people like them, and are far more likely to try something a respected colleague is enthusiastically using than something a corporate email told them to do.

Champions vs the alternatives

It helps to see why peer-led adoption outlasts the usual approaches. Each alternative spreads AI a different way, and stickiness varies sharply.

The mandate and the webinar reach people once and leave; the external vendor fades when the contract ends. Only internal champions provide the local, sustained reinforcement that makes new behaviour stick.

Who to choose as champions

The instinct is to choose the most senior or the most technical people as champions. This is usually a mistake. The best champions are defined by three qualities, none of which is seniority or technical depth. The first is genuine enthusiasm — people who are excited about AI and using it naturally, because their energy is contagious and credible. The second is peer credibility — people their colleagues respect and listen to, regardless of title, because influence flows through trust. The third is helpfulness — people willing and able to support others patiently, because the role is fundamentally about helping.

A champion is often a mid-level person who has embraced AI, is good with people, and is well-connected within their team. They do not need to be an expert; they need to be a credible, enthusiastic, helpful guide who is a step or two ahead of their colleagues. Choosing for influence and enthusiasm rather than rank is what makes the program work — choosing on seniority or tech skill is the classic error.

The Edison champions playbook

Edison builds champions programs in four moves through AI champions training:

1. Select one credible, curious champion per team.
2. Train them deeper than peers, including evaluation and safe use (workshops).
3. Empower them with time, status and a direct line to leadership.
4. Sustain them with a community, recognition and ongoing learning.

Champions become the human infrastructure of your AI operating model and the front line of adoption. The remit must be real, not vague: model good use, support colleagues, surface what is working, and reinforce the change — never an unsupported volunteer role, and never a glorified help desk.

How to run the program

A good champions program has a few elements. First, identify and recruit champions across teams — ideally one or two per team, chosen for the qualities above, and invited rather than conscripted, because the role depends on genuine enthusiasm. Second, equip them — give champions deeper training and early access so they are genuinely ahead of their peers and able to help, and connect them to each other so they share what works across the organisation. Third, support and recognise them — give them time to perform the role, recognise their contribution, and make clear it is valued, because champions who feel unsupported quietly stop. Fourth, give them a clear remit — helping colleagues, answering questions, sharing use cases, gathering feedback and flagging blockers — so the role is real, not vague.

The champions network also becomes a valuable feedback channel, surfacing what is working, what is confusing, and where adoption is stuck — intelligence that helps leadership steer the wider AI effort. Without that direct line to leadership, issues and wins go nowhere.

A high-return, low-cost lever

The economics of a champions program are compelling: for the modest cost of some extra training and recognition for a handful of people, an organisation gets distributed, credible, ongoing adoption support that no central program can match. It is one of the highest-return, lowest-cost moves in the entire AI adoption toolkit. For an SME, even one or two genuine champions can transform how AI spreads through the business. For an enterprise, a structured champions network across functions is often the single most effective adoption mechanism.

Adoption is contagious, but only through people others believe. Resource your champions properly — treat the role as real work, not a volunteer hobby — and they will carry AI further into the business than any mandate or vendor ever could. Pair this with strong manager enablement, and designing and supporting an AI champions program as part of a broader capability effort is exactly what Edison AI's AI training work helps organisations do. Adoption is a human process — and champions are how you make it human, local and credible, at scale.