A workshop changes individual skills. An AI Champions Programme changes how an entire organisation works. Here's how we build the people who make that happen.
The single biggest reason AI training ROI evaporates is structural, not pedagogical. We've written about this in more detail on our page about why AI training fails, but the short version is this: there's no one inside the organisation whose job it is to maintain momentum.
People leave the workshop energised. They've built something. They've seen what's possible. They return to a desk where nobody else is talking about AI, their manager hasn't mentioned it since the training brief went out, and the default tools are still the old ones. Three weeks later, it's forgotten. Not because the training was bad. Because the environment didn't change to support the new behaviour.
A single workshop, however well-designed, cannot fix a cultural and structural adoption problem. What it can do is give you a cohort of people who are genuinely capable — and from that cohort, you build your champions.
An AI champion is not a technical role. You don't need to recruit your most technical people. You need to recruit the people who are trusted by their colleagues, curious about new tools, and willing to be visible about how they work.
In practice, an AI champion does four things:
Think of them as an internal AI coach, not an IT person. The difference matters. IT is about systems. Champions are about people.
The AI Champions Programme runs across three phases over approximately eight weeks. The structure is designed so that champions are doing real work inside the organisation from week two — not sitting in training rooms for the whole programme.
Champions receive deeper AI training than the general team. This isn't a repeat of the introductory workshop — it's designed for people who will need to explain AI to sceptical colleagues, apply it across multiple workflow contexts, and build their own tools and automations. Champions leave with genuine fluency in the tools, a personal AI workflow they've already stress-tested, and the ability to articulate what AI is and isn't good for in plain language. The build challenges on this day are harder and more open-ended than in a standard team session.
Champions return to their own teams and begin operating as internal AI coaches. They have a structured set of conversations to initiate with colleagues — not formal training sessions, but organic, low-pressure exchanges about what people are working on and where AI might help. They document use cases, note resistance points, and track questions that keep coming up. ANCHR runs check-in calls during this phase to help champions navigate what they're finding and adjust their approach if needed. This is where the real learning happens — both for the champions and for us.
Champions come back together — plus the L&D lead or CHRO — to share what they found in the field. What worked, what didn't, where the organisation is ahead of expectations, and where adoption is stalling. Together, we iterate on the champions' approach and design the next phase of AI adoption for the organisation. This might mean a wider team training rollout, a revised AI policy, or a set of targeted interventions for specific resistance points. The output is a concrete action plan, not a general recommendations document.
One champion per 10–15 people is our rule of thumb. For most of the organisations we work with — typically teams of 30–120 — that means training three to eight champions.
Champions don't need to be the most senior people in the room. In fact, some of the most effective champions are enthusiastic mid-level staff: people who understand how the work actually gets done day-to-day, who colleagues feel comfortable asking "dumb questions," and who don't carry the overhead of being seen as representing management.
Seniority has one advantage: visibility. If a VP or department head is visibly using AI in their own work, it signals to the rest of the team that this is expected and valued — not an optional experiment. Where possible, we recommend a mix: at least one senior voice plus your strongest peer-level early adopters.
Champions are not being trained to run formal AI workshops. They're being trained to use AI fluently and to help colleagues in informal, peer-to-peer ways. The skill set is different. If you need people who can run structured training sessions, that's a different conversation.
There's no exam, no badge, no points system. Champions complete the programme and are recognised internally by the organisation — not by a third-party body. We don't believe certifications for AI tools that change every three months serve anyone's learning goals.
ANCHR is not coming in to audit your AI strategy, produce a transformation roadmap, or bill by the hour for advisory time. This is a practical, hands-on training programme with a specific output: a small group of capable, trusted internal AI coaches.
The programme is designed with the client organisation's specific workflows, tools, and team context in mind. We do a readiness conversation before designing the Phase 1 content. The build challenges and deployment structure are adapted to what your champions actually do at work.
Approximately eight weeks from Phase 1 to the Phase 3 review session. Phase 1 is one full day. The four weeks of Phase 2 run alongside your champions' regular work — it's not four weeks out of the business. Phase 3 is a half-day. The total time investment for each champion is roughly one and a half days of structured training plus their ongoing work within the team during Phase 2.
No. All of the tools we train on — Claude Cowork, Claude Code, and related AI applications — are designed to be used by non-technical professionals. The selection criterion isn't technical ability; it's curiosity, trust among peers, and the willingness to be a visible early adopter. We've trained effective champions from HR, operations, finance, marketing, and customer success — none of whom had technical backgrounds.
Yes, and this is often the most effective sequencing. Run the wider team through a half-day or full-day workshop first — so there's a shared baseline across the organisation — and then run the champions programme as the follow-through layer that maintains and deepens adoption. The champions' Phase 2 deployment is more effective when the people they're working with have already had some AI exposure.
That depends on what the Phase 3 review surfaces. Some organisations run a second cohort of champions six months later. Some proceed with a broader team training rollout. Some bring ANCHR back for targeted sessions on specific tools or use cases the champions identified. There's no mandatory next engagement — champions are designed to be self-sustaining. But organisations that treat Phase 3 as a true review, rather than a close-out session, consistently find it generates a clear picture of what to do next.
Tell us about your team. We'll discuss the right number of champions, the right sequencing, and how the programme fits alongside any wider training you're planning. Takes 20 minutes.
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