Most companies recognise this and respond by having IT or HR run an AI training. That builds literacy, sometimes some excitement, but rarely capability. People mostly already know what AI is. What's missing is a group of people inside the business who can actually do the work: spot real use cases inside their own function, build the skills and agents that solve them, and pull colleagues along.
The AI Enabler Programme builds that group. We call them AI Champions, and they're not a steering committee or a group of part-time enthusiasts. They're a trained cohort: selected against a profile we've refined across enterprises, developed over several months, and equipped to carry AI capability into every corner of the business from the inside.
What makes a Champion
Not every keen volunteer makes a strong Champion, and the people who put themselves forward are often not the ones who'd do the most. So we start with a selection step: we share a best-practice profile, work with you to identify candidates against it, and help with structured interviewing where it's useful.
We look for a specific combination. Deep credibility inside the function, so colleagues take the work seriously when it comes from them. A working understanding of the operational rhythm, so they know which moments in the week the new tools actually have to survive. And the temperament to coach without lecturing, paired with enough curiosity that a new tool gets explored rather than feared.
A Champion isn't an evangelist, and they're not a technologist either. They're a peer with real capability and standing, which is what lets the work spread sideways instead of getting pushed top-down.
How the cohort develops
The programme runs as a cohort over several months, the same people moving through the same arc together. That matters: a cohort working through the same material at the same time accounts for half of the capability that walks out at the end.
Live trainings. The cohort works through the substantive material together in live sessions: the foundations of how the technology actually behaves, prompting beyond the surface-level tricks, the context and knowledge architecture an AI workflow runs on, and skills, agents, and workflow design. These are working sessions, not lectures, and each Champion uses the tools live on their own material throughout.
Use-case discovery with peer review. Between sessions, every Champion runs structured use-case discovery inside their own function: looking at where the work is slow, where quality leaks, where the bottlenecks actually sit, and what the people doing the work would change if they could. The cohort then reviews each other's use cases, with our facilitation. This isn't a politeness exercise. Peer review pressure-tests every candidate use case before it goes into the build, so the weak ones die early and the strong ones come out sharper.
An on-site hackathon. After discovery, the cohort comes together for a full-day hackathon to turn the prioritised use cases into actual working components. The skills and agents get built here, on the cohort's own real material, with us alongside as live coaches. The Champions aren't spectators. They're the ones building it.
Building sessions. After the hackathon, a series of follow-up working sessions takes the components from "works" to "ready for the wider team to use." That means folding in feedback from inside each Champion's function, coaching individual Champions where they need it, and refining until the skills and agents are something colleagues can pick up and use without an explanation.
A final report. The programme wraps up with a documented use-case library, the working components produced during the hackathon and the building sessions, and a set of recommendations for what to do with the multiplier structure once it exists.
What compounds inside a cohort
Three things compound through the programme that no single training event can produce on its own.
A shared language. The cohort ends up with common vocabulary, shared reasoning patterns, and the same bar for what counts as good work. When two Champions from different functions meet around a use case six months later, they can work together immediately, because they came through the same arc.
A working knowledge architecture. The Champions learn to think in layers: the domain knowledge that doesn't change case by case, the process expertise that lives between the lines of any policy, the templates and references the work actually runs on, and the precedent and history behind every decision. They learn to build skills and agents on top of that architecture, which is what makes the work hold up across cases, and across model generations.
A network. The cohort becomes the start of an internal community of practice. After the programme closes, the network keeps meeting: use cases travel between functions, and a pattern that worked in one place gets picked up in another. That's what the multiplier structure actually is: a working network of people who can move AI into the business without external help, not just a name on an org chart.
What the business brings
- A selection process taken seriously. The wrong selection at the start costs the most later.
- Time for the Champions, protected. The role works only when the business makes it explicit that this time is part of their work, not on top of it. Half a Champion makes no Champion at all.
- Real use cases from inside real functions. The hackathon and the building sessions need actual material from the business to produce anything that lasts.
- Visible leadership support. A Champion programme that leadership doesn't name and back from the top quietly fades into a side project. With visible backing, it scales.
What you walk away with
- A trained cohort of internal AI Champions, equipped to identify use cases, build skills and agents, and enable colleagues across every function.
- A use-case library that has been filled, prioritised, and peer-reviewed, with the strongest cases already built into working components.
- Production-ready skills and agents from the hackathon and the building sessions, ready for colleagues across the business to use.
- An internal multiplier structure that carries AI capability into every corner of the business from the inside, and a documented method for developing the next cohort when you decide to scale further.
Who this is for
Organisations rolling AI out at real scale, where the work has to land across many functions and external consultants alone won't reach all of them. Businesses that already have leadership alignment and some early literacy, and have hit the bottleneck question of how to actually get capability into the work. Functions across legal, compliance, finance, HR, operations, sales, customer service, internal advisory, and professional services, where internal multipliers will outperform any external rollout.
How it fits
The Enabler Programme sits inside the wider Full Transformation Partnership as its multiplier engine, and it's also offered on its own where a focused investment in internal capability is the right next move. It most often follows leadership-tier engagements (the Executive Workshop or the Executive AI Experience) that set direction and give the cohort air cover. Where a single team wants to enable itself directly on its own workflows rather than develop cross-cutting Champions, the AI Practice Sprint is the team-level version of similar work. The Enablement Retainer is often the rhythm that carries the Champion network forward once the programme closes.