Most companies recognise this and respond by asking IT or HR to run an AI training. That produces literacy, sometimes excitement, rarely capability. What is missing is not knowledge of what AI is. What is 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. They are not a steering committee. They are not part-time enthusiasts. They are a trained cohort, selected for a profile we have 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 self-nominate are often not the people who would do the most. 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 useful.
We look for the combination that does this work well. 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 have to survive. The temperament to coach colleagues without lecturing them. And the kind of intellectual interest that means a new tool gets explored rather than feared.
A Champion is not an evangelist and not a technologist. They are a peer with capability and standing, which is what lets the work spread sideways rather than need to be pushed top-down.
How the cohort develops
The programme runs as a cohort over several months. The same people through the same arc. That matters: the effect of a cohort working through the same material at the same time is half of the capability that walks out at the end.
Live trainings. The cohort works through the substantive material in live sessions. The foundations of how the technology actually behaves. Prompting beyond the surface tricks. Context and the knowledge architecture an AI workflow runs on. Skills, agents, and workflow design. The sessions are working sessions, not lectures. 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. Where the work is slow. Where the quality leaks. Where the bottlenecks are. What the people doing the work would change if they could. The cohort then reviews each other's use cases, with our facilitation. Peer review is not a politeness exercise. It pressure-tests every candidate use case before it goes into the build. Weak use cases die early. Strong ones get 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. Champions do not watch this. They build 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." Integration of feedback from inside each Champion's function. Coaching where individual Champions need it. Refinement until the skills and agents are something colleagues can pick up and use without an explanation.
A final report. The programme closes 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 develops common vocabulary, common reasoning patterns, and common quality bars. 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 does not change case by case. The process expertise that lives between the lines of any policy. The templates and references the work actually uses. The precedent and history that informs 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 is the start of an internal community of practice. After the programme closes, the network keeps meeting. Use cases travel between functions. A pattern that worked in one place gets adopted in another. The multiplier structure is not a name on an org chart. It is a working network of people who can move AI into the business without external help.
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 does not 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 by which the next cohort can be developed when you decide to scale further.
Who this is for
Organisations rolling AI out at any meaningful scale, where the work has to land across many functions and external consulting alone will not reach all of them. Businesses that already have leadership alignment and some early literacy, and have arrived at the bottleneck question of how to actually carry capability into the work. Functions across legal, compliance, finance, HR, operations, sales, customer service, internal advisory, and professional services, where deep 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 is also offered as a standalone 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 shape of similar work. The Enablement Retainer is often the rhythm that carries the Champion network forward after the programme closes.