Engagement

AI Adoption Programmes

Most organisations don't have an AI problem. They have an adoption problem. Licences are paid for, tools are technically rolled out, and yet the day-to-day work looks the same as it did before — expensive tools collecting dust, teams frustrated, budget wasted. Or you're mid-migration: moving from Microsoft to Google, or layering a new AI suite on top, and you can already see your people landing on the new platform and carrying on the old way. Adoption is structured work, not ad-hoc training. That's what these programmes are for. Engagements range from a focused readiness assessment, to a workshop series for a specific team, to a full phased programme, to an ongoing retained partnership as your AI footprint grows.

Book a scoping call

15 minutes. No sales pitch.

Recent work

What an adoption engagement looks like in practice

Phased programme Microsoft → Google Workspace migration

UK subsidiary, global consumer appliances group

Engaged as AI adoption partner for a UK subsidiary of a €3.8bn global consumer appliances group navigating a full migration from Microsoft to Google Workspace. Designed and delivered a phased programme spanning AI readiness assessment, hands-on Gemini enablement workshops for 12 middle managers, and strategic implementation planning with the UK Managing Director. Pre-workshop survey identified specific workflow pain points per participant, with sessions built around their actual daily tasks rather than generic tool demos.

Scope: Readiness assessment, manager workshops, MD strategy sessions
Cohort: 12 middle managers + UK leadership
Approach: Per-participant pain-point survey before sessions; workflows built on real tasks

Anonymised at the client's request. Further engagement summaries published as they're approved for release.

Programme structure

How these programmes work

Most adoption programmes run through four phases. Discovery establishes where the real opportunities are. Quick wins prove the value to a sceptical team. Structured adoption embeds AI into how people actually work. Strategic implementation tackles the bigger projects once the team has the muscle memory to absorb them. The shape varies by engagement — sometimes I run all four, sometimes I'm brought in for one — and Phase 4 typically runs in parallel with Phase 3 rather than after it. If a programme starts with a leadership-alignment moment, an executive event from the hackathons and workshops side often kicks off Phase 1.

Phase 1: Discovery and Assessment

Audit current tool usage, survey the team on specific pain points and time-wasters, identify the three to five highest-impact workflows per team, and assess data readiness and system access. The goal of Phase 1 is to stop guessing about where AI will pay off and start working from evidence — what your people actually spend their week doing, what they hate about it, and which of those tasks AI is genuinely good at right now.

Deliverable: Prioritised roadmap with quantified business cases.
Phase 2: Quick Wins and Proof of Value

Deliver the top-ranked opportunities from Phase 1 to build momentum. Hands-on sessions where the team works on their own workflows, not generic exercises. Each participant leaves with a working AI-assisted process they use immediately — the report they always run on a Friday, the inbox triage they dread, the spec document they always rewrite three times. Quick wins matter not because they're trivial, but because they convert sceptics through experience rather than argument.

Deliverable: Working solutions with measurable time savings.
Phase 3: Structured Adoption

Systematic rollout across teams. Regular coaching sessions, workflow building embedded in daily operations, adoption tracking so you can see where it's landing and where it isn't. This is the phase where AI stops being a project and becomes how people work. Done well, it's also where internal champions emerge — people who, once they've built two or three workflows of their own, start helping their colleagues without being asked.

Deliverable: Self-sustaining AI usage across the organisation.
Phase 4: Strategic Implementation

Larger, more complex projects — deeper data integration, cross-functional workflows, sophisticated automation. Phase 4 runs in parallel with Phase 3, not after it, because teams already using AI daily for small tasks are far more receptive to bigger changes. They've felt the difference, they trust the technology, and they'll tell you honestly which proposed automations would actually help versus which sound clever in a slide deck.

Deliverable: Documented ROI and capability transfer so your team doesn't need me anymore.

A view on adoption

"The right AI solution is the one your team actually uses on Monday morning. I'd rather build something simple that gets adopted than something sophisticated that gets ignored."

The failure mode this addresses is everywhere: an elegant, technically sophisticated AI workflow that stalls because it doesn't fit how people actually work. A custom agent that needs three system integrations no one's signed off. A clever automation that demos beautifully but adds a step to a process the team has already simplified by hand. Sophistication that nobody adopts is a more expensive failure than a simpler tool that everybody uses. The whole shape of these programmes — small wins first, complexity only once trust is in place — comes from this view. The longer version sits in how we think.

Common questions

What people usually ask before booking

How do you handle regulated clients and sensitive data?

I spent three years in compliance at a regulated financial services firm implementing ML-powered automation. I understand that in your environment, every AI workflow needs explicit governance checkpoints, data handling boundaries, and human-in-the-loop gates. All prototyping uses synthetic or anonymised data — I never need access to real client data or PII. I design AI workflows that work within your existing compliance framework, not around it.

We're migrating platforms — can you help with the transition and the AI enablement together?

That's where I add most value. The migration is happening regardless — the question is whether your team lands on the new platform and carries on working the old way, or whether they arrive with AI-assisted workflows already configured. I time the enablement around your migration schedule so people experience the new tools as better, not just different. For programmes where leadership alignment needs to come first, this often starts with an executive event from the hackathons and workshops side before Phase 1 fieldwork begins.

What if our team pushes back or doesn't trust AI?

They almost always do, and it's rational. I don't start with AI evangelism. I start with the task they hate most, show them it getting solved, and let them feel the difference. Trust builds from experience, not presentations. By the time we're talking about "AI adoption" they're already using it daily.

What engagements look like

Engagements range from a standalone readiness assessment over a few days, to phased programmes running three to six months, through to ongoing retained partnerships as your AI footprint grows. The discovery call is where we scope what fits — your situation, your timeline, your constraints — and decide together what shape the engagement should take.

If any of this sounds like your situation

The scoping call is the cheapest way to find out whether an adoption programme is the right next step. We talk through where your team is now, what's blocking real adoption, and what an engagement might actually look like — or whether you don't need one yet.

Book a scoping call

15 minutes. No slides. Just a conversation.