The AI rollout framework
Most AI initiatives fail not on the technology but on the strategy behind it. A tool is bought, a few people try it out — and three months later nobody talks about it any more. We developed a framework from our AI adoption work. This chapter is the map for it.
Aggregated from real rollouts · peak values anonymised
The framework at a glance
A successful rollout stands on two foundations — and then moves in two directions. Leave one out and you stay stuck in pilot limbo.
- Foundation 1 — AI strategy: a clear rollout plan and champions as multipliers. Not "who gets the tool" but "how does the whole organisation learn".
- Foundation 2 — AI platform: a platform that becomes the operating system for work — chat, assistants, workflows, knowledge and integrations in one secure place, instead of ten island solutions.
- Go broad takes AI to the breadth — to every employee.
- Go deep takes AI to the depth — into every system and every process.
The five stages of your AI journey
Maturity does not arrive overnight. Almost every organisation goes through the same five stages — the art is going through them deliberately and in the right order.
The first two stages are go broad, the last three go deep. The use-case map shows what people really do with AI at stage 2 — and measuring impact works out what that is worth.
Stage 1 — the three-month pilot programme
A pilot is not a tool test but a guided process with a clear goal: real, measurable use cases in three months. Four building blocks carry it:
Go broad — reaching every employee
AI creates its value in breadth, not in a lighthouse project. "Go broad" has two pillars: enabling people — and anchoring the learning directly in the tool.
- People enablement: champions as multipliers, community & best practice, events & training, expert sparring. → Change management
- Platform adoption: learning in the tool — a prompting coach, curated top use cases and prompts, guided starts. → Enablement & training
A good AI strategy is not a document but an operating model: who may do what, with which tool, on which data — and how does the organisation learn?
Go deep — integrating every system
For AI to take on real work, it has to reach your systems. Two routes, deliberately separated by risk:
- RAG search index (read-only): AI searches your knowledge — documents, structured data, specifications — without changing anything.
- MCP integrations (read & write): AI acts in your systems, with clean permissions. → Automation and governance & security go into depth here.
What matters
The four most common mistakes
The same stumbling blocks appear again and again in AI adoption work:
- A tool instead of an operating model — handing out licences is not a rollout.
- Starting too narrow — a single use case proves nothing; the value comes from breadth.
- Forgetting the champions — without multipliers, adoption peters out.
- Not measuring impact — measuring activity instead of value loses the management team.
Where to start
Start where the pain is large and the risk is small: recurring writing work, research, summaries. These early wins create the confidence for the more demanding stages. Look in the use-case map for where your team gains most — and take the matching assistants, workflows and prompts from the solution library.
Start with AI in your company
Together we find the right use cases, connect your systems, and bring AI into daily work in line with your business.