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AI Coaching

Senior, hands-on coaching for the leaders and teams who have to work with AI every day: fluent enough to explain it, capable enough to build with it, and clear about where it belongs in their own role.

Leaders and delivery teams Hands-on, not lecture Built around your real work From readiness to practice

Level up the software and the people at the same time.

Software that people do not understand does not get used, and people who understand AI without a system to apply it to stay at the demo stage. We coach the two together: the team learns on its own real tasks, and what it learns feeds straight into how the solution is built and adopted.

How a programme is built

00 · BASELINE Start from a readiness assessment

A short assessment before the first session: how the team feels about AI, which tools it uses, how it prompts, what it fears. The programme is tuned to the room, not to a generic syllabus.

01 · FLUENCY Use it, understand it, explain it

Everyone solves a real task from their own week with AI, then learns enough about models, tokens and cost to explain it to a customer or a colleague. Roughly half talk, half doing.

02 · BUILD Demos, agents and prompt craft

Hands-on sessions where participants stand up something that works, and learn when an agent is the answer and when deterministic automation is. Mostly doing.

03 · APPLY The value story in their own role

A repeatable framework for the participant’s own job, practised out loud with real cases. For a pre-sales team: identify, qualify, demonstrate, position, close.

How it runs

  • Two-hour live sessions, typically three, one to two weeks apart, with pre-work and homework in between
  • Groups of up to 25
  • Co-facilitated with a manager from your side, so there is no dependency on us afterwards and the manager sees the team’s progress first-hand
  • Reference material sent in advance so live time goes to practice, not slides

What you get

  • A team that uses AI daily on its own work, with a shared vocabulary
  • People who can demonstrate and explain, not just describe
  • A decision rule for agent versus deterministic that matches how we build
  • A manager equipped to keep the practice going

The people who own the system should own the solution.

Which team has to live with the outcome?

Tell us who will use, sell or defend the AI you are putting in place. We will propose a programme built on their real work.