AI education
The Field School.
AI education for leaders and teams, taught by the engineers and operators who build with you: in your tools, on your own work, and within the guardrails your business already answers to.
I / Who it is for
Everyone who will touch the work.
Executives and boards
The economics, the risks, and the handful of decisions only leadership can make.
Functional teams
Finance, sales, operations, people, and legal, each in its own tools and on its own work.
Internal owners
The people who will run what we build together, and choose what comes next.
Mission-driven teams
The same curriculum, fitted to small teams and smaller budgets.
II / Formats
From an afternoon to a residency.
Executive briefing
Half a day
Where AI pays in your business and where it does not, what it costs to run, and the decisions leadership must make first.
Leadership working session
One day
Your leadership team chooses its first three bets, each with a business case, an owner, and a way to measure it.
Function workshops
Ninety minutes each
Hands-on sessions for a single team, in its own tools and on its own work, ending with a workflow it can use the next morning.
Champions program
Six weeks
A cohort of internal owners who learn to scope, evaluate, and maintain AI workflows. They are the people we hand over to.
Residency
Eight to twelve weeks
Your engineers and operators work inside our pod on live work, and leave able to build and run it themselves.
Office hours
Weekly
A standing session for questions, reviews, and the problems nobody planned for.
Policy and governance kit
Tailored to you
An acceptable-use policy, data-classification guidance, evaluation templates, and a review process fitted to your regime.
III / Curriculum
Six modules, fitted to you.
Taught in the tools you already pay for, such as ChatGPT, Claude, Gemini, and Microsoft 365 Copilot. Technical teams also work with coding assistants such as Claude Code, Cursor, and GitHub Copilot.
- i.
Foundations
How the models work, where they fail, and what they cost to run.
- ii.
Working well
Context, instructions, and the habit of checking the answer.
- iii.
From task to workflow
Redesigning work so AI can take on its part safely, with people where judgment lives.
- iv.
Judging quality
Small evaluations built from your own cases, so ‘good enough’ means something.
- v.
Risk and responsibility
Data, privacy, intellectual property, bias, and what your policies allow.
- vi.
Running it
Ownership, monitoring, costs, and when to retire a workflow.
IV / How we measure
Adoption, not attendance.
- A short skills check at the start and the end
- Weekly use of the tools you already license
- Workflows each team adopts and keeps
- Hours returned: estimated first, then measured
We keep a record of who was trained and on what, for teams that must show their AI-literacy measures to a regulator or a board.
Teach the estate to tend itself.
Start with an executive briefing, or fold the Field School into an engagement so the people who run the work learn it while it is built.