Run In Our Own Companies First
This is the curriculum we use to roll AI out inside our own family business group, then teach.
The whole programme at a glance — how it is structured, sequenced, and measured.
The curriculum is built on one rule. About seventy percent of every session is a shared core — identical for finance, sales, HR, operations, everyone. One company, one AI language, one safety standard. The remaining thirty percent is the use-case layer, swapped for each role, so people apply the same skill to the work in front of them.
This is what keeps a rollout coherent, and it is what makes it efficient: the core is reused every session, and only the examples change. See how the layer differs by role in the role tracks.
Each role moves through the same five-session skeleton, with role-specific content inside it. The skeleton is deliberately simple: demystify and get one real result on day one, build the habit, deepen the use cases, sharpen the checking, then connect to a dashboard.
What AI is, the three rules, and one genuine task done live — a real result before anyone leaves the room.
AI drafts, summarises, and translates; the draft-read-correct-send habit starts to form.
Three or four repeatable prompts for the person's actual job, saved as reusable helpers.
Context, task, and format sharpened; the habit of catching confident-but-wrong answers.
Where a stronger tool earns its place — one account per department, introduced when the need is felt.
Alongside the five sessions runs a fuller twelve-step arc for the foundation itself, from a first prompt to a report tied to the job — set out on the foundation page.
The weekly report is the spine of the programme — it turns scattered experiments into a visible record. It starts trivially small and adds one layer each week, so it never feels like paperwork. By the last week it is tied to the person's job description and is the clearest signal of who is actually using AI at work.
Running the programme surfaced one finding twice, in separate cohorts: left to themselves, people summarise something about AI rather than using it on a real task. A summary is not the exercise. So every assignment forces an applied task — a draft you actually sent, a helper you reuse, a document you had to produce anyway. Applied work is what moves someone up the maturity ladder; a summary does not.
The homework is always the same shape: use AI on one real task from your own week, then note where it helped and where it fell short. That note becomes the next session's starting point.
The programme is tool-agnostic in principle and deliberately staged in practice. Teams start on one assistant to build the habit without confusion, and add a more capable tool only at the point where a dashboard or a heavier workload makes the case for it.
One assistant connected to email and calendar. Build the habit with a single tool before adding any others.
A more capable tool per department for dashboards and heavier work — introduced at Session 5, when the need is real.
The sequence matters more than the brand: build the discipline first, then let the tools follow the work. From there the path leads to Claude as a coworker, Claude Code, and an AI operating system.
This is the curriculum we use to roll AI out inside our own family business group, then teach.
The curriculum is not a course written to be sold. It is how our own family business group brings AI into daily work — the same foundation, the same weekly report, the same tooling path. We run it in our companies first, then teach it.
One shared foundation for the whole team, applied to each person's real work.
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