Sample curriculum

What the training actually looks like.

About 15 hours of live, hands-on training a year for each person in a training group, built on their own work and measured every quarter. This is a sample, the same for any organization. Your own sessions are built on your team’s results and your team’s work.

How it’s built

One rubric dimension per session. Each session works on one of the five dimensions in the AI Fluency rubric, not AI in general.
At the level the team is at. A team at Exploring and a team at Applied get different sessions on the same dimension, aimed at the next level up.
Using the team’s own work. Real documents, tasks and AI-assisted drafts, gathered before the session.

Each quarter, a training group of up to 30 people works on its two weakest dimensions below Applied. When no dimension is below Applied, training still continues each quarter on the team’s two lowest dimensions, at the Applied level, working toward Fluent. Leaders choose who is in the group; the dashboard shows which teams are furthest behind and never picks people.

A sample quarter

Two 90-minute workshops, a few weeks apart. One on each focus dimension.
A 45-minute practice clinic on real tasks from their own jobs.
A re-score at 90 days, which picks the next quarter’s focus. It shows whether answers changed, not what caused the change.

Over a year: 8 workshops and 4 clinics for each training group.

Example workshop: Verification, for a team at Exploring

Verification: critically evaluating AI output before acting on it. 90 minutes.

Time	Block	What happens
10 min	The team's results	Where this group sits on Verification, as a distribution rather than an average, and what Applied looks like on the rubric. Group figures only; nobody's score is shown.
20 min	Where it goes wrong	The ways AI output fails in this team's kind of work: invented specifics, confident gaps, arithmetic that does not add up, conclusions the source does not support. How to find the one claim that matters most in a piece of work, and check that first.
40 min	Hands-on, with their own AI-drafted work	Each person brings two recent pieces of work an AI tool helped draft. In pairs they check each against its source, mark what was checked and what was not, and fix what they find. The facilitator works the room.
15 min	A checklist they keep	The group writes a short checklist for its own work, scaled to the stakes: what always gets checked, what gets sampled, and what never leaves the team unchecked.
5 min	What happens before the clinic	Each person picks one real piece of work to run through the checklist before the practice clinic.

The practice clinic

45 minutes, after both workshops.

Time	Block	What happens
5 min	What came up	What people found when they used the checklist on real work, and where it got in the way.
30 min	Live work	Each person works a real task from their own job, with the focus dimensions in play, and a practitioner helps in the moment.
10 min	Adjust and keep	The group revises its checklist and working habits from what the session showed, and keeps the new version.

The full set

Ten workshops: one per dimension, at two starting levels. A team works on the ones its results point to.

Dimension	Starting level	What it works on
Awareness	Exploring	A working sense of what AI does well and badly in this team's own tasks, so fit is judged before the work starts.
Awareness	Applied	Anticipating where new tools and unfamiliar tasks will fail, and when a small trial is worth running first.
Instruction	Exploring	Giving the context, constraints, examples and definition of done a task needs, on work the team does every week.
Instruction	Applied	Diagnosing why an output missed, fixing the instruction or the context rather than retrying, and building patterns the team reuses.
Verification	Exploring	Checking output in proportion to the stakes: finding the claim that matters and confirming it at the source.
Verification	Applied	Knowing the failure modes of each kind of work, calibrating how much review it needs, and catching what a routine read misses.
Governance	Exploring	A clear, consistent line on which information goes into which tool, applied to the data this team actually handles.
Governance	Applied	Reasoning through new risks no rule covers yet, and explaining the tradeoff to whoever has to decide.
Integration	Exploring	Turning one recurring task into a documented, repeatable workflow, and showing what it changed.
Integration	Applied	Designing work around what AI does well, retiring steps that stop earning their place, and measuring outcomes rather than activity.

Optional programmes

Champion program ($3,000 one-time). A half-day session that prepares champions to reinforce the training between our workshops and carry it to teams in other time zones, using a facilitator kit built on the organization’s own workflows, kept current as Claude changes for as long as the subscription runs. The facilitator kit is built on each customer’s own workflows after they sign; it is not an off-the-shelf pack that exists in advance.

Engineering workshop (from $3,500). For technical teams: the same approach, scoped with the team and built on its own technical work.

What every session has in common

Built on real work. Each session uses the team's own documents, tasks and AI-assisted drafts, gathered before it starts.
At least half hands-on. People practise and get corrected. Talking at the room takes less than half of every session.
Something the team keeps. A checklist, a reusable instruction or a documented workflow, owned by the team after the session ends.
Taught to the skill. Never to the assessment. No session uses or rehearses the assessment's own scenarios, so a re-score measures judgment rather than memory.

Prices and what each plan includes are on the pricing page. To see the leadership view the training is chosen from, open the demo.