Plan the human rollout of an AI tool or workflow to an organisation — sized cohorts, enablement per cohort, named champions, a comms plan that states what will not change, and an adoption gate every wave must pass before the next one opens. Use when a tool has been chosen and the question is how it reaches people without stalling or backlash. Do NOT use to phase a technical cutover (that's migration-plan) or to choose which use case to roll out (that's ai-use-case-triage).
In Claude Code, with the ai-engineering plugin installed, invoke it as:
/ai-engineering:ai-adoption-rollout
Try it
A realistic prompt from the skill's acceptance evals:
We've picked an AI assistant for our 300-person support org. Plan the rollout.
What a good result looks like
Cohorts get smaller-to-larger with a stated reason each, and the plan refuses a single big-bang wave.
Every wave has an adoption gate with an observable, a threshold, and a bake time — plus a rollback if it fails.
The outcome is a behaviour, not a usage number. Licences activated is not adoption.
The comms plan states explicitly what will not change (headcount, evaluation, how work is judged). Omitting it is a defect, not a tone choice.
Champions are roles with protected hours, never names, and never unbudgeted.
Enablement is per cohort and hands-on with the participant's own work — a single deck for everyone is the failure mode.
The friction loop names an owner and a cadence.
How it's checked
Measured against plain Claude Code (2026-10-23): it met 73% of its checks, against 48% without it, and a blind judge preferred it. How skills are checked
22 assertions across 3 acceptance evals, and 8 trigger queries. Every skill's checks
What the output must do
Cohorts are sized smallest-first (pilot, early, general) with a stated reason each cohort is next
A single big-bang wave is explicitly refused
Every wave has an adoption gate with an observable, a threshold, and a bake time, plus a rollback if the gate fails
The adoption outcome is stated as a behaviour rather than a usage or licence number
The comms plan explicitly states what will not change regarding headcount, evaluation, or how work is judged
Champions are named as roles with protected hours
Enablement differs per cohort and is hands-on with the participant's own work
A friction loop names an owner and a cadence
It shows that all 1,400 nurses by 1 December cannot be met with 4 educators training 25 nurses each per week (about 14 weeks of training, plus the 21 December to 3 January freeze), says so plainly, and proposes a realistic date or a smaller first scope
It builds in at least 30 days' written notice and consultation with the union before nurses outside the pilot go live
It does not present the 52-minutes-per-shift saving as established, noting that it comes from 11 questionnaire responses out of 60 pilot nurses
It holds back areas with the Wi-Fi dead spots (Ward 7 bays C and D, at least on nights) until the connectivity problem is fixed, or makes going live there conditional on the fix
Its staff messages state the CNO's commitments (no use in performance management, no staffing reductions, nurses review every note) and do not add commitments the CNO did not make
It does not invent figures, dates, names or results that are not in the files
It is no longer than two pages
It gives a plan in this reply rather than asking questions first
It starts with a smaller group, such as the keen volunteers, before everyone, rather than all 22 teachers at once
It deals with the teachers who are against it by hearing their objections, without forcing them or singling them out
It suggests the head tells staff plainly what will not change, for example that teachers stay responsible for their plans or that the tool will not be used to judge them
It includes a check before widening, saying how the head will know whether the first group found it useful
It does not invent features of the tool, a product name or survey figures
It is about half a page (roughly 300 words or fewer)
When it should and shouldn't activate
Should activate:
Plan how we roll this AI tool out to the org
How do we drive adoption without a backlash?
Design the cohorts and enablement for our AI launch
Our last AI rollout decayed after launch — plan the retry
Should leave to another skill:
Plan the phased migration of our database schema
Which use case should we roll out first?
Write the rules for what people can put into these tools