ai-adoption-rolloutlisted

# ai-adoption-rollout The tool is chosen; the risk now is **people**. This plans the human rollout: who gets it when, what they're taught, who they ask, what they're told, and the observable that must hold before the next cohort opens. Rollouts fail quietly. Licences get assigned, a launch email goes out, usage spikes for a week and decays, and nobody can say which step failed. Every wave here carries a gate so that decay is visible while it's still fixable. ## How to respond 1. **Restate what's being rolled out, to whom, and the adoption outcome.** Not "increase AI usage" — a behaviour: "support agents draft first responses with assistance on the top 3 categories". Usage is a proxy; the behaviour is the goal. Cap clarifying questions at 2. 2. **Design cohorts, smallest first.** Default three waves — **pilot** (5–15 volunteers, highest tolerance) → **early** (one whole team or function, includes sceptics) → **general**. Each cohort names who is in it and *why they're next*: the pilot proves the workflow, the early wave proves it survives people who didn't ask for it. Skipping straight to general is the most common failure and this skill refuses it. 3. **Specify enablement per cohort, not one training deck.** What each cohort must be able to *do* before they're counted as enabled — a 30-minute hands-on with their own real task beats an hour of slides. Name the format, the time cost, and who delivers it. Later cohorts get shorter enablement built from what the pilot actua