AI Rollout

Last updated: September 2026

You’re launching (or accelerating) Claude Code, Codex, or Cursor across your org. This playbook is how to use the dashboard as the operational backbone of that rollout.

The problem

Most AI tooling rollouts fail not at the install step but at the adoption-depth step. Developers get licenses, install the tool once, use it for a week, and drift away. The dashboard’s job is to surface that drift early and give you a structured way to intervene.

This is the playbook for the first 6 months of an active rollout.

Where to look

Three operational views — each has a different cadence:

Cadence View What you’re checking
Weekly /developers, sorted by Adoption Score ascending The Emerging cohort. Who’s stuck? Who’s ramping?
Bi-weekly /teams tier mix bars Team-level momentum. Which teams are pulling ahead, which are stalled?
Monthly /executive trend lines The headline rollout health story. What goes in the leadership update?

What to do — the 6-month rollout playbook

Month 1 — Set the baseline

Goal: Establish where you are before the rollout intervention starts.

  1. Set Maturity Factor to 0.65 in Settings → General. Lower than the 0.75 default. In the first 3 months, you want the tier ladder to feel achievable. A dev who picks up Claude this month and uses it consistently for 4 weeks should land in Regular, not get stuck in Explorer because the org P90 hasn’t shifted yet. Raise back to 0.75 in month 4.
  2. Keep Tier Weights at defaults (0.5 / 0.2 / 0.3). Adoption-heavy is right for early rollouts.
  3. Take a baseline snapshot. Note the org’s Adoption %, Power User %, AI Adoption %. Write them in your rollout doc. You’ll cite these in month 3 and month 6.
  4. Roll out licenses to your launch cohort. A pilot team or two is usually the right scope. Don’t try org-wide on day one.

Month 2 — Activate the pilot

Goal: Get the pilot cohort from Emerging to Explorer+.

  1. Set up the weekly Emerging review. Every Friday, open /developers filtered to the pilot teams, sorted by Adoption ascending. Walk the bottom 10. For each: do they need a tooling fix, a pairing session, or a usage-pattern intro?
  2. Run an AI office hour. 30 minutes weekly. Open invitation. Show patterns. The single highest-leverage intervention in week 2–4.
  3. Pair a Power User with an Emerging developer. One hour of paired work. Watch the Emerging developer’s score climb in the next 2-week window.

Month 3 — Pilot review, expand the cohort

Goal: Pilot retro + expand to next cohort.

  1. Pull the month 1 baseline. Compare to today’s numbers. Honest read: did the pilot work? Pilot team’s Adoption % (target: 60%+ at Explorer+), pilot team’s tier mix (target: ≥20% in Regular), pilot team’s Output Score trend (target: flat or up — Output usually lags Adoption by 1-2 months).
  2. Document what worked. Office hours, pairing, internal Slack channel — whatever the pilot proved. Codify it as the rollout SOP.
  3. Expand to the next cohort. Apply the same playbook to 2–3 more teams.

Month 4 — Watch the Power User cohort

Goal: Make sure the pilot cohort doesn’t regress.

  1. Raise Maturity Factor to 0.75 (the default). The pilot cohort has earned the standard ceiling.
  2. Watch the pilot cohort weekly. Some developers will regress (the tool stops being novel; they fall back to old habits). Catch the regression in /developers — the trend sparkline shows it before the score does.
  3. Re-engage regressors. Same playbook: pairing, office hours, audit their toolchain.

Month 5 — Org-wide expansion

Goal: Make AI tools the default, not the opt-in.

  1. All new developers get licenses on day 1. Built into onboarding. New hires landing in Emerging after 8 weeks is a process flag.
  2. Watch the Maturing-team cohort. Some teams will plateau at “everyone’s an Explorer, no one’s a Power User.” That’s the “broad but shallow” pattern — start working on the Agent Autonomy Score by running agentic-pattern workshops.

Month 6 — Strategic review

Goal: Decide what’s next.

  1. Pull the 6-month report. Org Adoption %, Power User %, Productivity Uplift, AI-Assisted %. Compare to month 1 baseline.
  2. If the rollout worked: Consider shifting Tier Weights toward Output (e.g. 0.4 / 0.2 / 0.4). You’re now in the Maturing phase. (Tier Weights are configured in app_settings per-org and editable in Settings → General.)
  3. If parts didn’t work: Identify the laggard teams. They usually share a root cause (one team lead who’s skeptical, one repo with bad CI, one process gap). Address structurally rather than per-developer.
  4. Plan the next quarter: Is the focus broadening adoption (still teams to onboard), deepening adoption (Power User % to grow), or extracting value (Output Score / Productivity Uplift to push)?

What to expect

A typical “successful” rollout trajectory across 6 months:

Month Adoption % Power User % Productivity Uplift
1 (baseline) 5–15% 0–2% 0%
2 15–30% 1–5% 0–5%
3 30–50% 5–10% 5–10%
4 45–65% 10–18% 10–15%
5 60–80% 15–25% 15–25%
6 70–90% 20–35% 20–35%

These are typical ranges, not guarantees. Some orgs move faster (greenfield product teams), some slower (enterprise with platform gating). The shape is what matters: Adoption rises first, Power User % follows with a 1–2 month lag, Uplift follows Power User by another 1–2 months.

If your trajectory has Adoption climbing but Uplift flat at month 4+, your rollout is broad but shallow. Pivot to agentic workshops and pairing.

Common mistakes

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