AI Rollout Planner
Generate a phased adoption plan for agentic coding tools across your engineering org.
Pilot cohort, phase durations and guardrail strictness all rescale live as you adjust inputs.
Live plan preview
# Agentic Coding Rollout Plan **Org size:** 120 engineers · **Current adoption:** 10% · **Target timeline:** 6 months · **Risk posture:** balanced > Adoption is starting near zero. Phase 1 emphasizes recruiting motivated volunteers and producing internal before/after evidence. ## Phase 1 — Pilot cohort (1.50 months) **Cohort:** 8 developers (~7% of org) across at least 2 teams. **Goals** - Validate the sanctioned toolchain on real work, not demos. - Document the working setup: config, prompts, review workflow. - Establish per-developer baseline cost (expect wide variance). **Success metrics (gate for phase 2)** - ≥60% weekly active usage among pilot seats. - PR revert rate on AI-assisted changes ≤ team baseline. - Per-developer spend within the budgeted band for 2 consecutive weeks. **Guardrails** - Secret scanning in pre-commit before any pilot seat is provisioned. - Per-developer spend visibility from day one. - AI-generated code is reviewed to the same standard as human code before merge. ## Phase 2 — Expansion (2 months) **Cohort:** grow to ~36 developers; prioritize teams adjacent to pilot champions. **Goals** - Convert pilot learnings into onboarding docs and a 1-hour training. - Stress-test review and budget processes at 3-5× pilot volume. - Surface team-level differences (monorepo vs services, legacy code). **Success metrics (gate for phase 3)** - ≥60% weekly active across all expansion seats. - Onboarding time to first productive session ≤ 1 day. - Zero unresolved budget anomalies older than one business day. **Guardrails** - Hard per-team budget caps with alerts at 50/80/100%. - Named budget owner per team (engineering manager). - Incident path documented and tested with one tabletop exercise. ## Phase 3 — Org-wide (2.50 months) **Cohort:** all 120 engineers provisioned; opt-out, not opt-in. **Goals** - Tool access is part of standard onboarding. - Quarterly review cadence for spend, quality and tool landscape. - Retire shadow/unapproved tool usage by making the sanctioned path easier. **Success metrics (steady state)** - ≥70% weekly active org-wide within 8 weeks of full provisioning. - Cost per merged PR tracked and trending stable or down. - Policy exceptions all have a named approver and an expiry date. **Guardrails** - Org-level budget with automated enforcement (alert → throttle by posture). - Quarterly policy and vendor review. - AI-generated code is reviewed to the same standard as human code before merge. --- *If a phase misses its gate: hold, fix the named blocker, re-measure. Do not advance on hope.* *Generated with the FORG AI Rollout Planner (forg.pro/tools/ai-rollout-planner).*
How it works
This planner turns four inputs — org size, current adoption, target timeline and risk posture — into a complete phased rollout plan for agentic coding tools, rendered as markdown you can copy or download and drop straight into your planning doc. Everything computes in your browser; nothing you enter is transmitted anywhere. The structure is the one that survives contact with real engineering organizations: pilot cohort first, structured expansion second, org-wide rollout last, each with explicit goals, success metrics and guardrails.
The math behind the plan is deliberately simple and visible. Pilot cohort size scales with org headcount and tightens under a conservative posture; phase durations split your target timeline roughly 25/35/40 across pilot, expansion and org-wide, with the split shifting toward longer early phases when risk tolerance is low. Current adoption matters too: if a third of your org is already using these tools informally, the plan skips the evangelism and goes straight to formalizing what shadow usage already proved.
Each phase carries three things rollout plans usually omit. Goals are stated as measurable outcomes, not activities — "60% weekly active among provisioned seats" rather than "run training sessions." Success metrics are the gate for the next phase, so the decision to expand is a measurement against a number agreed in advance. Guardrails name the controls that must exist before the phase starts: secret scanning before any pilot, budget caps before expansion, incident paths before org-wide. A rollout without guardrails is how teams end up explaining a five-figure anomaly to finance.
Treat the output as a strong first draft to argue with, not gospel. Your org chart, your compliance regime and your existing tooling all bend the details. What the generated plan guarantees is that you will not forget a phase gate, ship without spend controls, or measure success by enthusiasm. The budget guardrails it prescribes are exactly the kind of per-developer tracking and hard caps that FORG enforces automatically — the plan says what should happen, tooling makes it binding.
Frequently asked questions
Why start with a pilot cohort instead of rolling out to everyone?
Because the failure modes of agentic coding tools are organizational, not technical. A pilot of motivated volunteers surfaces the real blockers — review bottlenecks, secret-handling gaps, runaway spend — while the blast radius is a handful of people. Pilots also produce internal champions whose before/after numbers persuade skeptics far better than any vendor deck. Skipping the pilot means discovering those problems at full scale, where they cost real money and credibility.
How big should the pilot cohort be?
Five to ten percent of the engineering org is the working range, with a floor of three developers so results are not one person's opinion. The planner scales this from your org size and risk posture: a conservative posture trims the cohort and lengthens each phase, an aggressive one widens it. Pick volunteers across at least two teams so the pilot tests your processes, not just one team's culture.
What success metrics actually matter during a rollout?
Three families: adoption (weekly active users against seats provisioned — anything under 60% means the tool or the training is wrong), output quality (PR revert rate and review pushback on AI-assisted changes versus baseline), and cost discipline (spend per developer against the budgeted band). Vanity metrics like total suggestions accepted tell you nothing about whether the org is genuinely faster. The generated plan embeds a concrete metric set per phase so the go/no-go decision at each gate is a measurement, not a vibe.
What happens if a phase misses its success criteria?
You hold, you do not skip. The plan treats each phase boundary as a gate: if adoption stalls or quality metrics regress, the right move is to extend the current phase, fix the named blocker, and re-measure — not to push forward hoping scale solves it. Most stalled rollouts trace to one of three fixable causes: no sanctioned workflow documentation, review processes that punish AI-assisted PRs, or missing budget guardrails that make managers nervous about usage.
How does risk posture change the generated plan?
It rescales durations, cohort sizes and guardrail strictness across all three phases. Conservative postures get smaller pilots, longer observation windows and mandatory human review of all AI-generated changes through phase two. Aggressive postures compress timelines and widen cohorts but keep the same gates — speed comes from shorter phases, never from skipping measurement. Balanced sits between, and is the right default for most teams without regulatory pressure either way.
Budgeting AI spend for a team? FORG is $15/dev with hard budget caps and per-seat attribution.
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