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of organizations use AI in at least one function. Only 39% report any measurable impact on EBIT.- McKinsey, 2025
of developers use AI coding tools. Only 22% of AI-authored code ships without major rewrites.
of engineering leaders cite lack of clear metrics as their biggest AI challenge. - LeadDev, 2025
Adoption, Execution, Guardrails, Integrity, Sustainability — five lenses that show how AI is reshaping delivery behavior, not just accelerating it. Includes a detailed reference table for each dimension.
Scale, Stabilize, Investigate, or Step Back — four quadrants that tell you what to do next, based on where your impact and risk signals actually sit. Built for quarterly leadership reviews.
Five questions. Five minutes. Places your organization on the decision matrix so you can walk into your next leadership meeting with a starting point, not a guess.
| Dimension | What It Means | What to Measure | Level | How to Benchmark | Common Failure Modes |
|---|---|---|---|---|---|
| A: Adoption | Degree to which AI is embedded in real workflows (not licenses) | Active vs enabled users, AI-assisted PR share, adoption distribution across teams | Team, System | Benchmark against own baseline by team and repo type; compare AI-heavy vs AI-light cohorts over same window | High adoption can be cosmetic; concentrated usage masquerades as org adoption |
| E: Execution | How AI changes SDLC flow mechanics and where work/time moves | PR cycle time distribution (p50/p75/p95), pickup time, review time, throughput, PR size distribution | Team, System | Use distributional baselines (tails matter). Interpret deltas by work type (feature vs maintenance vs refactor) | 'Faster' may be local to authoring while review slows; averages hide tail pain; PR size inflation degrades comprehension |
| G: Guardrails | Quality control and risk containment under AI-assisted delivery | Rework proxies (follow-up fixes), quality trends on AI-heavy PRs, review depth proxies, rollback/incident linkage | Team, System | Benchmark against pre-AI or early-AI periods; 'no change' is a valid target for risk metrics while pursuing execution gains | Quiet quality drift is the core risk; good short-term throughput accumulates long-term correction cost |
| I: Integrity | Human trust, judgment, and cognitive load in AI-assisted engineering | Dev confidence in AI outputs (survey), perceived verification burden, reviewer confidence | Individual (aggregated), Team | Benchmark directionally using internal survey baselines; look for divergence across teams adopting AI differently | Over-trust increases risk; under-trust adds review tax; if engineers feel measured, signal becomes unreliable |
| S: Sustainability | Whether gains are durable, equitable, and stable | Review load concentration, burnout risk signals, sustained DevEx trends, variance across teams | Team, System | Benchmark stability over multiple cycles; focus on reducing variance and reviewer overload | Sustainability failures invalidate execution wins; high velocity with rising concentration and fatigue is brittle |
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