Pilot Your AI-Augmented Team
The metrics that actually matter when AI writes 70% of your code.
When Velocity Lies
When AI accelerates delivery, old benchmarks break. PRs per day goes up, which looks like progress, while masking quality and skill problems underneath. You need different sensors.
4 Metric Categories
Each one covers a blind spot the others miss. Together they give you a complete picture.
Delivery Health
DORAThe baseline. Automate these first.
- Deployment Frequency How often you ship to production
- Lead Time for Changes Commit to production elapsed time
- Change Failure Rate % of deploys causing incidents
- MTTR Mean time to restore after failure
Quality Signal
Where AI hides its mistakes.
- Bug Escape Rate Bugs found in prod per sprint
- PR Review Comprehension % of PRs reviewed with genuine understanding
- CI Speed P50/P90 Pipeline latency at median and 90th percentile
Product Impact
The layer most teams skip.
- Time-to-Value Days from feature start to first user value
- Feature Adoption (14-day) % of target users activating a feature within 2 weeks of release
- CSAT on Key Features User satisfaction score on high-investment areas
Human Health
SPACEWhat DORA doesn't see.
- Developer Satisfaction Quarterly CSAT survey (5 questions, anonymous)
- PR Review Time Avg hours from PR open to first review
- Burnout Signals After-hours commits, PTO utilization, qualitative check-ins
Start Small, Scale Right
The right metrics depend on your team size. Don't track what you can't act on.
5-person team
- Deployment Frequency
- Cycle Time
- Time-to-Value
- Bugs in prod/month
- Quarterly satisfaction
25-person team
- All 4 DORA (automated)
- Cycle Time per squad
- Bug Escape Rate
- AI contribution %
- Quarterly satisfaction
The 4-Question Test
For any metric you're considering tracking, run it through this checklist. Fewer than 3 "yes" answers? Drop it. It's noise, not signal.
When Will This Actually Ship
A velocity-based point estimate is a guess wearing a suit. Monte Carlo forecasting runs your own cycle-time history through thousands of simulated iterations and hands back a range, not a single date pulled from an average.
Reporting to a Board That Doubts You
When a board doubts delivery capacity after an estimate has slipped, adding more dashboards rarely fixes it. The doubt is almost always about trust, not about the number on the screen.
Sources & References
Every data point on this page is traceable. Here are the primary sources.
Read the full framework in the guide
Covers implementation playbook, tooling comparison, anti-patterns, and worked examples for squads already using Claude Code daily.