AI impact on pull request flow
AI impact on pull request flow shows up in GitHub as shifting merge throughput, review lag, and lead time — not in token bills alone. Engineering managers watch PR-level signals to see whether reviews keep pace as AI-assisted coding increases volume. DeliveryCompass measures from GitHub PR metadata; dedicated AI impact views are on the roadmap.
What should engineering managers watch?
- Throughput shift — more merges per week; human vs bot/dependency PR share
- Review lag — time to first review as volume grows (review responsiveness)
- Lead time — open-to-merge duration (lead time)
What can you measure from GitHub today?
Toggle Include bots on Overview and Team analytics for volume context when automation PRs increase. Compare period-over-period on the same definitions.
What is not in the pilot?
DeliveryCompass does not track AI token spend or vendor billing. AI-assisted vs human attribution and dedicated AI impact dashboards are exploring on the product roadmap — do not treat them as shipped.
FAQ
How does AI affect pull request throughput?
Teams often see more PRs opened and merged. The coaching question is whether review capacity scales with volume.
Can I measure AI impact on code review from GitHub alone?
Partially — review lag and throughput trends help. Full AI attribution needs metadata we are exploring on the roadmap.
How should I frame AI metrics in staff meetings?
Use comparative team trends, not stack ranking. Pair with weekly summary attention callouts.
Pilot scope
Early Access — PR statistics only, daily sync. See pilot scope.