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 this directly: run a free AI delivery impact report on any public repository — no account and no app installation.
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 supported?
DeliveryCompass does not track AI token spend or vendor billing, and it does not attribute individual pull requests to AI vs human authorship — GitHub metadata cannot tell them apart. The impact report is descriptive: it shows what changed in two windows of history, never that a tool caused the change.
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?
Partly. Throughput, lead time, first-review wait, PR size and revert rate are all measurable from pull request metadata, and the impact report compares them before and after a cutover date. What metadata cannot show is attribution — which specific pull requests were AI-assisted.
How should I frame AI metrics in staff meetings?
Use comparative team trends, not stack ranking. Pair with weekly summary attention callouts.
Product scope
PR statistics only, daily sync. See product scope.