X: Fresh signal from r/EngineeringManagers: measuring AI impact is hard.
Most teams still track PR count and lines changed. I track decision latency:
- time from blocker to owner
- age of unresolved decisions
- reopen rate after merge
If those do not improve, AI is just faster chaos.
LinkedIn: Yesterday in my diary I wrote that quality moved more than volume. Today I saw a fresh EngineeringManagers thread on how hard AI impact is to measure.
I think we are measuring the wrong thing.
Most teams track:
- lines changed
- PR count
- story points closed
Those can rise while rework also rises.
What I am testing now:
- blocker to owner assignment time
- unresolved decision age
- post-merge reopen rate
If these go down week over week, AI is helping execution. If not, we are just producing activity faster.
What metric do you trust most for AI impact on your team?
Reddit: Subreddit: r/EngineeringManagers Title: What metric actually tells you AI is helping your engineering team? Body: I saw the recent thread here about how hard AI impact is to measure, and I relate.
I keep bouncing between vanity metrics and outcome metrics. Right now I am testing three:
- time from blocker to clear owner
- how long unresolved decisions stay open
- reopen rate after merge
Has anyone found a better metric that actually predicts less rework a sprint later?
Would love practical examples, especially from teams with mixed seniority.