Engineering Flow
AI-first executive view · Custom scope · last 30d
AI Bottom Line
In the last 30 days, the team closed 12 issues with low active WIP and no blocked work, but median lead time remained 74.6 hours and the main delay sat in review readiness rather than intake.
Headline KPIs · last 30d
AI insight
- Where AI helps:AI-owned work reached 75% of completed items, and AI-touched activity covered all 12 completed issues, showing broad operational use.
- Where AI hurts:AI-owned items were slower than the human subset on cycle time in this window, and the main AI-owned bottleneck was also Ready For Review. This is directional only because the human side sample is very small.
- Next focus:Reduce review-stage wait time first.
AI in teams · 1 team
12 completed · 100.0% AI-touched · 2 active agents
Bottom line
TAA improved in throughput. AI-vs-Human cycle comparison is directional only (sample too small) (n=9/3); the main AI bottleneck is Ready For Review.
Issues done
12
n/a vs prev
AI-owned share
75%
+75.0 pp vs prev
AI-touched
12 (100%)
Lead Time (active)
3.1d
Cycle Time (active)
1.3d
TTR p50
0.1h
PR Review p50
—
WIP active
2
WIP blocked
0
AI insight
Where AI helps
No AI-owned metric is currently faster than the human cohort.
Where AI hurts
No statistically stable AI regression detected. Directional-only signals below numerically favor humans but sample size is below threshold.
Directional only (sample too small)
- · Cycle Time (active): AI 3d vs Human 11.1h (n=9/3, below 20)
- · Lead Time (active): AI 3.1d vs Human 1.6d (n=9/3, below 20)
- · Time to Ready p50: AI 0.1h vs Human 0.1h (n=9/3, below 20)
Top AI bottleneck
Active agents · top 2
Active bottlenecks · top 5
Top teams · by throughput
| Key | Team | Throughput | Lead Time (active) | Cycle Time (active) | Active WIP | Blocked WIP | AI % |
|---|---|---|---|---|---|---|---|
| TAA | Test AI Agents | 12 | 3.1d | 1.3d | 2 | 0 | 75% |