Engineering Flow
AI-first executive view · Custom scope · last 30d
AI Bottom Line
Output increased to 77 issues done, but flow remains constrained by queueing and wait states: median active lead time is 142.9 hours, with most of that spent reaching ready rather than executing work.
Headline KPIs · last 30d
AI insight
- Where AI helps:No demonstrated AI help is measurable this period because AI-owned work was 0% and AI participation was 0%.
- Where AI hurts:The AI-touched cohort shows much slower cycle, lead, and time-to-ready metrics than the overall baseline, but this is directional only because it covers just 2 items.
- Next focus:Reduce in-flight inventory and clear old waiting states, starting with failed testing and long-aged client/on-hold items.
AI in teams · 1 team
89 completed · 1.1% AI-touched · 0 active agents
Bottom line
PU improved (78.0%) in throughput. AI cohort too small for cycle comparison (n=0/64); no specific AI bottleneck stands out.
Issues done
89
+78.0% vs prev
AI-owned share
0%
+0.0 pp vs prev
AI-touched
1 (1%)
Lead Time (active)
8.2d
prev 8.6d
Cycle Time (active)
1.5d
prev 5d
TTR p50
5d
prev 3.9d
PR Review p50
—
WIP active
127
WIP blocked
32
AI insight
Where AI helps
No AI-owned metric is currently faster than the human cohort.
Where AI hurts
No AI-owned metric is currently slower than the human cohort.
AI cohort too small for direct comparison on this team.
Top AI bottleneck
no AI-owned cohort with stage data
Active agents · top 0
none in window
Active bottlenecks · top 5
Top teams · by throughput
| Key | Team | Throughput | Lead Time (active) | Cycle Time (active) | Active WIP | Blocked WIP | AI % |
|---|---|---|---|---|---|---|---|
| PU | Portfolio Upkeep | 89 | 8.2d | 1.5d | 127 | 32 | 0% |