Engineering Flow
AI-first executive view · Custom scope · last 30d
AI Bottom Line
Throughput fell sharply this period while flow remained slow and highly blocked, with most elapsed time accumulating before work becomes ready rather than during active execution.
Headline KPIs · last 30d
AI insight
- Where AI helps:AI-touched items showed lower lead time and faster time to ready than the team baseline, but the sample is directional only.
- Where AI hurts:AI-touched items had longer cycle time than the team baseline; with only two items, treat this as directional only.
- Next focus:Reduce blocked WIP and clear hold/testing-failure queues before pulling additional work.
AI in teams · 1 team
21 completed · 9.5% AI-touched · 2 active agents
Bottom line
TDOT declined (-61.8%) in throughput. AI cohort too small for cycle comparison (n=0/21); no specific AI bottleneck stands out.
Issues done
21
-61.8% vs prev
AI-owned share
0%
+0.0 pp vs prev
AI-touched
2 (10%)
Lead Time (active)
24d
prev 22.2d
Cycle Time (active)
4d
prev 8.6d
TTR p50
19.9d
prev 7d
PR Review p50
—
WIP active
28
WIP blocked
18
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 2
Active bottlenecks · top 5
Top teams · by throughput
| Key | Team | Throughput | Lead Time (active) | Cycle Time (active) | Active WIP | Blocked WIP | AI % |
|---|---|---|---|---|---|---|---|
| TDOT | Tripledot | 21 | 24d | 4d | 28 | 18 | 0% |