Engineering Flow
AI-first executive view · Custom scope · last 30d
AI Bottom Line
Throughput fell while active flow remained slow and congested; the immediate management issue is reducing waiting states and intake aging rather than increasing activity.
7 cautions ›
- · ADS — AI cohort n=1 (<10) — Lead delta directional only
- · IMC — no AI-assigned completions in 30d — cohort comparison unavailable
- · PDO — AI cohort n=1 (<10) — Lead delta directional only
- · PU — no AI-assigned completions in 30d — cohort comparison unavailable
- · TAA — AI cohort n=9 (<10) — Lead delta directional only
- · TDOT — no AI-assigned completions in 30d — cohort comparison unavailable
- · TECH — no AI-assigned completions in 30d — cohort comparison unavailable
Headline KPIs · last 30d
AI insight
- Where AI helps:AI-owned and AI-touched work both show materially lower median Jira flow lead time versus their prior period, and time to ready is near-zero in both cohorts.
- Where AI hurts:Cycle time moved the wrong way in both AI cohorts, with the broader AI-touched cohort worsening more than the AI-owned cohort.
- Next focus:Focus the next operating review on draining aged Ready inventory and aggressively clearing external/client waits before adding more WIP.
AI in teams · 7 teams
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
78 completed · 41.0% AI-touched · 1 active agent
Bottom line
EVO declined (-46.9%) in throughput. AI-owned work moves faster on cycle (n=24/50); the main AI bottleneck is Ready To Merge.
Issues done
78
-46.9% vs prev
AI-owned share
36%
+15.5 pp vs prev
AI-touched
32 (41%)
Lead Time (active)
21.1d
prev 19.8d
Cycle Time (active)
6.7d
prev 6.8d
TTR p50
10.3d
prev 7.1d
PR Review p50
—
WIP active
42
WIP blocked
5
AI insight
Where AI helps
- · AI Cycle Time (active) 6.4d vs Human 7d (n=24/50)
- · AI Lead Time (active) 7.1d vs Human 30d (n=28/50)
- · AI Time to Ready p50 22.6h vs Human 14.3d (n=24/50)
Where AI hurts
No AI-owned metric is currently slower than the human cohort.
Top AI bottleneck
Active agents · top 1
AI Period: compare · intro 2026-04-02
18 completed · 5.6% AI-touched · 1 active agent
Bottom line
PDO declined (-63.3%) in throughput. AI-vs-Human cycle comparison is directional only (sample too small) (n=1/14); the main AI bottleneck is Ready For Review.
Issues done
18
-63.3% vs prev
AI-owned share
6%
+3.6 pp vs prev
AI-touched
1 (6%)
Lead Time (active)
28.7d
prev 31.8d
Cycle Time (active)
7.9d
prev 4d
TTR p50
20.8d
prev 19.1d
PR Review p50
—
WIP active
15
WIP blocked
6
AI insight
Where AI helps
No statistically stable AI advantage detected yet. Directional-only signals below numerically favor AI but sample size is below threshold.
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 41.1d vs Human 7.7d (n=1/14, below 20)
- · Lead Time (active): AI 41.1d vs Human 28.7d (n=1/17, below 20)
- · Time to Ready p50: AI 1.8h vs Human 20.8d (n=1/13, below 20)
Top AI bottleneck
Active agents · top 1
AI Period: compare · intro 2026-03-30
42 completed · 38.1% AI-touched · 2 active agents
Bottom line
ADS improved (27.3%) in throughput. AI-vs-Human cycle comparison is directional only (sample too small) (n=1/41); the main AI bottleneck is Ready For Review.
Issues done
42
+27.3% vs prev
AI-owned share
2%
-3.7 pp vs prev
AI-touched
16 (38%)
Lead Time (active)
3.8d
prev 8.1d
Cycle Time (active)
2.9d
prev 4d
TTR p50
0.1h
prev 0.2h
PR Review p50
—
WIP active
30
WIP blocked
14
AI insight
Where AI helps
No statistically stable AI advantage detected yet. Directional-only signals below numerically favor AI but sample size is below threshold.
Where AI hurts
No AI-owned metric is currently slower than the human cohort.
Directional only (sample too small)
- · Cycle Time (active): AI 1.1d vs Human 3.8d (n=1/41, below 20)
- · Lead Time (active): AI 1.1d vs Human 3.9d (n=1/41, below 20)
- · Time to Ready p50: AI 0h vs Human 0.1h (n=1/41, below 20)
Top AI bottleneck
Active agents · top 2
AI Period: compare · intro 2025-08-01
3 completed · 33.3% AI-touched · 1 active agent
Bottom line
IMC improved (50.0%) in throughput. AI cohort too small for cycle comparison (n=0/2); no specific AI bottleneck stands out.
Issues done
3
+50.0% vs prev
AI-owned share
0%
+0.0 pp vs prev
AI-touched
1 (33%)
Lead Time (active)
69.1d
prev 23.4d
Cycle Time (active)
31.7d
TTR p50
0.5h
prev 15.5d
PR Review p50
—
WIP active
3
WIP blocked
0
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 1
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
92 completed · 2.2% AI-touched · 0 active agents
Bottom line
PU improved (91.7%) in throughput. AI cohort too small for cycle comparison (n=0/65); no specific AI bottleneck stands out.
Issues done
92
+91.7% vs prev
AI-owned share
0%
+0.0 pp vs prev
AI-touched
2 (2%)
Lead Time (active)
9.5d
prev 5.5d
Cycle Time (active)
1.5d
prev 4.7d
TTR p50
5.2d
prev 3.8d
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 | 92 | 9.5d | 1.5d | 127 | 32 | 0% |
| EVO | Platforms Evolution | 78 | 21.1d | 6.7d | 42 | 5 | 36% |
| ADS | Advertisement | 42 | 3.8d | 2.9d | 30 | 14 | 2% |
| TDOT | Tripledot | 21 | 24d | 4d | 28 | 18 | 0% |
| PDO | Platform Daily Operations | 18 | 28.7d | 7.9d | 15 | 6 | 6% |
| TAA | Test AI Agents | 12 | 3.1d | 1.3d | 2 | 0 | 75% |
| TECH | Tech Services | 9 | 5d | 4.9d | 61 | 11 | 0% |
| IMC | [Engineering]: Incident Management | 3 | 69.1d | 31.7d | 3 | 0 | 0% |