Jira Metrics

Anatomy of a ticket

Range:
Validation & drill-down view. Use this page to understand exactly how each flow metric is computed for one project, and where the AI cohort differs from the human cohort. Headline numbers on /flow and /overview roll up the same formulas across all projects. Hover any lane label for the formula and a sample-size note.

Anatomy of a ticket β€” TDOT

Window: 30d Β· All 21 Β· AI 0 Β· Human 21

Jira Flow Lead Time (raw / wall-clock)
34.0dn=21AIno dataH34.0dn=21
Lead Time (active, Γ· pauses)
24.0dn=21AIno dataH24.0dn=21
Time to Ready
19.9dn=21AIno dataH19.9dn=21
Cycle Time (raw / wall-clock)
11.0dn=21AIno dataH11.0dn=21
Lead Time for Changes
76hn=12AIno dataH76hn=12
Cycle Time (active, Γ· pauses)
97hn=21AIno dataH97hn=21
Time to First PR
5.1hn=8AIno dataH5.1hn=8 Β· directional
PR Review Duration
130hn=6AIno dataH130hn=6 Β· directional
Created
Ready
In Progress
In Review
Done
Deployed

AI vs Human comparison

Bars proportional within each row (max = 100%). Ξ”% lower-is-better β€” green = AI faster.

No lane has both AI and Human samples in this window. Try a longer range, or check that this project has AI-assigned tickets.

Not measurable (one cohort has zero samples):

  • Β·Jira Flow Lead Time (raw / wall-clock) β€” no AI cohort samples (no completed tickets with an AI assignee, or no PRs by AI authors)
  • Β·Lead Time (active, Γ· pauses) β€” no AI cohort samples (no completed tickets with an AI assignee, or no PRs by AI authors)
  • Β·Time to Ready β€” no AI cohort samples (no completed tickets with an AI assignee, or no PRs by AI authors)
  • Β·Cycle Time (raw / wall-clock) β€” no AI cohort samples (no completed tickets with an AI assignee, or no PRs by AI authors)
  • Β·Cycle Time (active, Γ· pauses) β€” no AI cohort samples (no completed tickets with an AI assignee, or no PRs by AI authors)
  • Β·Time to First PR β€” no AI cohort samples (no completed tickets with an AI assignee, or no PRs by AI authors)
  • Β·PR Review Duration β€” no AI cohort samples (no completed tickets with an AI assignee, or no PRs by AI authors)
  • Β·Lead Time for Changes β€” no AI cohort samples (no completed tickets with an AI assignee, or no PRs by AI authors)

Cohort breakdown

⚠ marker when AI or Human cohort sample size is below 10.

MetricAll p50nAI p50nHuman p50nΞ” (AI vs Human)
Jira Flow Lead Time (raw / wall-clock)34.0d21β€”0 ⚠34.0d21β€”
Lead Time (active, Γ· pauses)24.0d21β€”0 ⚠24.0d21β€”
Time to Ready19.9d21β€”0 ⚠19.9d21β€”
Cycle Time (raw / wall-clock)11.0d21β€”0 ⚠11.0d21β€”
Cycle Time (active, Γ· pauses)97h21β€”0 ⚠97h21β€”
Time to First PR5.1h8β€”0 ⚠5.1h8 βš β€”
PR Review Duration130h6β€”0 ⚠130h6 βš β€”
Lead Time for Changes76h12β€”0 ⚠76h12β€”

All = every measurable ticket / PR in the window. AI = agent-owned cohort (ticket assignee or PR author is an active agent at completion). H = human cohort (everyone else, including unassigned). Ξ” compares AI vs Human p50 β€” negative = AI faster.

Side metrics

Throughput / week
4.9
AI 0.0 Β· H 4.9
WIP β€” Active now
28
Blocked 18
Bug rate / week
0.7
AI participation
0%
AI 0 / 21
Top bottleneck
Failed On Testing
median 22h
Deploys / week
0.5
ADO-style
Change failure rate
0%
ADO-style
PR acceptance
93%
AI β€” Β· H 93%

🧠 AI deep analysis

Generate an LLM-driven explanation of WHY the AI cohort is faster / slower on each lane. Cites specific tickets from the evidence pack, suggests next steps.

Active variants use business hours minus pause statuses (same formula as the per-project Cycle / Lead pages). Raw variants are pure wall-clock. AI cohort = ticket assignee (or PR author) is an active agent at completion time. Sample-size suppression marks values directional only when AI or Human cohort drops below 10 β€” the value is still shown for context but trust it less.