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 — ADS

Window: 30d · All 52 · AI 0 · Human 52

Jira Flow Lead Time (raw / wall-clock)
112hn=52AIno dataH112hn=52
Lead Time (active, ÷ pauses)
112hn=52AIno dataH112hn=52
Time to Ready
93hn=52AIno dataH93hn=52
Cycle Time (raw / wall-clock)
0.0hn=52AIno dataH0.0hn=52
Lead Time for Changes
3.5hn=29AIno dataH3.5hn=29
Cycle Time (active, ÷ pauses)
0.0hn=52AIno dataH0.0hn=52
Time to First PR
0.6hn=13AIno dataH0.6hn=13
PR Review Duration
3.1hn=7AIno dataH3.1hn=7 · directional
Created
Ready
In Progress
In Review
Done
Deployed

AI vs Human comparison

Bars proportional within each row (max = 100%). Δ% lower-is-better — 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):

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

Cohort breakdown

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

MetricAll p50nAI p50nHuman p50nΔ (AI vs Human)
Jira Flow Lead Time (raw / wall-clock)112h52—0 ⚠112h52—
Lead Time (active, ÷ pauses)112h52—0 ⚠112h52—
Time to Ready93h52—0 ⚠93h52—
Cycle Time (raw / wall-clock)0.0h52—0 ⚠0.0h52—
Cycle Time (active, ÷ pauses)0.0h52—0 ⚠0.0h52—
Time to First PR0.6h13—0 ⚠0.6h13—
PR Review Duration3.1h7—0 ⚠3.1h7 ⚠—
Lead Time for Changes3.5h29—0 ⚠3.5h29—

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). Δ compares AI vs Human p50 — negative = AI faster.

Side metrics

Throughput / week
12.1
AI 0.0 · H 12.1
WIP — Active now
26
Blocked 17
Bug rate / week
0.9
AI participation
0%
AI 0 / 52
Top bottleneck
On Hold
median 20h
Deploys / week
4.9
ADO-style
Change failure rate
13%
ADO-style
PR acceptance
79%
AI 0% · H 86%

🧠 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 — the value is still shown for context but trust it less.

19 tickets excluded — managed centrally.