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€” IMC

Window: 30d Β· All 3 Β· AI 0 Β· Human 3

Jira Flow Lead Time (raw / wall-clock)
97.1dn=3AIno dataH97.1dn=3 Β· directional
Lead Time (active, Γ· pauses)
69.1dn=3AIno dataH69.1dn=3 Β· directional
Time to Ready
0.5hn=3AIno dataH0.5hn=3 Β· directional
Cycle Time (raw / wall-clock)
44.7dn=2AIno dataH44.7dn=2 Β· directional
Lead Time for Changes
Not enough data in this window
Cycle Time (active, Γ· pauses)
31.7dn=2AIno dataH31.7dn=2 Β· directional
Time to First PR
Not enough data in this window
PR Review Duration
Not enough data in this window
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 tickets / PRs measurable in this window
  • Β·PR Review Duration β€” no tickets / PRs measurable in this window
  • Β·Lead Time for Changes β€” no tickets / PRs measurable in this window

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)97.1d3β€”0 ⚠97.1d3 βš β€”
Lead Time (active, Γ· pauses)69.1d3β€”0 ⚠69.1d3 βš β€”
Time to Ready0.5h3β€”0 ⚠0.5h3 βš β€”
Cycle Time (raw / wall-clock)44.7d2β€”0 ⚠44.7d2 βš β€”
Cycle Time (active, Γ· pauses)31.7d2β€”0 ⚠31.7d2 βš β€”
Time to First PRβ€”0β€”0 βš β€”0 βš β€”
PR Review Durationβ€”0β€”0 βš β€”0 βš β€”
Lead Time for Changesβ€”0β€”0 βš β€”0 βš β€”

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
0.7
AI 0.0 Β· H 0.7
WIP β€” Active now
3
Blocked 0
Bug rate / week
1.4
AI participation
0%
AI 0 / 3
Top bottleneck
Triaged
median 38.4d
Deploys / week
β€”
no ADO mapping
Change failure rate
β€”
no ADO mapping
PR acceptance
β€”
no PRs in window

🧠 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.