User Story Lifecycle View
End-to-end change management tracking — intake, delivery stages, defects, effort, AI productivity, and TAT
Full Delivery Chain
User Story Volume
Stories Received
148
Total intake this period
Stories Accepted
132
89.2% acceptance rate
CGB Planned
98
74.2% of accepted
Adhoc Stories
34
25.8% of accepted
Dropped
9
6.1% drop rate
Rejected
16
10.8% rejection rate
On Hold
7
5.3% hold rate
Calculated KPIs
Story Acceptance Rate
89.2%
132 / 148 received
CGB Coverage Rate
74.2%
98 / 132 accepted
Live Conversion Rate
61.4%
81 / 132 accepted
CUG Coverage
72.8%
59 / 81 live stories
Defect Rate
18.5%
24 defects / 130 tested
User Story Start Map — When Dev Began vs Actual Live Month
🔗 How this links to the Delivery Map above: Bubble color = same scale (green = delivered same/faster month, blue = 1 month, amber = 2M, orange = 3M, red = 4M+). Row axis differs: Delivery Map rows = Target Date month · This map rows = Dev Start Month. Use both together: a user story planned in Jan (Delivery Map) but Dev started in Mar (this map) = 2 months of pre-dev delay.
Dev Start Month When first dev task planned | User Stories Started | ✓ Live Month (Actual Delivery Month) | Totals | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| JAN-26 | FEB-26 | MAR-26 | APR-26 | MAY-26 | JUN-26 | JUL-26 | AUG-26 | SEP-26 | OCT-26 | NOV-26 | DEC-26 | DELIVERED | NOT YET | ||
| Jan-26 | 109 | 15 | 32 | 25 | 3 | 9 | – | – | – | – | – | – | – | 84 | 25 |
| Feb-26 | 84 | – | 19 | 37 | 5 | 5 | – | – | – | – | – | – | – | 66 | 18 |
| Mar-26 | 74 | – | – | 20 | 26 | 3 | – | – | – | – | – | – | – | 49 | 25 |
| Apr-26 | 70 | – | – | – | 14 | 11 | – | – | – | – | – | – | – | 25 | 45 |
| May-26 | 32 | – | – | – | – | – | – | – | – | – | – | – | – | 0 | 32 |
| Jun-26 | – | – | – | – | – | – | – | – | – | – | – | – | – | 0 | 0 |
| Jul-26 | – | – | – | – | – | – | – | – | – | – | – | – | – | 0 | 0 |
| Aug-26 | – | – | – | – | – | – | – | – | – | – | – | – | – | 0 | 0 |
| Sep-26 | – | – | – | – | – | – | – | – | – | – | – | – | – | 0 | 0 |
| Oct-26 | – | – | – | – | – | – | – | – | – | – | – | – | – | 0 | 0 |
| Nov-26 | – | – | – | – | – | – | – | – | – | – | – | – | – | 0 | 0 |
| Dec-26 | – | – | – | – | – | – | – | – | – | – | – | – | – | 0 | 0 |
| Column Totals | 15 | 51 | 82 | 48 | 28 | – | – | – | – | – | – | – | 224 | 145 | |
Delivery Map — Planned vs Actual Live Month
Planned Month | No. of User Stories | Production Deployment Month (Actual Live Date) | Totals Delivered | Not Yet | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| JAN-26 | FEB-26 | MAR-26 | APR-26 | MAY-26 | JUN-26 | JUL-26 | AUG-26 | SEP-26 | OCT-26 | NOV-26 | DEC-26 | DELIVERED | NOT YET | ||
| Jan-26 | 70 | 31 | 25 | 10 | 66 | 4 | |||||||||
| Feb-26 | 103 | 6 | 54 | 40 | 1 | 1 | 102 | 1 | |||||||
| Mar-26 | 129 | 7 | 84 | 21 | 3 | 115 | 14 | ||||||||
| Apr-26 | 102 | 10 | 39 | 19 | 68 | 34 | |||||||||
| May-26 | 83 | 2 | 6 | 8 | 75 | ||||||||||
| Jun-26 | 48 | 0 | 48 | ||||||||||||
| Jul-26 | – | 0 | 0 | ||||||||||||
| Aug-26 | 2 | 2 | 2 | 0 | |||||||||||
| Sep-26 | – | 0 | 0 | ||||||||||||
| Oct-26 | 10 | 2 | 2 | 8 | |||||||||||
| Nov-26 | 1 | 1 | 1 | 0 | |||||||||||
| Dec-26 | 6 | 2 | 2 | 4 | 2 | ||||||||||
| Column Totals | 40 | 88 | 146 | 63 | 29 | – | – | 2 | – | – | – | – | 368 | 186 | |
· 188 user stories have no Live Date (not yet delivered) · 139 user stories have no Target Date (excluded from map)
| Month | Total Capacity | Carry Forward | New Requirements | Delivered | Spill Over | Utilized Efficiency |
|---|---|---|---|---|---|---|
| Jan'26 | 1400 | 180 | 1300 | 1450 | 30 | 108% |
| Feb'26 | 1380 | 160 | 1250 | 1370 | 40 | 99% |
| Mar'26 | 1430 | 140 | 1320 | 1410 | 50 | 99% |
| Apr'26 | 1410 | 155 | 1280 | 1390 | 45 | 99% |
| May'26 | 1450 | 170 | 1310 | 1420 | 60 | 98% |
| Jun'26 | ||||||
| Jul'26 | ||||||
| Aug'26 | ||||||
| Sep'26 | ||||||
| Oct'26 | ||||||
| Nov'26 | ||||||
| Dec'26 |
User Story Funnel
Received → Accepted → CGB Planned → Live → CUG
Stage-wise Delivery Pipeline
Stories count at each delivery stage
Planned vs Adhoc Story Trend
CGB planned vs adhoc stories — monthly rolling
Accepted vs Rejected vs Dropped Trend
Monthly intake quality trend
Defect Metrics
QA Reported Issues
18
Total defects in UAT phase
Biz Reported Issues
7
Defects in Biz UAT
N2P Reported Issues
3
Defects in N2P phase
Effort Metrics
Estimated Effort
186 MD
Total estimated dev effort
Effort (No AI)
256 MD
Actual without AI assistance
Effort (With AI)
138 MD
Actual using AI/Copilot
AI Effort Saving
118 MD
256 − 138 MD saved
AI Productivity Gain
46.1%
118 / 256 effort saved
Delivery Waste Metrics
Delivered & Dropped
4
Developed but not released
Delivered & Hold
6
Completed but blocked
Adhoc Story %
25.8%
34 / 132 accepted
Rejected Story %
10.8%
16 / 148 received
Defects by Stage
QA, Biz UAT, and N2P reported defects — monthly trend
Effort Variance Chart
Estimated vs actual (no AI) vs actual (with AI) — per story
AI Productivity Benefit Chart
Monthly effort saving and AI productivity gain % trend
Live Conversion Trend
Accepted vs live stories and conversion rate — monthly
TAT by Stage
Average turnaround time vs target per delivery stage (days)
TAT Radar — Stage Coverage
Actual vs target TAT across all stages
Average TAT Summary
Stage-wise TAT definition, average, target, and variance
| Stage | Avg TAT (days) | Target (days) | Variance |
|---|---|---|---|
| Solution Done | 3.2 | 3 | +0.2 |
| Development | 8.5 | 8 | +0.5 |
| UAT | 4.1 | 4 | +0.1 |
| Biz UAT | 3.8 | 3 | +0.8 |
| Infosec | 5.2 | 4 | +1.2 |
| N2P | 2.9 | 3 | -0.1 |
| RFP | 2.1 | 2 | +0.1 |
| Live | 1.8 | 2 | -0.2 |