Automation index
How much of a Agile coach’s job can AI already do?
A board and a stock retro come earlier. The team stopping the fake sprint do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Agile coach
24%
AI can already do 24% of this job.
#257 / 633
View rankingAgile coach
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Boards, metrics and the cadence design.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
The real retro and the elephant in the room.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Train, unblock and the manager who will not let go.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Scale, dependencies and fake agile.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Conflict, power and not selling extra frameworks.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
That they stop faking it, and not inflating velocity.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 24% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- retros
- agile cadence
- unblock teams
- flow metrics
- fake agile
- agile training
- dependencies
- velocity
Sources for this job· O*NET 13-1111.00· ESCO/ISCO 2421
IS WORK OVER?
Last-mile manager
26%
#211 / 633
View rankingLast-mile manager
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Zones, capacity and route cuts.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 44% | +7 |
KPIs, first attempt and incidents.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
The courier, the lobby and the one who will not open.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 16% | +2.9 |
Peaks, rain and hiring too many or too few.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The angry customer and not dressing delivered.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Cover the peak, and not leaving the neighbourhood orphaned.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 26% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- delivery zones
- first attempt
- delivery KPIs
- last-mile peaks
- lobbies
- delivery incidents
- courier capacity
- cut routes
Sources for this job· O*NET 11-3071.00· ESCO/ISCO 1324