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 / 623
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?
ML engineer
29%
#64 / 623
View rankingML engineer
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 |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511