Automation index
How much of a Scrum master’s job can AI already do?
A board and a retro template assemble. Unblocking a team and holding a director do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Scrum master
25%
AI can already do 25% of this job.
#251 / 623
View rankingScrum master
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 |
|---|---|---|---|
Facilitate ceremonies, boards and notes.EstimateNo public source provides a precise figure. This is our estimate. | 20% | 45% | +9 |
Flow metrics and impediment reports.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 38% | +6.1 |
Team coaching and internal conflict.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 15% | +2.7 |
Protect the sprint from interruptions.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Train PO and managers in the method.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Change the system, not just the ritual.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 25% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Facilitate ceremonies, boards and notes
- Flow metrics and impediment reports
- Team coaching and internal conflict
- Protect the sprint from interruptions
- Train PO and managers in the method
- Change the system, not just the ritual
- scrum master
- agile coach
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