Automation index
How much of a Sporting director’s job can AI already do?
A squad map comes earlier. Signing without breaking the dressing room do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Sporting director
27%
AI can already do 27% of this job.
#149 / 623
View rankingSporting director
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 |
|---|---|---|---|
Squad, wages and financial fair play.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Signings, scouting and the one who does not fit.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Coach, the project and the one who burns.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Board, the fans and not selling smoke.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The injury, the window and not papering over.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Sign well, and not breaking the dressing room.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- squad
- signings
- fair play
- scouting
- wages
- the project
- dressing room
- the window
Sources for this job· O*NET 27-2022.00· ESCO/ISCO 3422
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