Automation index
How much of a Football coach’s job can AI already do?
A lineup and a clip come earlier. Going out to compete without breaking do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Football coach
22%
AI can already do 22% of this job.
#332 / 633
View rankingFootball 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 |
|---|---|---|---|
Lineup, plan and the opponent clip.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Train, the one who is late and the one who hides.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Match, the changes and the one who boils.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Dressing room, the ego and not losing the group.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The board, the parents and not picking the son under pressure.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Compete, and not breaking.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- lineups
- opponent clip
- training
- changes
- dressing room
- parents
- pick the side
- match plan
Sources for this job· O*NET 27-2022.00· ESCO/ISCO 3422
IS WORK OVER?
ML engineer
29%
#64 / 633
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