Automation index
How much of a Choreographer’s job can AI already do?
A notation and a video come earlier. Making a body tell something does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Choreographer
18%
AI can already do 18% of this job.
#572 / 643
View rankingChoreographer
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 |
|---|---|---|---|
Notation, video and a first breakdown.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Rehearse, mark and adjust bodies.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Cast, ego and injuries.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Set, light and music.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Opening, tour and revival.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 16% | +2.6 |
Not breaking a dancer for an idea.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 14% | +2 |
| Total | 100% | 18% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Notation, video and a first breakdown
- Rehearse, mark and adjust bodies
- Cast, ego and injuries
- Set, light and music
- Opening, tour and revival
- Not breaking a dancer for an idea
- coreografo
- choreographer
Sources for this job· O*NET 27-2032.00· ESCO/ISCO 2653
IS WORK OVER?
ML engineer
29%
#64 / 643
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