Automation index
How much of a Art conservator’s job can AI already do?
A photo and an analysis come earlier. The scalpel, and not loading the work, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Art conservator
21%
AI can already do 21% of this job.
#436 / 633
View rankingArt conservator
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 |
|---|---|---|---|
Survey, photos, analysis and the treatment proposal.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Cleaning, fill, inpainting and protection.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Reversibility, and not overworking the piece.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Report, dossier and the owner’s or museum’s yes.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Transport, climate and the piece’s setting.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Ethics and liability for what is touched.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
| Total | 100% | 21% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- conserve art
- clean paint
- inpainting
- fills
- treatment report
- reversibility
- analyse the work
- not overworking the piece
Sources for this job· O*NET 25-4013.00· ESCO/ISCO 2621
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