Automation index
How much of a Cobbler’s job can AI already do?
A sole quote comes earlier. Stitching, sticking and not coming off on day three do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Cobbler
19%
AI can already do 19% of this job.
#559 / 643
View rankingCobbler
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 |
|---|---|---|---|
Diagnose, sole and the quote.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Stitch, stick and the heel that goes.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Last, the fit and the one that pinches.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Colour, cream and the pair that does not match.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The client, the dance and not cutting the vamp.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
That it does not come off, and not leaving a lame heel.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
| Total | 100% | 19% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- stitch soles
- stick heels
- lasts
- creams
- vamp
- shoe fit
- pairs
- cobbler quotes
Sources for this job· O*NET 51-6041.00· ESCO/ISCO 7536
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