Automation index
How much of a Tool and die maker’s job can AI already do?
A CAD file and an electrode come earlier. Fitting the die so the run comes out do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Tool and die maker
25%
AI can already do 25% of this job.
#244 / 623
View rankingTool and die maker
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 |
|---|---|---|---|
Read the die, clearances and the 3D drawing.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Machine, fit and try the tooling.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Mount in the press, shoot and correct.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 22% | +4 |
EDM, polish and cavity finish.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Die maintenance and changeovers.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Die life and liability for the part.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 25% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- make dies
- fit tooling
- run EDM
- try out tooling
- polish cavities
- mount in the press
- maintain moulds
- correct clearances
Sources for this job· O*NET 51-4111.00· ESCO/ISCO 7222
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