Automation index
How much of a Lathe operator’s job can AI already do?
A CNC run and a pass come earlier. Setting up, measuring and not scrapping the part do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Lathe operator
26%
AI can already do 26% of this job.
#214 / 623
View rankingLathe operator
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 drawing, tolerances and the route card.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Set up, clamp, pick the tool and the origin.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Turn, face, thread and finish the part.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 28% | +5 |
Measure, tweak offsets and not scrap the batch.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Lathe care, swarf and safety.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Grades, times and liability for the dimension.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 26% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- turn parts
- set up the lathe
- measure dimensions
- cut threads
- adjust offsets
- read mechanical drawings
- avoid scrap
- pick tools
Sources for this job· O*NET 51-4034.00· ESCO/ISCO 7223
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