Automation index
How much of a Parts clerk’s job can AI already do?
An OEM code comes earlier. Giving the part that fits, not the lookalike, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Parts clerk
28%
AI can already do 28% of this job.
#114 / 623
View rankingParts clerk
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 |
|---|---|---|---|
Catalogue, equivalents and the code.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Counter, the waiting shop and the doubt.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Stock, urgent orders and the one that is late.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Returns, warranties and the wrong part.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The mechanic, the blurry photo and not selling blind.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
The one that fits, not the lookalike.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- parts catalogue
- OEM equivalents
- shop counter
- urgent orders
- returns
- part warranties
- parts stock
- part codes
Sources for this job· O*NET 41-2022.00· ESCO/ISCO 4321
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