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?
Prompt engineer
34%
#21 / 623
View rankingPrompt 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 |
|---|---|---|---|
Design prompts, tools and chains.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 58% | +13.9 |
Evals, datasets and regressions.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Integrate with product and safety limits.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to the business about a good output.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Watch hallucinations, bias and cost.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 20% | +2.8 |
Decide what the model gets, and what it does not.EstimateNo public source provides a precise figure. This is our estimate. | 12% | 16% | +1.9 |
| Total | 100% | 34% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Design prompts, tools and chains
- Evals, datasets and regressions
- Integrate with product and safety limits
- Talk to the business about a good output
- Watch hallucinations, bias and cost
- prompt engineer
- ingeniero de prompts
- llm engineer
Sources for this job· O*NET 15-1252.00· ESCO/ISCO 2512