Automation index
How much of a Workshop teacher’s job can AI already do?
A practical sheet comes earlier. Making the piece without cutting themselves do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Workshop teacher
19%
AI can already do 19% of this job.
#519 / 643
View rankingWorkshop teacher
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 |
|---|---|---|---|
Sheet, material and the station.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Shop, the machine and the one without glasses.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
The piece, the error and doing it again.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Assess, the portfolio and not giving the pass away.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The firm, the placement and the one who does not fit.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
The piece made, and no cuts.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
- practical sheets
- shop machines
- glasses
- portfolio
- firm placements
- shop stations
- shop safety
- pieces
Sources for this job· O*NET 25-2032.00· ESCO/ISCO 2320
IS WORK OVER?
Prompt engineer
34%
#21 / 643
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