Automation index
How much of a Ice-cream maker’s job can AI already do?
A base recipe comes earlier. Creamy in the mouth without turning to ice do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Ice-cream maker
22%
AI can already do 22% of this job.
#338 / 633
View rankingIce-cream 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 |
|---|---|---|---|
Base, overrun and the recipe.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 44% | +7 |
Churn, age and the one that crystals.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 12% | +2.2 |
List, season and the flavour that misses.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Workshop, cold and the broken chain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Summer, the queue and not running out of vanilla.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Creamy in the mouth, and not selling ice.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- churn
- overrun
- age bases
- ice-cream list
- cold chain
- ice crystals
- vanilla
- ice-cream season
Sources for this job· O*NET 51-3099.00· ESCO/ISCO 7512
IS WORK OVER?
Prompt engineer
34%
#21 / 633
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