Automation index
How much of a Valet’s job can AI already do?
A key tag comes earlier. Not marking the car and having it ready when they come out do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Valet
19%
AI can already do 19% of this job.
#556 / 643
View rankingValet
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 |
|---|---|---|---|
Tag, keys and the bay map.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Drive someone else’s, the tight car park and not marking it.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
The client already out and the car still downstairs.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Insurance, the knock and who pays.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The event, the rain and not losing a key.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
No mark, and the car on time.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
- keep keys
- park others’ cars
- bay map
- not mark
- valet events
- knock insurance
- tight car park
- return on time
Sources for this job· O*NET 53-6021.00· ESCO/ISCO 8322
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