Automation index
How much of a Parking attendant’s job can AI already do?
A barrier comes earlier. Running the car park without wrecking the person who lost the ticket do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Parking attendant
24%
AI can already do 24% of this job.
#260 / 633
View rankingParking attendant
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 |
|---|---|---|---|
Barriers, tickets and the count.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
The one who cannot find the car and the one who will not pay.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Incidents, the down barrier and the jam.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Shift, cash and cashing up.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
The event, the full house and not blocking the street.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Run the car park, and not wrecking the lost-ticket person.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 24% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- barriers
- parking tickets
- space count
- cashing up
- stuck barrier
- events
- lost cars
- exit jam
Sources for this job· O*NET 33-9091.00· ESCO/ISCO 5414
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