Automation index
How much of a Ticket inspector’s job can AI already do?
A validator comes earlier. Walking the train and dealing with no ticket do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Ticket inspector
22%
AI can already do 22% of this job.
#378 / 633
View rankingTicket inspector
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 |
|---|---|---|---|
Validate, fares and the intervention note.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Aisle, the group and the one who hides.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 12% | +2.2 |
Fines, data and what you can ask.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Incident, the full train and calling the conductor.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The insult, keeping calm and not making a scene.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Charge fairly, and not skipping a coach.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
- validate tickets
- rail fares
- interventions
- on-train fines
- walk coaches
- call the conductor
- passenger data
- full trains
Sources for this job· O*NET 53-4031.00· ESCO/ISCO 5112
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