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?
ML engineer
29%
#64 / 633
View rankingML 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 |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511