Automation index
How much of a Bus driver’s job can AI already do?
The timetable generates itself. Carrying 50 people, an amber light, and a child at the crossing do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Bus driver
15%
AI can already do 15% of this job.
#630 / 643
View rankingBus driver
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 |
|---|---|---|---|
Run sheet, logs and clocking.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Drive the route or the charter.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Stops, accessibility and fare or validation.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 10% | +1.8 |
Incidents, diversions and passenger care.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Pre-trip check and defect reports.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
Shifts, fatigue and driving rules.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 15% | +2.1 |
| Total | 100% | 15% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- drive
- route
- load goods
- Run sheet, logs and clocking
- Drive the route or the charter
- Incidents, diversions and passenger care
- Pre-trip check and defect reports
- Shifts, fatigue and driving rules
- conductor de autobus
- bus driver
Sources for this job· O*NET 53-3052.00· ESCO/ISCO 8331
IS WORK OVER?
ML engineer
29%
#64 / 643
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