Automation index
How much of a Tram driver’s job can AI already do?
Urban ATO comes earlier. Crossing pedestrians without clipping the badly parked car do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Tram driver
19%
AI can already do 19% of this job.
#496 / 643
View rankingTram 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 |
|---|---|---|---|
Checks, log and the cab.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Drive, stops and the pedestrian who steps out.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 8% | +1.9 |
Badly parked car, calls and Control.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Timetable, reliefs and rush hour.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The tourist, the bike and not over-hooting.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
Hit nobody, and make the terminus.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
- drive trams
- urban stops
- pedestrians
- badly parked cars
- Control
- rush hour
- cab checks
- terminus
Sources for this job· O*NET 53-4041.00· ESCO/ISCO 8311
IS WORK OVER?
Development cooperation officer
26%
#217 / 643
View rankingDevelopment cooperation officer
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 |
|---|---|---|---|
Agreements, alignment and the SDGs on the card.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 48% | +7.7 |
Follow-up, missions and reports to the agency.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
The local partner, the counterpart and the disagreement.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 16% | +2.9 |
Budget, currency and what stays in management.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The context that shifts and reworking the approach.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Not imposing, and accounting on both sides.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 26% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- cooperation agreements
- align with SDGs
- agency reports
- southern partners
- monitoring missions
- hard-currency budget
- counterparts
- accountability
Sources for this job· O*NET 19-3094.00· ESCO/ISCO 2635