IS WORK OVER?

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

Tram 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.

TaskShareAIPoints
Checks, log and the cab.
Estimate

No public source provides a precise figure. This is our estimate.

16%32%+5.1
Drive, stops and the pedestrian who steps out.
Estimate

No public source provides a precise figure. This is our estimate.

24%8%+1.9
Badly parked car, calls and Control.
Estimate

No public source provides a precise figure. This is our estimate.

16%22%+3.5
Timetable, reliefs and rush hour.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
The tourist, the bike and not over-hooting.
Estimate

No public source provides a precise figure. This is our estimate.

14%18%+2.5
Hit nobody, and make the terminus.
Estimate

No public source provides a precise figure. This is our estimate.

14%12%+1.7
Total100%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

Last-mile manager

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.

TaskShareAIPoints
Zones, capacity and route cuts.
Estimate

No public source provides a precise figure. This is our estimate.

16%44%+7
KPIs, first attempt and incidents.
Estimate

No public source provides a precise figure. This is our estimate.

16%36%+5.8
The courier, the lobby and the one who will not open.
Estimate

No public source provides a precise figure. This is our estimate.

18%16%+2.9
Peaks, rain and hiring too many or too few.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
The angry customer and not dressing delivered.
Estimate

No public source provides a precise figure. This is our estimate.

18%18%+3.2
Cover the peak, and not leaving the neighbourhood orphaned.
Estimate

No public source provides a precise figure. This is our estimate.

16%14%+2.2
Total100%26%

Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low

Skills and activities

  • delivery zones
  • first attempt
  • delivery KPIs
  • last-mile peaks
  • lobbies
  • delivery incidents
  • courier capacity
  • cut routes

Sources for this job· O*NET 11-3071.00· ESCO/ISCO 1324

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