IS WORK OVER?

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

Ticket 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.

TaskShareAIPoints
Validate, fares and the intervention note.
Estimate

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

16%40%+6.4
Aisle, the group and the one who hides.
Estimate

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

18%12%+2.2
Fines, data and what you can ask.
Estimate

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

16%28%+4.5
Incident, the full train and calling the conductor.
Estimate

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

16%22%+3.5
The insult, keeping calm and not making a scene.
Estimate

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

18%18%+3.2
Charge fairly, and not skipping a coach.
Estimate

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

16%12%+1.9
Total100%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

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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