IS WORK OVER?

Automation index

How much of a Marine engineer’s job can AI already do?

An engine log comes earlier. The engine lasting the crossing do not.

This percentage does not predict how many people will lose their jobs.

Task comparison

Marine 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.

TaskShareAIPoints
Rounds, parameters and the engine log.
Estimate

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

16%36%+5.8
Breakdown, the sea and repairing with what you have.
Estimate

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

18%12%+2.2
Fuel, oils and the port change.
Estimate

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

16%28%+4.5
Watch, alarms and the one who does not sleep.
Estimate

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

16%22%+3.5
Survey, class and not dressing the log.
Estimate

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

18%18%+3.2
Last the crossing, and not silencing the alarm.
Estimate

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

16%12%+1.9
Total100%21%

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

Skills and activities

  • engine rounds
  • engine log
  • breakdowns at sea
  • watches
  • class
  • fuels
  • engine-room alarms
  • repair on passage

Sources for this job· O*NET 53-5031.00· ESCO/ISCO 3151

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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