IS WORK OVER?

Automation index

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

A course comes earlier. Bringing people and boat whole into harbour do not.

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

Task comparison

Skipper

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
Course, log and the day’s weather.
Estimate

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

16%36%+5.8
Helm, the sea and the harbour manoeuvre.
Estimate

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

22%8%+1.8
Crew, the novice and the one who is sick.
Estimate

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

16%22%+3.5
Breakdown, sail or engine and calling it in time.
Estimate

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

16%28%+4.5
The client, the charter and not pushing it.
Estimate

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

16%18%+2.9
Whole into harbour, and not trusting the plotter alone.
Estimate

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

14%10%+1.4
Total100%20%

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

Skills and activities

  • courses
  • helm
  • harbour manoeuvre
  • weather
  • charter
  • novices aboard
  • breakdowns at sea
  • the plotter

Sources for this job· O*NET 53-5021.00· ESCO/ISCO 3152

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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