IS WORK OVER?

Automation index

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

A current model comes earlier. Going to sea and actually reading the water do not.

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

Task comparison

Oceanographer

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
Model, buoys and the cruise.
Estimate

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

16%40%+6.4
Sea, sampling and the one who misses the cast.
Estimate

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

18%14%+2.5
Data, the gap and not interpolating extra.
Estimate

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

16%28%+4.5
Report, the coast and the one who will not hear.
Estimate

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

16%22%+3.5
The storm, the ship and aborting.
Estimate

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

18%18%+3.2
Read the water, and not trusting the model alone.
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

  • current models
  • buoys
  • ocean cruises
  • sampling
  • interpolation
  • the coast
  • the storm
  • casts

Sources for this job· O*NET 19-2042.00· ESCO/ISCO 2114

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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