IS WORK OVER?

Automation index

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

An oxygen reading and a ration come earlier. The batch not dying in the night do not.

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

Task comparison

Aquaculturist

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
Oxygen, ration and the mortality log.
Estimate

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

16%40%+6.4
Cage, net and the escape.
Estimate

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

18%12%+2.2
Water quality, exchange and the bloom.
Estimate

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

16%28%+4.5
Vaccines, the vet and the batch that turns.
Estimate

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

16%22%+3.5
The storm, the swell and not losing the cage.
Estimate

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

18%18%+3.2
Not dying in the night, and not pushing density.
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

  • cage oxygen
  • fish ration
  • nets
  • water quality
  • aquaculture mortality
  • batch vaccines
  • swell
  • stocking density

Sources for this job· O*NET 45-1011.00· ESCO/ISCO 6221

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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