IS WORK OVER?

Automation index

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

A dock dashboard comes earlier. Getting the shift out without mixing the goods do not.

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

Task comparison

Warehouse supervisor

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
Docks, waves and the shift plan.
Estimate

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

16%40%+6.4
The team, the peak and the one who cannot keep up.
Estimate

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

18%14%+2.5
Stock, shrink and the mystery gap.
Estimate

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

16%32%+5.1
Safety, forklifts and the near miss.
Estimate

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

16%28%+4.5
The late haulier and not mixing the docks.
Estimate

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

18%18%+3.2
Get the shift out, and not mixing the goods.
Estimate

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

16%14%+2.2
Total100%24%

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

Skills and activities

  • plan docks
  • waves
  • warehouse shift
  • shrink
  • forklifts
  • near misses
  • outbound peaks
  • mixed goods

Sources for this job· O*NET 53-1048.00· ESCO/ISCO 1324

Data architect

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
Models, lineage and the catalogue.
Estimate

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

18%52%+9.4
Pipelines, quality and the data contract.
Estimate

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

16%40%+6.4
The analyst who finds nothing and the mess.
Estimate

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

16%22%+3.5
Privacy, retention and the field you cannot keep.
Estimate

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

16%28%+4.5
The business, shadow IT and not building another lake.
Estimate

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

18%14%+2.5
That it gets used, and not letting raw data rot.
Estimate

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

16%12%+1.9
Total100%28%

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

Skills and activities

  • model data
  • lineage
  • data catalogue
  • data contracts
  • pipeline quality
  • retention
  • data lakes
  • privacy

Sources for this job· O*NET 15-1243.00· ESCO/ISCO 2511

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