IS WORK OVER?

Automation index

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

A stain card comes earlier. Lifting the stain without eating the colour do not.

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

Task comparison

Dry cleaner

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
Card, fibre and pretreatment.
Estimate

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

16%40%+6.4
Machine, solvent and the delicate garment.
Estimate

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

18%14%+2.5
Press, the finish and the one that marks.
Estimate

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

16%28%+4.5
Job, the groom and the one who wants it now.
Estimate

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

16%22%+3.5
The old stain and not promising miracles.
Estimate

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

18%18%+3.2
Lift the stain, and not eating the colour.
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

  • pretreat stains
  • solvents
  • press
  • delicates
  • job cards
  • finish
  • fibres
  • old stains

Sources for this job· O*NET 51-6011.00· ESCO/ISCO 9121

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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