IS WORK OVER?

Automation index

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

A woodworm diagnosis comes earlier. Treating without overworking the piece do not.

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

Task comparison

Furniture restorer

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
Diagnosis, woodworm and the proposal.
Estimate

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

16%32%+5.1
Consolidate, glue and the one that opens.
Estimate

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

22%8%+1.8
Finish, shellac and the colour.
Estimate

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

16%22%+3.5
Hardware, upholstery and what is not touched.
Estimate

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

16%28%+4.5
The owner, the memory and not leaving it too new.
Estimate

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

16%18%+2.9
Not overworking the piece, and signing the treatment.
Estimate

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

14%12%+1.7
Total100%19%

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

Skills and activities

  • woodworm
  • consolidate wood
  • shellac
  • hardware
  • upholstery
  • furniture diagnosis
  • glue
  • not overwork

Sources for this job· O*NET 51-7021.00· ESCO/ISCO 7522

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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