IS WORK OVER?

Automation index

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

A colour formula comes earlier. A metallic that does not show does not.

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

Task comparison

Vehicle painter

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
Formulas, codes and paint orders.
Estimate

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

16%32%+5.1
Mask, spray and bake.
Estimate

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

22%10%+2.2
Sand, polish and match the gloss.
Estimate

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

16%14%+2.2
Booth, filters and waste.
Estimate

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

16%18%+2.9
Shop, deadlines and the panel beater.
Estimate

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

16%22%+3.5
Not leaving a patch you can see from ten metres.
Estimate

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

14%14%+2
Total100%18%

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

Skills and activities

  • Formulas, codes and paint orders
  • Mask, spray and bake
  • Sand, polish and match the gloss
  • Booth, filters and waste
  • Shop, deadlines and the panel beater
  • pintor de coches
  • vehicle painter

Sources for this job· O*NET 51-9124.00· ESCO/ISCO 7132

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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