IS WORK OVER?

Automation index

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

A measured opening comes earlier. Closing, insulating and not trapping the lintel do not.

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

Task comparison

Window installer

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
Measure openings, subframe and the order.
Estimate

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

16%32%+5.1
Set, plumb and the missing packer.
Estimate

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

22%8%+1.8
Foam, seal and the sweating lintel.
Estimate

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

16%22%+3.5
Fittings, adjust and the sash that rubs.
Estimate

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

16%28%+4.5
The lived-in house and not leaving the room freezing.
Estimate

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

16%18%+2.9
That it closes, and not trapping the lintel.
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

  • measure openings
  • subframes
  • plumb windows
  • seal lintels
  • adjust sashes
  • foam
  • packers
  • window fittings

Sources for this job· O*NET 47-2121.00· ESCO/ISCO 7125

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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