IS WORK OVER?

Automation index

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

A fall set-out comes earlier. Leaving a pavement that does not pond do not.

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

Task comparison

Paver

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
Set-out, levels and layer thickness.
Estimate

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

16%32%+5.1
Lay, compact and the edge that opens.
Estimate

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

22%8%+1.8
Joints, seals and the gully junction.
Estimate

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

16%22%+3.5
Plant, mix temperature and the cold day.
Estimate

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

16%28%+4.5
Traffic, the closure and not leaving a step.
Estimate

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

16%18%+2.9
No ponding, and not skimping compaction.
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

  • lay asphalt
  • compact
  • falls
  • pavement joints
  • gullies
  • mix temperature
  • pavement levels
  • seals

Sources for this job· O*NET 47-2071.00· ESCO/ISCO 7112

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

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