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

Home-automation 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
Diagram, buses and scene programming.
Estimate

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

16%40%+6.4
Wire, sensors and the switch that does not answer.
Estimate

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

18%14%+2.5
Integrate lights, climate and the client app.
Estimate

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

16%32%+5.1
Train, guest mode and the one who breaks it.
Estimate

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

16%22%+3.5
The lived-in house and not leaving it dark.
Estimate

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

18%18%+3.2
That it obeys, and not needing the cloud for a switch.
Estimate

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

16%12%+1.9
Total100%23%

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

Skills and activities

  • program scenes
  • home buses
  • integrate climate
  • sensors
  • home app
  • switches
  • guest mode
  • wire KNX

Sources for this job· O*NET 47-2111.00· ESCO/ISCO 7411

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