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

Dosimetrist

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
CT, contours and the plan.
Estimate

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

16%40%+6.4
Organ at risk, the curve and not overshooting.
Estimate

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

18%14%+2.5
Physics, the check and the one that does not fit.
Estimate

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

16%28%+4.5
Fraction, the setup and the patient who is not the same.
Estimate

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

16%22%+3.5
The oncologist, the trade-off and not signing blind.
Estimate

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

18%18%+3.2
The tumour gets it, and the healthy organ does not.
Estimate

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

16%12%+1.9
Total100%22%

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

Skills and activities

  • dose plans
  • contours
  • organs at risk
  • dosimetry check
  • fractions
  • setup
  • sim CT
  • DVH curves

Sources for this job· O*NET 29-2036.00· ESCO/ISCO 3211

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