IS WORK OVER?

Automation index

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

A shopping list writes itself. Someone else’s house, a trust, and a back do not.

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

Task comparison

Domestic worker

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
Lists, online shopping and written errands.
Estimate

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

14%30%+4.2
Menus and pantry organisation.
Estimate

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

12%22%+2.6
Clean, cook, iron and tidy.
Estimate

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

26%8%+2.1
Care for elders or children of the house.
Estimate

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

16%10%+1.6
Trust, keys and boundaries.
Estimate

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

16%12%+1.9
Relationship with the employing family.
Estimate

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

16%14%+2.2
Total100%15%

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

Skills and activities

  • Lists, online shopping and written errands
  • Menus and pantry organisation
  • Clean, cook, iron and tidy
  • Care for elders or children of the house
  • Trust, keys and boundaries
  • Relationship with the employing family
  • empleada de hogar
  • interno
  • domestic worker
  • trabajadora del hogar

Sources for this job· O*NET 39-9011.00· ESCO/ISCO 9111

Station master

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
Graph, orders and the station log.
Estimate

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

16%40%+6.4
Platforms, the crossing and the late train.
Estimate

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

18%14%+2.5
Staff, shifts and the ticket-office gap.
Estimate

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

16%32%+5.1
Incident, passengers and the PA.
Estimate

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

16%22%+3.5
Works, closures and leaving nobody on the platform.
Estimate

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

18%18%+3.2
That they do not meet, and signing the graph.
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

  • train graph
  • orders
  • platforms
  • ticket office
  • PA
  • line crossings
  • station shifts
  • station logs

Sources for this job· O*NET 53-4031.00· ESCO/ISCO 4323

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