IS WORK OVER?

Automation index

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

A roster and a checklist come earlier. A hundred rooms out on time does not.

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

Task comparison

Executive housekeeper

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
Roster, checklists and linen logs.
Estimate

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

18%40%+7.2
Rounds, standards and VIP rooms.
Estimate

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

18%14%+2.5
Room team, shifts and conflict.
Estimate

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

16%22%+3.5
Front desk, maintenance and complaints.
Estimate

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

16%18%+2.9
Inventory, minibar and lost-and-found.
Estimate

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

16%16%+2.6
Not handing a dirty room to a guest.
Estimate

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

16%14%+2.2
Total100%21%

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

Skills and activities

  • Roster, checklists and linen logs
  • Rounds, standards and VIP rooms
  • Room team, shifts and conflict
  • Front desk, maintenance and complaints
  • Inventory, minibar and lost-and-found
  • Not handing a dirty room to a guest
  • gobernanta
  • ama de llaves
  • executive housekeeper

Sources for this job· O*NET 37-1011.00· ESCO/ISCO 5151

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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