IS WORK OVER?

Automation index

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

A booking and a map come earlier. A table at ten, and calming someone, do not.

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

Task comparison

Hotel concierge

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
Bookings, maps and recommendations.
Estimate

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

16%40%+6.4
Desk, bags and difficult guests.
Estimate

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

20%12%+2.4
Restaurants, tickets and transport.
Estimate

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

16%22%+3.5
Logs, shifts and front-desk coordination.
Estimate

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

16%28%+4.5
Neighbourhood, vendors and errands.
Estimate

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

16%16%+2.6
Not leaving a guest stranded at two in the morning.
Estimate

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

16%14%+2.2
Total100%22%

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

Skills and activities

  • Bookings, maps and recommendations
  • Desk, bags and difficult guests
  • Restaurants, tickets and transport
  • Logs, shifts and front-desk coordination
  • Neighbourhood, vendors and errands
  • conserje
  • concierge
  • portero de hotel

Sources for this job· O*NET 43-4081.00· ESCO/ISCO 4224

Agile coach

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
Boards, metrics and the cadence design.
Estimate

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

16%40%+6.4
The real retro and the elephant in the room.
Estimate

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

18%14%+2.5
Train, unblock and the manager who will not let go.
Estimate

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

16%32%+5.1
Scale, dependencies and fake agile.
Estimate

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

16%28%+4.5
Conflict, power and not selling extra frameworks.
Estimate

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

18%18%+3.2
That they stop faking it, and not inflating velocity.
Estimate

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

16%14%+2.2
Total100%24%

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

Skills and activities

  • retros
  • agile cadence
  • unblock teams
  • flow metrics
  • fake agile
  • agile training
  • dependencies
  • velocity

Sources for this job· O*NET 13-1111.00· ESCO/ISCO 2421

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