IS WORK OVER?

Automation index

How much of a Revenue manager’s job can AI already do?

A pickup comes earlier. Raising the rate without emptying the hotel do not.

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

Task comparison

Revenue manager

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
Pickup, competitors and the rate.
Estimate

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

18%48%+8.6
Channels, overbooking and the one left on the street.
Estimate

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

16%40%+6.4
Events, the holiday and not sleeping on the price.
Estimate

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

16%22%+3.5
Report, management and the staff cut.
Estimate

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

16%28%+4.5
Sales, the group and not giving the room away.
Estimate

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

18%14%+2.5
A high rate and a full house, not empty.
Estimate

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

16%12%+1.9
Total100%27%

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

Skills and activities

  • pickup
  • rates
  • channels
  • overbooking
  • holidays
  • groups
  • competitors
  • rooms

Sources for this job· O*NET 11-9081.00· ESCO/ISCO 1411

Last-mile manager

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
Zones, capacity and route cuts.
Estimate

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

16%44%+7
KPIs, first attempt and incidents.
Estimate

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

16%36%+5.8
The courier, the lobby and the one who will not open.
Estimate

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

18%16%+2.9
Peaks, rain and hiring too many or too few.
Estimate

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

16%28%+4.5
The angry customer and not dressing delivered.
Estimate

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

18%18%+3.2
Cover the peak, and not leaving the neighbourhood orphaned.
Estimate

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

16%14%+2.2
Total100%26%

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

Skills and activities

  • delivery zones
  • first attempt
  • delivery KPIs
  • last-mile peaks
  • lobbies
  • delivery incidents
  • courier capacity
  • cut routes

Sources for this job· O*NET 11-3071.00· ESCO/ISCO 1324

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