Automation index

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

An occupancy forecast comes earlier. The house running without breakfast collapsing do not.

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

Task comparison

IS WORK OVER?

27%

AI can already do 27% of this job.

#147 / 623

Hotel manager

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.

Task	Share	AI	Points
Occupancy, rates and the forecast. Estimate No public source provides a precise figure. This is our estimate.	18%	48%	+8.6
Shifts, complaints and the one who leaves staff. Estimate No public source provides a precise figure. This is our estimate.	16%	40%	+6.4
Owner, the budget and not cutting breakfast. Estimate No public source provides a precise figure. This is our estimate.	16%	22%	+3.5
Incident, the VIP and the one who floods the room. Estimate No public source provides a precise figure. This is our estimate.	16%	28%	+4.5
The inspection, insurance and not dressing the log. Estimate No public source provides a precise figure. This is our estimate.	18%	14%	+2.5
That the house runs, and breakfast standing. Estimate No public source provides a precise figure. This is our estimate.	16%	12%	+1.9
Total	100%		27%

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

IS WORK OVER?

26%

#211 / 623

Last-mile manager

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.

Task	Share	AI	Points
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
Total	100%		26%

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