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?
Hotel manager
27%
AI can already do 27% of this job.
#147 / 623
View rankingHotel 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.
| Task | Share | AI | Points |
|---|---|---|---|
Occupancy, rates and the forecast.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Shifts, complaints and the one who leaves staff.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Owner, the budget and not cutting breakfast.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Incident, the VIP and the one who floods the room.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The inspection, insurance and not dressing the log.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That the house runs, and breakfast standing.EstimateNo 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
Skills and activities
- occupancy
- hotel forecast
- shifts
- complaints
- VIP
- breakfast
- inspection
- rates
Sources for this job· O*NET 11-9081.00· ESCO/ISCO 1411
IS WORK OVER?
Data architect
28%
#83 / 623
View rankingData architect
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.
| Task | Share | AI | Points |
|---|---|---|---|
Models, lineage and the catalogue.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Pipelines, quality and the data contract.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
The analyst who finds nothing and the mess.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Privacy, retention and the field you cannot keep.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The business, shadow IT and not building another lake.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That it gets used, and not letting raw data rot.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- model data
- lineage
- data catalogue
- data contracts
- pipeline quality
- retention
- data lakes
- privacy
Sources for this job· O*NET 15-1243.00· ESCO/ISCO 2511