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
IS WORK OVER?
Executive housekeeper
21%
AI can already do 21% of this job.
#422 / 633
View rankingExecutive 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.
| Task | Share | AI | Points |
|---|---|---|---|
Roster, checklists and linen logs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 40% | +7.2 |
Rounds, standards and VIP rooms.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Room team, shifts and conflict.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Front desk, maintenance and complaints.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Inventory, minibar and lost-and-found.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 16% | +2.6 |
Not handing a dirty room to a guest.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 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
IS WORK OVER?
ML engineer
29%
#64 / 633
View rankingML engineer
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 |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511