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.
#417 / 623
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?
Data engineer
31%
#36 / 623
View rankingData 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 |
|---|---|---|---|
Design pipelines, models and orchestration.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 50% | +12 |
Write SQL, Spark and data tests.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 45% | +8.1 |
Incidents, quality and lineage in production.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to analysts and negotiate data contracts.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Cloud cost, permissions and PII.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 15% | +2.1 |
Decide architecture and technical debt.EstimateNo public source provides a precise figure. This is our estimate. | 12% | 20% | +2.4 |
| Total | 100% | 31% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Design pipelines, models and orchestration
- Write SQL, Spark and data tests
- Cloud cost, permissions and PII
- Decide architecture and technical debt
- ingeniero de datos
- data engineer
- etl
Sources for this job· O*NET 15-1243.00· ESCO/ISCO 2511