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
IS WORK OVER?
Revenue manager
27%
AI can already do 27% of this job.
#175 / 623
View rankingRevenue 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 |
|---|---|---|---|
Pickup, competitors and the rate.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Channels, overbooking and the one left on the street.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Events, the holiday and not sleeping on the price.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Report, management and the staff cut.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Sales, the group and not giving the room away.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
A high rate and a full house, not empty.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
- pickup
- rates
- channels
- overbooking
- holidays
- groups
- competitors
- rooms
Sources for this job· O*NET 11-9081.00· ESCO/ISCO 1411
IS WORK OVER?
ML engineer
29%
#64 / 623
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