Automation index
How much of a Banquet manager’s job can AI already do?
A menu and a table plan come earlier. The service going out without the plate going cold do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Banquet manager
22%
AI can already do 22% of this job.
#345 / 633
View rankingBanquet 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 |
|---|---|---|---|
Menu, plan and the timing.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Set-up, staff and the one who is late.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Service, the toast and the one without a plate.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Kitchen, the pass and not working too far ahead.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The groom, the change and not mixing allergies.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
The service goes out, and the plate hot.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- table plans
- banquet timing
- set-up
- toast
- kitchen pass
- allergies
- floor staff
- menus
Sources for this job· O*NET 35-1012.00· ESCO/ISCO 1412
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