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?
Prompt engineer
34%
#21 / 633
View rankingPrompt 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 prompts, tools and chains.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 58% | +13.9 |
Evals, datasets and regressions.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Integrate with product and safety limits.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to the business about a good output.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Watch hallucinations, bias and cost.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 20% | +2.8 |
Decide what the model gets, and what it does not.EstimateNo public source provides a precise figure. This is our estimate. | 12% | 16% | +1.9 |
| Total | 100% | 34% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Design prompts, tools and chains
- Evals, datasets and regressions
- Integrate with product and safety limits
- Talk to the business about a good output
- Watch hallucinations, bias and cost
- prompt engineer
- ingeniero de prompts
- llm engineer
Sources for this job· O*NET 15-1252.00· ESCO/ISCO 2512