Automation index
How much of a Stage manager’s job can AI already do?
A prompt book comes earlier. Calling the cue so the show goes up do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Stage manager
22%
AI can already do 22% of this job.
#375 / 633
View rankingStage 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 |
|---|---|---|---|
Book, cues and the day’s plan.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Show, the delay and the one who is not there.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Rehearsals, changes and updating the book.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Crews, the clash and not missing the cue.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Opening, nerves and not bringing the curtain early.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
That the show goes up, and the cue on time.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
- prompt book
- cues
- show plan
- rehearsals
- crews
- opening night
- curtain
- rehearsal changes
Sources for this job· O*NET 27-2012.00· ESCO/ISCO 3435
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