Automation index
How much of a Props master’s job can AI already do?
A props list comes earlier. The object being there, working and not breaking on stage do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Props master
19%
AI can already do 19% of this job.
#488 / 643
View rankingProps master
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 |
|---|---|---|---|
List, references and the budget.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Make, adapt and the one that will not fit the hand.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Rehearsal, the change and the one that vanishes.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Show, tables and the preset.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The actor, the whim and not improvising the weapon.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
That it is there, and not breaking on stage.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
| Total | 100% | 19% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- props lists
- presets
- adapt objects
- props rehearsals
- show tables
- stage weapons
- references
- props budget
Sources for this job· O*NET 27-1027.00· ESCO/ISCO 3435
IS WORK OVER?
Last-mile manager
26%
#216 / 643
View rankingLast-mile 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 |
|---|---|---|---|
Zones, capacity and route cuts.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 44% | +7 |
KPIs, first attempt and incidents.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
The courier, the lobby and the one who will not open.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 16% | +2.9 |
Peaks, rain and hiring too many or too few.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The angry customer and not dressing delivered.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Cover the peak, and not leaving the neighbourhood orphaned.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 26% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- delivery zones
- first attempt
- delivery KPIs
- last-mile peaks
- lobbies
- delivery incidents
- courier capacity
- cut routes
Sources for this job· O*NET 11-3071.00· ESCO/ISCO 1324