IS WORK OVER?

Automation index

How much of a Camera operator’s job can AI already do?

A schedule and an overlay come earlier. The shot you have to nail does not.

This percentage does not predict how many people will lose their jobs.

Task comparison

Camera operator

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.

TaskShareAIPoints
Schedule, lists and overlays.
Estimate

No public source provides a precise figure. This is our estimate.

16%32%+5.1
Frame, follow and not lose focus.
Estimate

No public source provides a precise figure. This is our estimate.

22%10%+2.2
Kit, cards and backups.
Estimate

No public source provides a precise figure. This is our estimate.

16%18%+2.9
Director, light and sound.
Estimate

No public source provides a precise figure. This is our estimate.

16%22%+3.5
Knees, shoulder and long days.
Estimate

No public source provides a precise figure. This is our estimate.

16%16%+2.6
Not wiping a full card.
Estimate

No public source provides a precise figure. This is our estimate.

14%14%+2
Total100%18%

Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low

Skills and activities

  • Schedule, lists and overlays
  • Frame, follow and not lose focus
  • Kit, cards and backups
  • Director, light and sound
  • Knees, shoulder and long days
  • Not wiping a full card
  • camara
  • operador de camara
  • camera operator

Sources for this job· O*NET 27-4031.00· ESCO/ISCO 3521

Last-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.

TaskShareAIPoints
Zones, capacity and route cuts.
Estimate

No public source provides a precise figure. This is our estimate.

16%44%+7
KPIs, first attempt and incidents.
Estimate

No 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.
Estimate

No public source provides a precise figure. This is our estimate.

18%16%+2.9
Peaks, rain and hiring too many or too few.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
The angry customer and not dressing delivered.
Estimate

No public source provides a precise figure. This is our estimate.

18%18%+3.2
Cover the peak, and not leaving the neighbourhood orphaned.
Estimate

No public source provides a precise figure. This is our estimate.

16%14%+2.2
Total100%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

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