IS WORK OVER?

Automation index

How much of a Makeup artist’s job can AI already do?

A moodboard and a product list come earlier. Odd skin, a live show, and sweat do not.

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

Task comparison

Makeup artist

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
Moodboards, lists and quotes.
Estimate

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

16%38%+6.1
Bookings, consents and social.
Estimate

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

14%28%+3.9
Make up, hair and retouch on set.
Estimate

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

24%6%+1.4
Difficult skin, allergies and sweat.
Estimate

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

16%10%+1.6
Talent, director and the clock.
Estimate

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

16%12%+1.9
Kit, hygiene and travel.
Estimate

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

14%16%+2.2
Total100%17%

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

Skills and activities

  • Moodboards, lists and quotes
  • Bookings, consents and social
  • Make up, hair and retouch on set
  • Difficult skin, allergies and sweat
  • Talent, director and the clock
  • Kit, hygiene and travel
  • maquillador
  • makeup artist
  • maquillaje

Sources for this job· O*NET 39-5091.00· ESCO/ISCO 5142

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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