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

Station 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.

TaskShareAIPoints
Graph, orders and the station log.
Estimate

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

16%40%+6.4
Platforms, the crossing and the late train.
Estimate

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

18%14%+2.5
Staff, shifts and the ticket-office gap.
Estimate

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

16%32%+5.1
Incident, passengers and the PA.
Estimate

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

16%22%+3.5
Works, closures and leaving nobody on the platform.
Estimate

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

18%18%+3.2
That they do not meet, and signing the graph.
Estimate

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

16%12%+1.9
Total100%23%

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

Skills and activities

  • train graph
  • orders
  • platforms
  • ticket office
  • PA
  • line crossings
  • station shifts
  • station logs

Sources for this job· O*NET 53-4031.00· ESCO/ISCO 4323

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