IS WORK OVER?

Automation index

How much of a Massage therapist’s job can AI already do?

A chart and a booking come earlier. Hands on a trapezius, and not doing harm, do not.

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

Task comparison

Massage therapist

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
Bookings, charts and consent.
Estimate

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

14%32%+4.5
Massage, pressure and tissue work.
Estimate

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

24%8%+1.9
Assess complaint, contraindication and referral.
Estimate

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

16%18%+2.9
Explain posture and exercises.
Estimate

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

16%22%+3.5
Couch hygiene, oils and the room.
Estimate

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

16%14%+2.2
The therapist’s body and not injuring anyone.
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

  • Bookings, charts and consent
  • Massage, pressure and tissue work
  • Explain posture and exercises
  • Couch hygiene, oils and the room
  • masajista
  • massage therapist

Sources for this job· O*NET 31-9011.00· ESCO/ISCO 3255

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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