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
IS WORK OVER?
Massage therapist
17%
AI can already do 17% of this job.
#603 / 643
View rankingMassage 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.
| Task | Share | AI | Points |
|---|---|---|---|
Bookings, charts and consent.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 32% | +4.5 |
Massage, pressure and tissue work.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 8% | +1.9 |
Assess complaint, contraindication and referral.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Explain posture and exercises.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Couch hygiene, oils and the room.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
The therapist’s body and not injuring anyone.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 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
IS WORK OVER?
ML engineer
29%
#64 / 643
View rankingML engineer
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 |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511