Automation index
How much of a Ambulance driver’s job can AI already do?
A call and a route come earlier. Arriving in one piece and not making the patient worse do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Ambulance driver
18%
AI can already do 18% of this job.
#571 / 643
View rankingAmbulance driver
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 |
|---|---|---|---|
Calls, routes and service logs.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Drive urgent, park and help the technician.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 10% | +2.2 |
Stretcher, transfer and family.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Cleaning, kit and handovers.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Hospital, ICU and times.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 16% | +2.6 |
Not arriving late to a heart attack.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 14% | +2 |
| Total | 100% | 18% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- drive
- route
- load goods
- Calls, routes and service logs
- Drive urgent, park and help the technician
- Stretcher, transfer and family
- Cleaning, kit and handovers
- Hospital, ICU and times
- Not arriving late to a heart attack
- conductor de ambulancia
Sources for this job· O*NET 53-3011.00· ESCO/ISCO 8322
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