Automation index
How much of a Private-hire driver’s job can AI already do?
An app and a route come earlier. Carrying someone who arrives in one piece does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Private-hire driver
20%
AI can already do 20% of this job.
#445 / 633
View rankingPrivate-hire 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 |
|---|---|---|---|
App, routes and airport times.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Drive, pick up and drop the client.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 10% | +2.2 |
Car, cleanliness and maintenance.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Service, bags and difficult clients.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Shifts, fatigue and zones.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 16% | +2.6 |
Not missing a flight or a passenger.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 14% | +2 |
| Total | 100% | 20% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- drive
- route
- load goods
- App, routes and airport times
- Drive, pick up and drop the client
- Car, cleanliness and maintenance
- Service, bags and difficult clients
- Shifts, fatigue and zones
- Not missing a flight or a passenger
- conductor vtc
Sources for this job· O*NET 53-3051.00· ESCO/ISCO 8322
IS WORK OVER?
ML engineer
29%
#64 / 633
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