Automation index
How much of a Parking attendant’s job can AI already do?
A barrier comes earlier. Running the car park without wrecking the person who lost the ticket do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Parking attendant
24%
AI can already do 24% of this job.
#260 / 623
View rankingParking attendant
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 |
|---|---|---|---|
Barriers, tickets and the count.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
The one who cannot find the car and the one who will not pay.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Incidents, the down barrier and the jam.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Shift, cash and cashing up.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
The event, the full house and not blocking the street.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Run the car park, and not wrecking the lost-ticket person.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 24% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- barriers
- parking tickets
- space count
- cashing up
- stuck barrier
- events
- lost cars
- exit jam
Sources for this job· O*NET 33-9091.00· ESCO/ISCO 5414
IS WORK OVER?
ML engineer
29%
#64 / 623
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