Automation index
How much of a Car salesperson’s job can AI already do?
A valuer and stock come earlier. Closing the deal without the car coming back do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Car salesperson
22%
AI can already do 22% of this job.
#399 / 633
View rankingCar salesperson
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 |
|---|---|---|---|
Stock, valuation and the card.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Test drive, objections and the one who will not decide.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Finance, the bank and the one who fails.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Handover, the extra and the one who argues the scratch.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The boss, the margin and not giving the discount away.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Close the deal, and it not coming back.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- value cars
- test drives
- finance
- handover
- stock
- margin
- scratches
- objections
Sources for this job· O*NET 41-2031.00· ESCO/ISCO 5223
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