Automation index
How much of a Valet’s job can AI already do?
A key tag comes earlier. Not marking the car and having it ready when they come out do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Valet
19%
AI can already do 19% of this job.
#546 / 633
View rankingValet
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 |
|---|---|---|---|
Tag, keys and the bay map.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Drive someone else’s, the tight car park and not marking it.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
The client already out and the car still downstairs.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Insurance, the knock and who pays.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The event, the rain and not losing a key.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
No mark, and the car on time.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
| Total | 100% | 19% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- keep keys
- park others’ cars
- bay map
- not mark
- valet events
- knock insurance
- tight car park
- return on time
Sources for this job· O*NET 53-6021.00· ESCO/ISCO 8322
IS WORK OVER?
Data architect
28%
#83 / 633
View rankingData architect
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 |
|---|---|---|---|
Models, lineage and the catalogue.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Pipelines, quality and the data contract.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
The analyst who finds nothing and the mess.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Privacy, retention and the field you cannot keep.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The business, shadow IT and not building another lake.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That it gets used, and not letting raw data rot.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- model data
- lineage
- data catalogue
- data contracts
- pipeline quality
- retention
- data lakes
- privacy
Sources for this job· O*NET 15-1243.00· ESCO/ISCO 2511