Valet Parking Attendants
Food Preparation & ServiceAI exposure
- Data source: BLSPublished: 2026-08
Moderate· relative
LowFour relative bandsVery highGroup-level value
Scale, basis and source
Four relative bands (Low / Moderate / High / Very high)
831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band
- Data source: AnthropicPublished: 2026-03
0.000
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: 2025
0.29
0.09Range of values carried here0.70Group-level value
Scale, basis and source
Generative AI exposure index, 0–1 as published
ISCO-08 unit group — every occupation sharing the code gets this value
Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 69% score at or above this value.
What kind of figure this source publishes
The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.
Task-level exposure
Exposed tasks only| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Provide customer assistance and information, such as giving directions or handling wheelchairs.53-6021 | 0.0083100.0 | 0.0016100.0 |
| Not observed on any surface — 17 task(s) — These tasks have no row in the source for this release. The 0 in the share row is a display-stage composition ratio; absence is what the — in the raw row states. | ||
Take numbered tags from customers, locate vehicles, and deliver vehicles, or provide customers with instructions for locating vehicles. | —0 | —0 |
Keep parking areas clean and orderly to ensure that space usage is maximized. | —0 | —0 |
Park and retrieve automobiles for customers in parking lots, storage garages, or new car lots. | —0 | —0 |
Greet customers and open their car doors. | —0 | —0 |
Inspect vehicles to detect any damage. | —0 | —0 |
Issue ticket stubs or place numbered tags on windshields, log tags or attach tag to customers' keys, and give customers matching tags for locating parked vehicles. | —0 | —0 |
Perform cash handling tasks, such as making change, balancing and recording cash drawer, or distributing tips. | —0 | —0 |
Explain and calculate parking charges, collect fees from customers, and respond to customer complaints. | —0 | —0 |
Call emergency responders or the proper authorities and provide motorist assistance, such as giving directions or helping jump start a stalled vehicle. | —0 | —0 |
Direct motorists to parking areas or parking spaces, using hand signals or flashlights as necessary. | —0 | —0 |
Patrol parking areas to prevent vehicle damage and vehicle or property thefts. | —0 | —0 |
Lift, position, and remove barricades to open or close parking areas. | —0 | —0 |
Review motorists' identification before allowing them to enter parking facilities. | —0 | —0 |
Escort customers to their vehicles to ensure their safety. | —0 | —0 |
Service vehicles with gas, oil, and water. | —0 | —0 |
Perform maintenance on cars in storage to protect tires, batteries, or exteriors from deterioration. | —0 | —0 |
Perform personnel activities, such as supervising or scheduling employees. | —0 | —0 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Perform personnel activities, such as supervising or scheduling employees.O*NET Task ID 20840 | 1.0 |
Inspect vehicles to detect any damage.O*NET Task ID 3196 | 0.5 |
Explain and calculate parking charges, collect fees from customers, and respond to customer complaints.O*NET Task ID 20837 | 0.5 |
Take numbered tags from customers, locate vehicles, and deliver vehicles, or provide customers with instructions for locating vehicles.O*NET Task ID 3187 | 0.0 |
Keep parking areas clean and orderly to ensure that space usage is maximized.O*NET Task ID 3188 | 0.0 |
Direct motorists to parking areas or parking spaces, using hand signals or flashlights as necessary.O*NET Task ID 3189 | 0.0 |
Patrol parking areas to prevent vehicle damage and vehicle or property thefts.O*NET Task ID 3190 | 0.0 |
Park and retrieve automobiles for customers in parking lots, storage garages, or new car lots.O*NET Task ID 3191 | 0.0 |
Greet customers and open their car doors.O*NET Task ID 3192 | 0.0 |
Lift, position, and remove barricades to open or close parking areas.O*NET Task ID 3195 | 0.0 |
Review motorists' identification before allowing them to enter parking facilities.O*NET Task ID 3197 | 0.0 |
Escort customers to their vehicles to ensure their safety.O*NET Task ID 3198 | 0.0 |
Service vehicles with gas, oil, and water.O*NET Task ID 3199 | 0.0 |
Perform maintenance on cars in storage to protect tires, batteries, or exteriors from deterioration.O*NET Task ID 3200 | 0.0 |
Issue ticket stubs or place numbered tags on windshields, log tags or attach tag to customers' keys, and give customers matching tags for locating parked vehicles.O*NET Task ID 20835 | 0.0 |
Perform cash handling tasks, such as making change, balancing and recording cash drawer, or distributing tips.O*NET Task ID 20836 | 0.0 |
Provide customer assistance and information, such as giving directions or handling wheelchairs.O*NET Task ID 20838 | 0.0 |
Call emergency responders or the proper authorities and provide motorist assistance, such as giving directions or helping jump start a stalled vehicle.O*NET Task ID 20839 | 0.0 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page