Parking Lot Attendants

Transportation & Material Moving

AI exposure

  • Data source: BLSPublished: 2026-08

    Moderate· relative

    LowFour relative bandsVery high

    Group-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

    Source dataset (XLSX download)

  • Data source: AnthropicPublished: 2026-03

    0.000

    0.000Range of values carried here0.745
    Scale, basis and source

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Source dataset

  • Data source: ILOPublished: 2025

    0.29

    0.09Range of values carried here0.70

    Group-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.

    Source dataset

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

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.

TaskβE1 + 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®, Eloundou et al. (2023): see full notices on the Credits page

Occupation information