Beverage Directors

Food Preparation & Service

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

    0.36

    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, 60% 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

Show 6 hidden tasks

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
Keep records required by government agencies regarding sanitation or food subsidies.

O*NET Task ID 1085

1.0
Create specialty dishes and develop recipes to be used in dining facilities.

O*NET Task ID 1101

1.0
Investigate and resolve complaints regarding food quality, service, or accommodations.

O*NET Task ID 1078

0.5
Schedule and receive food and beverage deliveries, checking delivery contents to verify product quality and quantity.

O*NET Task ID 1079

0.5
Monitor budgets and payroll records, and review financial transactions to ensure that expenditures are authorized and budgeted.

O*NET Task ID 1081

0.5
Schedule staff hours and assign duties.

O*NET Task ID 1082

0.5
Monitor compliance with health and fire regulations regarding food preparation and serving, and building maintenance in lodging and dining facilities.

O*NET Task ID 1083

0.5
Coordinate assignments of cooking personnel to ensure economical use of food and timely preparation.

O*NET Task ID 1084

0.5
Establish standards for personnel performance and customer service.

O*NET Task ID 1086

0.5
Estimate food, liquor, wine, and other beverage consumption to anticipate amounts to be purchased or requisitioned.

O*NET Task ID 1087

0.5

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