Food Service Managers
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
0.36
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, 60% 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
Show 23 hidden tasks| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Assess staffing needs and recruit staff, using methods such as newspaper advertisements or attendance at job fairs.11-9051 | 0.003528.9 | 0.001612.4 |
Investigate and resolve complaints regarding food quality, service, or accommodations.11-9051 | 0.002924.0 | 0.009170.5 |
Estimate food, liquor, wine, and other beverage consumption to anticipate amounts to be purchased or requisitioned.11-9051 | 0.002016.5 | —0 |
Maintain food and equipment inventories, and keep inventory records.11-9051 | 0.001915.7 | 0.002217.1 |
Review menus and analyze recipes to determine labor and overhead costs, and assign prices to menu items.11-9051 | 0.001814.9 | —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 |
|---|---|
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®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page