Baristas

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

    0.24

    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, 79% 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
Describe menu items to customers, or suggest products that might appeal to them.

O*NET Task ID 17585

1.0
Provide customers with product details, such as coffee blend or preparation descriptions.

O*NET Task ID 17587

1.0
Create signs to advertise store products or events.

O*NET Task ID 17595

0.5
Prepare or serve hot or cold beverages, such as coffee, espresso drinks, blended coffees, or teas.

O*NET Task ID 17581

0.0
Clean or sanitize work areas, utensils, or equipment.

O*NET Task ID 17582

0.0
Clean service or seating areas.

O*NET Task ID 17583

0.0
Check temperatures of freezers, refrigerators, or heating equipment to ensure proper functioning.

O*NET Task ID 17584

0.0
Order, receive, or stock supplies or retail products.

O*NET Task ID 17586

0.0
Receive and process customer payments.

O*NET Task ID 17588

0.0
Serve prepared foods, such as muffins, biscotti, or bagels.

O*NET Task ID 17589

0.0
Stock customer service stations with paper products or beverage preparation items.

O*NET Task ID 17590

0.0
Take customer orders and convey them to other employees for preparation.

O*NET Task ID 17591

0.0
Take out garbage.

O*NET Task ID 17592

0.0
Weigh, grind, or pack coffee beans for customers.

O*NET Task ID 17593

0.0
Wrap, label, or date food items for sale.

O*NET Task ID 17594

0.0
Demonstrate the use of retail equipment, such as espresso machines.

O*NET Task ID 17596

0.0
Prepare or serve menu items, such as sandwiches or salads.

O*NET Task ID 17597

0.0
Set up or restock product displays.

O*NET Task ID 17598

0.0
Slice fruits, vegetables, desserts, or meats for use in food service.

O*NET Task ID 17599

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