Food Servers, Nonrestaurant

AI & job outlook

What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.

Exposure to current AI

Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.1), with simple added tooling (β 0.1), and including AI-powered software (γ 0.2). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.

This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.

Mixed signals. Today's AI/LLM studies show relatively low exposure for this job, but the older (2013) Frey–Osborne work rated it higher for computerization and robotics. Different eras, different technologies — the AI measures above reflect the current state.

How AI is actually used in this job

Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.

Job outlook

Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.

“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.

Where this work sits on the global GenAI gradient

The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international 2 occupations below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.

Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.

Tasks

All 14 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

• Place food servings on plates or trays according to orders or instructions. Core · importance 4.8
• Clean or sterilize dishes, kitchen utensils, equipment, or facilities. Core · importance 4.8
• Monitor food distribution, ensuring that meals are delivered to the correct recipients and that guidelines, such as those for special diets, are followed. Core · importance 4.8
• Examine trays to ensure that they contain required items. Core · importance 4.7
• Load trays with accessories, such as eating utensils, napkins, or condiments. Core · importance 4.6
• Take food orders and relay orders to kitchens or serving counters so they can be filled. Core · importance 4.5
• Total checks, present them to customers, and accept payment for services. Supplemental · importance 4.5
• Monitor food preparation or serving techniques to ensure that proper procedures are followed. Core · importance 4.5
• Remove trays and stack dishes for return to kitchen after meals are finished. Core · importance 4.5
• Carry food, silverware, or linen on trays or use carts to carry trays. Core · importance 4.4
• Record amounts and types of special food items served to customers. Core · importance 4.3
• Stock service stations with items, such as ice, napkins, or straws. Core · importance 4.3
• Prepare food items, such as sandwiches, salads, soups, or beverages. Core · importance 4.3
• Determine where patients or patrons would like to eat their meals and help them get situated. Core · importance 4.2

Work activities

• Monitor food services operations to ensure procedures are followed.
• Clean tableware.
• Check quality of foods or supplies.
• Inspect food products.
• Arrange food for serving.
• Stock serving stations or dining areas with food or supplies.
• Communicate dining or order details to kitchen personnel.
• Collect dirty dishes or other tableware.
• Cook foods.
• Move equipment, supplies or food to required locations.
• Assist customers with seating arrangements.
• Record operational or production data.
• Process customer bills or payments.

Knowledge, skills & abilities

O*NET importance rating, from 1 (not important) to 5 (extremely important).

Knowledge

Abilities

Essential skills

Transferable skills

Skills in demand

Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.

• English Language Common Skill
• Speech Recognition Specialized Skill
• Microsoft Windows Common Skill
• Microsoft Outlook Common Skill
• Facebook Specialized Skill
• Active Listening Common Skill
• Time Management Common Skill
• Social Perceptiveness Common Skill
• Reading Comprehension Common Skill
• Information Ordering Specialized Skill
• Inductive Reasoning Common Skill
• Deductive Reasoning Common Skill
• Critical Thinking Common Skill
• Mathematics Common Skill
• Psychology Specialized Skill
• Telecommunications Specialized Skill
• Finger Dexterity Common Skill
• Visualization Specialized Skill
• Negotiation Common Skill
• Instructing Specialized Skill
• Complex Problem Solving Common Skill
• Chemistry Specialized Skill
• Writing Common Skill
• Systems Analysis Specialized Skill
• Learning Strategies Specialized Skill
• Depth Perception Common Skill
• Active Learning Common Skill
• Biology Specialized Skill
• Foreign Language Common Skill
• Physics Specialized Skill
• Geography Specialized Skill
• Equipment Selection Specialized Skill
• Equipment Maintenance Specialized Skill
• Installation Specialized Skill

Tools & technology

Work context

How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.

How to get in

Job zone

Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed

Education

Usually requires a high school diploma or GED, though some occupations may not.

Typical entry-level education

No formal educational credential · BLS, the typical path — not a requirement

Related experience

Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.

Preparation level

SVP (Below 6.0) — total schooling plus on-the-job experience.

Education of current workers

Share of people in this occupation at each level of education.

Interests & work styles

The interests and personal qualities O*NET associates with people who do this work.

Interest areas

Career interests (Holland / RIASEC)

Work styles

Wages & employment

U.S. · annual wages (BLS OEWS)

Industries that employ this occupation

Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.

Where this work is most concentrated

Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).

Related occupations

Part of the Healthcare & Human Services career cluster.

• Dining Room and Cafeteria Attendants and Bartender Helpers · 35-9011.00 Compare →
• Fast Food and Counter Workers · 35-3023.00 Compare →
• Waiters and Waitresses · 35-3031.00 Compare →
• Food Preparation Workers · 35-2021.00 Compare →
• Dishwashers · 35-9021.00 Compare →
• Cooks, Restaurant · 35-2014.00 Compare →
• Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop · 35-9031.00 Compare →
• Cooks, Institution and Cafeteria · 35-2012.00 Compare →
• Cooks, Short Order · 35-2015.00 Compare →
• Food Service Managers · 11-9051.00 Compare →
• Baristas · 35-3023.01 Compare →
• Maids and Housekeeping Cleaners · 37-2012.00 Compare →

Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.

What you can do with this

Options the data surfaces for Food Servers, Nonrestaurant — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Sources for this page

Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.

• O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
• BLS Occupational Employment and Wage Statistics (OEWS) May 2024 U.S. Bureau of Labor Statistics
• BLS Employment Projections 2024–2034 U.S. Bureau of Labor Statistics
• Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
• Microsoft “Working with AI” working-with-ai Microsoft Research
• “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic
• AI Occupational Exposure (AIOE) Felten, Raj & Seamans academic
• ILO / Gmyrek et al. GenAI exposure gradient 2025 International Labour Organization
• IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022 Institute for Structural Research (IBS)
• Frey & Osborne (2013) frey-osborne-automation academic
• Dingel & Neiman (2020) dingel-neiman-workathome academic

Data compiled June 2, 2026. Figures are estimates, not advice.

@misc{singulariki-role-35-3041-00,
  title  = {Food Servers, Nonrestaurant},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
  url    = {https://singulariki.com/roles/role-35-3041-00}
}