Dining Room and Cafeteria Attendants and Bartender Helpers

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 0.0), with simple added tooling (β 0.0), and including AI-powered software (γ 0.0). 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.

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

• Run cash registers. Core · importance 4.5
• Serve ice water, coffee, rolls, or butter to patrons. Core · importance 4.5
• Scrape and stack dirty dishes and carry dishes and other tableware to kitchens for cleaning. Core · importance 4.5
• Wipe tables or seats with dampened cloths or replace dirty tablecloths. Core · importance 4.5
• Set tables with clean linens, condiments, or other supplies. Core · importance 4.5
• Greet and seat customers. Core · importance 4.5
• Clean up spilled food or drink or broken dishes and remove empty bottles and trash. Core · importance 4.4
• Maintain adequate supplies of items, such as clean linens, silverware, glassware, dishes, or trays. Core · importance 4.4
• Locate items requested by customers. Core · importance 4.3
• Fill beverage or ice dispensers. Core · importance 4.3
• Carry food, dishes, trays, or silverware from kitchens or supply departments to serving counters. Core · importance 4.3
• Perform serving, cleaning, or stocking duties in establishments, such as cafeterias or dining rooms, to facilitate customer service. Core · importance 4.3
• Carry trays from food counters to tables for cafeteria patrons. Core · importance 4.2
• Replenish supplies of food or equipment at steam tables or service bars. Supplemental · importance 4.2
• Wash glasses or other serving equipment at bars. Supplemental · importance 4.1
• Stock cabinets or serving areas with condiments and refill condiment containers. Core · importance 4.1
• Serve food to customers when waiters or waitresses need assistance. Core · importance 4.0
• Carry linens to or from laundry areas. Supplemental · importance 4.0
• Garnish foods and position them on tables to make them visible and accessible. Supplemental · importance 4.0
• Clean and polish counters, shelves, walls, furniture, or equipment in food service areas or other areas of restaurants and mop or vacuum floors. Core · importance 3.9
• Mix and prepare flavors for mixed drinks. Supplemental · importance 3.6
• Slice and pit fruit used to garnish drinks. Supplemental · importance 3.6
• Stock refrigerating units with wines or bottled beer or replace empty beer kegs. Supplemental · importance 3.3
• Stock vending machines with food. Supplemental

Emerging tasks

Newer responsibilities O*NET has flagged as growing for this occupation.

• Prepare food, such as sandwiches, for customers.

Work activities

• Clean food service areas.
• Arrange tables or dining areas.
• Collect dirty dishes or other tableware.
• Clean facilities or work areas.
• Serve food or beverages.
• Store supplies or goods in kitchens or storage areas.
• Maintain food, beverage, or equipment inventories.
• Stock serving stations or dining areas with food or supplies.
• Provide customers with general information or assistance.
• Move equipment, supplies or food to required locations.
• Clean food preparation areas, facilities, or equipment.
• Operate cash registers.
• Clean tableware.
• Add garnishes to food.
• Arrange food for serving.
• Mix ingredients.
• Cut cooked or raw foods.
• Usher patrons to seats or exits.
• Greet customers, patrons, or visitors.
• Assist customers to ensure comfort or safety.

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

Career interests (Holland / RIASEC)

Interest areas

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 Hospitality, Events, & Tourism career cluster.

• Food Servers, Nonrestaurant · 35-3041.00 Compare →
• Fast Food and Counter Workers · 35-3023.00 Compare →
• Waiters and Waitresses · 35-3031.00 Compare →
• Food Preparation Workers · 35-2021.00 Compare →
• Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop · 35-9031.00 Compare →
• Baristas · 35-3023.01 Compare →
• Dishwashers · 35-9021.00 Compare →
• Maids and Housekeeping Cleaners · 37-2012.00 Compare →
• Bartenders · 35-3011.00 Compare →
• Cooks, Short Order · 35-2015.00 Compare →
• First-Line Supervisors of Food Preparation and Serving Workers · 35-1012.00 Compare →
• Food Service Managers · 11-9051.00 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 Dining Room and Cafeteria Attendants and Bartender Helpers — 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
• 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-35-9011-00,
  title  = {Dining Room and Cafeteria Attendants and Bartender Helpers},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; 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-35-9011-00}
}