Dishwashers

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.

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 occupation 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 13 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

• Wash dishes, glassware, flatware, pots, or pans, using dishwashers or by hand. Core · importance 4.5
• Maintain kitchen work areas, equipment, or utensils in clean and orderly condition. Core · importance 4.4
• Place clean dishes, utensils, or cooking equipment in storage areas. Core · importance 4.3
• Sweep or scrub floors. Core · importance 4.2
• Load or unload trucks that deliver or pick up food or supplies. Supplemental · importance 4.0
• Stock supplies, such as food or utensils, in serving stations, cupboards, refrigerators, or salad bars. Core · importance 3.9
• Clean or prepare various foods for cooking or serving. Supplemental · importance 3.8
• Sort and remove trash, placing it in designated pickup areas. Core · importance 3.8
• Transfer supplies or equipment between storage and work areas, by hand or using hand trucks. Core · importance 3.7
• Receive and store supplies. Core · importance 3.6
• Clean garbage cans with water or steam. Core · importance 3.6
• Prepare and package individual place settings. Supplemental · importance 3.2
• Set up banquet tables. Supplemental · importance 3.2

Emerging tasks

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

• Clean and sanitize the dining area, including tables.

Work activities

• Clean tableware.
• Store supplies or goods in kitchens or storage areas.
• Clean food preparation areas, facilities, or equipment.
• Stock serving stations or dining areas with food or supplies.
• Remove trash.
• Prepare foods for cooking or serving.
• Arrange tables or dining areas.
• Move equipment, supplies or food to required locations.
• Package food or supplies.
• Load shipments, belongings, or materials.

Knowledge, skills & abilities

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

Abilities

Knowledge

Transferable skills

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

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.

• Cleaners of Vehicles and Equipment · 53-7061.00 Compare →
• Laundry and Dry-Cleaning Workers · 51-6011.00 Compare →
• Food Preparation Workers · 35-2021.00 Compare →
• Dining Room and Cafeteria Attendants and Bartender Helpers · 35-9011.00 Compare →
• Maids and Housekeeping Cleaners · 37-2012.00 Compare →
• Food Servers, Nonrestaurant · 35-3041.00 Compare →
• Fast Food and Counter Workers · 35-3023.00 Compare →
• Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders · 51-9192.00 Compare →
• Janitors and Cleaners, Except Maids and Housekeeping Cleaners · 37-2011.00 Compare →
• Cooks, Short Order · 35-2015.00 Compare →
• Food and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders · 51-3091.00 Compare →
• Packaging and Filling Machine Operators and Tenders · 51-9111.00 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 Dishwashers — 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-35-9021-00,
  title  = {Dishwashers},
  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-35-9021-00}
}