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First-Line Supervisors of Food Preparation and Serving Workers

Occupation · SOC 35-1012.00

Directly supervise and coordinate activities of workers engaged in preparing and serving food.

Also called: Dietary Supervisor · Food Service Supervisor · Kitchen Manager · Restaurant Manager · Cafeteria Manager · Dining Services Director · Food Production Supervisor · Food and Beverage Director · Food and Nutrition Services Supervisor · Kitchen Supervisor · Banquet Captain · Banquet Supervisor

Job family: Food Preparation and Serving Related Occupations

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A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch /roles/role-35-1012-00/context.md directly.

AI work map

A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.

Often handed to AI

Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.

  • Specify food portions and courses, production and time sequences, and workstation and equipment arrangements. · 1.2%
See how AI is used here →

Use as a copilot

Task areas where people work with AI — iterating, learning, or checking — staying in the loop rather than handing the task off.

  • Train workers in food preparation, and in service, sanitation, and safety procedures. · 1.7%
  • Develop departmental objectives, budgets, policies, procedures, and strategies. · 0.3%
See collaboration patterns →

Keep a human in the loop

Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.

  • Develop departmental objectives, budgets, policies, procedures, and strategies. · 100.0% need a human
  • Train workers in food preparation, and in service, sanitation, and safety procedures. · 98.8% need a human
  • Specify food portions and courses, production and time sequences, and workstation and equipment arrangements. · 97.4% need a human
See the boundary tasks →

47th-percentile task overlap — yet about 183,900 openings a year (+6% projected, BLS), and observed AI use leans 5000% copilot, not hand-off (AEI) . What exposure means →

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.

Measure Rank vs all occupations Percentile Score
Overall AI exposure (Felten et al.) Moderate 42nd -0.3
LLM task exposure, γ (OpenAI / Eloundou) Moderate 65th 0.8
AI assistant applicability (Microsoft) Moderate 37th 0.1

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.2), with simple added tooling (β 0.5), and including AI-powered software (γ 0.8). 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.

Historical automation estimate (2013)

A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.

Frey–Osborne probability 0.6 · 53rd percentile among occupations · Moderate

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.

Resolve customer complaints regarding food service. 0.2%
Assess nutritional needs of patients, plan special menus, supervise the assembly of regular and special diet trays, and oversee the delivery of food trolleys to hospital patients. 0.2%

Job outlook

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

Outlook About average · +6.0% by 2034
Projected annual openings 183,900
Employment 2024 → 2034 1,215,000 → 1,288,000

“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.

22% mean task exposure (2025)
40th percentile of 427 placed occupations
−1 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Chefs · 3434 25% Not exposed
Cooks · 5120 18% Not exposed

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.

Working with AI in this job

How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.

Augmentation vs. automation 50.0% working with AI · 39.0% handed to AI
Most common way people use AI here Directive · AI does it; you give the instruction
Typical AI autonomy 4.0 / 5 · higher = AI acts more independently
Used for work (vs. personal / coursework) 22.3%

What people delegate to AI

The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.

Task How Usage
Train workers in food preparation, and in service, sanitation, and safety procedures. Learning 1.7%
Specify food portions and courses, production and time sequences, and workstation and equipment arrangements. Directive 1.2%
Develop departmental objectives, budgets, policies, procedures, and strategies. Iteration 0.3%

Where a human is still needed

Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.

Develop departmental objectives, budgets, policies, procedures, and strategies. 100.0%
Train workers in food preparation, and in service, sanitation, and safety procedures. 98.8%
Specify food portions and courses, production and time sequences, and workstation and equipment arrangements. 97.4%

What people most often hand AI here

Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.

  • Help me train workers in food preparation, and in service, sanitation, and safety procedures.

    From: Train workers in food preparation, and in service, sanitation, and safety procedures. · 1.7% of measured AI use · learning

  • Help me specify food portions and courses, production and time sequences, and workstation and equipment arrangements.

    From: Specify food portions and courses, production and time sequences, and workstation and equipment arrangements. · 1.2% of measured AI use · directive

  • Help me develop departmental objectives, budgets, policies, procedures, and strategies.

    From: Develop departmental objectives, budgets, policies, procedures, and strategies. · 0.3% of measured AI use · task iteration

Tasks

All 26 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

Work activities

Knowledge, skills & abilities

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

Knowledge

Customer and Personal Service 4.1
Administration and Management 3.6
Food Production 3.5
English Language 3.5
Production and Processing 3.3
Sales and Marketing 3.3
Education and Training 3.3
Personnel and Human Resources 3.1

Transferable skills

Management of Personnel Resources 4.0
Coordination 3.9
Instructing 3.8
Service Orientation 3.6
Social Perceptiveness 3.1
Negotiation 3.1
Time Management 3.1
Persuasion 3.0
Complex Problem Solving 3.0
Judgment and Decision Making 3.0
Systems Analysis 3.0
Systems Evaluation 3.0
Management of Material Resources 3.0

Abilities

Oral Comprehension 4.0
Oral Expression 4.0
Problem Sensitivity 3.9
Deductive Reasoning 3.8
Speech Clarity 3.6
Speech Recognition 3.5
Written Comprehension 3.1
Written Expression 3.0
Fluency of Ideas 3.0
Inductive Reasoning 3.0

Essential skills

Speaking 3.9
Monitoring 3.9
Active Listening 3.8
Reading Comprehension 3.3
Critical Thinking 3.1
Learning Strategies 3.1
Writing 3.0
Mathematics 3.0
Active Learning 3.0

Skills in demand

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

Showing the top 40 of 45.

Tools & technology

Example Category
Microsoft Excel Spreadsheet software Hot technology
Microsoft Office software Office suite software Hot technology
Microsoft Outlook Electronic mail software Hot technology
Microsoft PowerPoint Presentation software Hot technology
Microsoft Project Project management software Hot technology
Microsoft Word Word processing software Hot technology
ADP Workforce Now Human resources software
CaterPro Data base user interface and query software
CBORD Foodservice Suite Data base user interface and query software
CBORD Group Menu Management System Inventory management software
Compeat Restaurant Accounting Systems Accounting software
Compris Advanced Manager's Workstation Point of sale POS software
Compris software Point of sale POS software
CostGuard Accounting software
Delphi Technology Financial analysis software
Evernote Word processing software
IBM Domino Communications server software
Intuit QuickBooks Point of Sale Point of sale POS software
MICROS Systems HSI Profits Series Point of sale POS software
Microsoft Dynamics Enterprise resource planning ERP software
Microsoft Publisher Desktop publishing software
NCR Advanced Checkout Solution Point of sale POS software
NCR NeighborhoodPOS Point of sale POS software
Ordering and purchasing software Procurement software
ParTech PixelPoint POS Point of sale POS software
Quizlet Computer based training software
Regnow Chrysanth Inventory Manager Inventory management software
Restaurant Operations & Management Spreadsheet Library Spreadsheet software
Sage 50 Accounting Accounting software
SoftCafe ScheduleWriter Human resources software
Staff scheduling software Calendar and scheduling software
The General Store Point of sale POS software

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.

Telephone Conversations 4.7
Indoors, Environmentally Controlled 4.7
Contact With Others 4.6
Work With or Contribute to a Work Group or Team 4.4
Physical Proximity 4.4
Deal With External Customers or the Public in General 4.1
Importance of Being Exact or Accurate 4.1
Coordinate or Lead Others in Accomplishing Work Activities 3.9
Face-to-Face Discussions with Individuals and Within Teams 3.9
Health and Safety of Other Workers 3.9
Spend Time Standing 3.8
Freedom to Make Decisions 3.7
Spend Time Making Repetitive Motions 3.7
Work Outcomes and Results of Other Workers 3.7
Determine Tasks, Priorities and Goals 3.7
Public Speaking 3.6
Importance of Repeating Same Tasks 3.6
Spend Time Walking or Running 3.5
Dealing With Unpleasant, Angry, or Discourteous People 3.5
Time Pressure 3.4
Frequency of Decision Making 3.3
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 3.2
Conflict Situations 3.2
E-Mail 3.1
Wear Common Protective or Safety Equipment such as Safety Shoes, Glasses, Gloves, Hearing Protection, Hard Hats, or Life Jackets 3.0
Consequence of Error 2.9
Impact of Decisions on Co-workers or Company Results 2.8
Degree of Automation 2.7
Level of Competition 2.7
Exposed to Minor Burns, Cuts, Bites, or Stings 2.6
Spend Time Bending or Twisting Your Body 2.5
Written Letters and Memos 2.0
Dealing with Violent or Physically Aggressive People 2.0
Spend Time Kneeling, Crouching, Stooping, or Crawling 1.9
Spend Time Sitting 1.9
Pace Determined by Speed of Equipment 1.8
Indoors, Not Environmentally Controlled 1.7
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 1.6
Spend Time Keeping or Regaining Balance 1.5
Exposed to Very Hot or Cold Temperatures 1.4

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
High school diploma or equivalent · 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.

What to study: Culinary, Entertainment, and Personal Services , Family and Consumer Sciences/Human Sciences . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.

Education of current workers

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

High School Diploma 69.6%
Less than a High School Diploma 27.1%
Post-Secondary Certificate 1.9%
Some College Courses 1.4%
Associate's Degree (or other 2-year degree) 0.1%

Interests & work styles

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

Career interests (Holland / RIASEC)

Enterprising 6.4
Conventional 4.7
Realistic 4.6
Social 3.4

Interest areas

Management/Administration 5.8
Culinary Art 4.1
Personal Service 3.8
Human Resources 3.6
Business Initiatives 3.0
Accounting 3.0
Teaching/Education 2.2
Finance 2.1

Work styles

Dependability 4.0
Cooperation 3.0
Leadership Orientation 2.5
Social Orientation 2.1

Wages & employment

U.S. · annual wages (BLS OEWS)

$29k10th$35k25th$42kMedian$51k75th$63k90th
Annual wages by percentile — U.S. (BLS OEWS). The light band spans the 10th–90th percentile; the darker band is the middle half (25th–75th); the line is the median.
1.22M20241.29M2034 (proj.)+6.0% · About average
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $29,340
25th percentile $35,400
Median (50th) $42,010
75th percentile $50,920
90th percentile $63,420
People employed 1,187,460

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.

Industry Workers National median pay
Accommodation and Food Services · Sector 1,010,610 $39,520
Full-Service Restaurants · National industry 291,090 $47,100
Retail Trade · Sector 45,240 $45,740
Educational Services · Sector 37,970 $45,070
Health Care and Social Assistance · Sector 35,220 $51,080
Arts, Entertainment, and Recreation · Sector 20,570 $48,530
Manufacturing · Sector 8,410 $47,280
Other Services (except Public Administration) · Sector 6,400 $39,810
Casino Hotels · National industry 4,320 $54,070
Management of Companies and Enterprises · Sector 3,130 $51,350
Information · Sector 2,140 $38,640
Fitness and Recreational Sports Centers · National industry 1,190 $42,880

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).

Industry Concentration Workers
Accommodation and Food Services · Sector 9.22× 1,010,610
Full-Service Restaurants · National industry 7.05× 291,090
Casino Hotels · National industry 1.66× 4,320
Arts, Entertainment, and Recreation · Sector 1.01× 20,570
Theater Companies and Dinner Theaters · National industry 0.84× 470
Retail Trade · Sector 0.38× 45,240
Educational Services · Sector 0.36× 37,970
Residential Mental Health and Substance Abuse Facilities · National industry 0.32× 630

Part of the Hospitality, Events, & Tourism career cluster.

Exposure quadrant: AI task-overlap percentile vs Median pay First-Line Supervisors of Food Preparation and Serving Workers sits at the 47th percentile of AI task-overlap and the 17th percentile of median pay, placed here against 9 adjacent occupations on the same two axes. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay First-Line Supervisors of Food Preparation and Serving Workers First-Line Supervisors of Farming, Fishing, and Forestry Workers First-Line Supervisors of Housekeeping and Janitorial Workers First-Line Supervisors of Mechanics, Installers, and Repairers First-Line Supervisors of Production and Operating Workers First-Line Supervisors of Non-Retail Sales Workers First-Line Supervisors of Office and Administrative Support Workers AI task-overlap percentile → ↑ Median pay
AI task-overlap percentile (horizontal) vs. median-pay percentile (vertical), across all scored occupations. This occupation is highlighted; related occupations are plotted alongside it. Overlap measures shared tasks with AI, not automation.

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 First-Line Supervisors of Food Preparation and Serving Workers — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Write a report on thisheadline · factoids · citation

First-Line Supervisors of Food Preparation and Serving Workers show 47th-percentile AI task overlap — and about 183,900 annual U.S. openings

  • First-Line Supervisors of Food Preparation and Serving Workers rank in the 47th percentile (Moderate band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
  • The occupation is projected to see about 183,900 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
  • BLS projects employment to be about average (+6%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $42,010, across about 1,187,460 U.S. workers.BLS OEWS (May 2024)
  • Of the AI use actually observed for this work, 50% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census.2026-01-15-v4-plus-2025-03-27-v2
Copy the whole kit
First-Line Supervisors of Food Preparation and Serving Workers show 47th-percentile AI task overlap — and about 183,900 annual U.S. openings

• First-Line Supervisors of Food Preparation and Serving Workers rank in the 47th percentile (Moderate band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE)
• The occupation is projected to see about 183,900 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34)
• BLS projects employment to be about average (+6%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $42,010, across about 1,187,460 U.S. workers. (BLS OEWS (May 2024))
• Of the AI use actually observed for this work, 50% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census. (2026-01-15-v4-plus-2025-03-27-v2)

Source: Singulariki — "First-Line Supervisors of Food Preparation and Serving Workers". https://singulariki.com/roles/role-35-1012-00
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.

AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom

Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.

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.

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

Cite this page
Plain

Singulariki. "First-Line Supervisors of Food Preparation and Serving Workers." Singulariki: a source-backed encyclopedia of work. Built from 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. https://singulariki.com/roles/role-35-1012-00

APA

Singulariki. (2026). First-Line Supervisors of Food Preparation and Serving Workers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-35-1012-00

BibTeX
@misc{singulariki-role-35-1012-00,
  title  = {First-Line Supervisors of Food Preparation and Serving Workers},
  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-35-1012-00}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.

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