Slaughterers and Meat Packers

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

• Remove bones, and cut meat into standard cuts in preparation for marketing. Supplemental · importance 4.7
• Sever jugular veins to drain blood and facilitate slaughtering. Supplemental · importance 4.6
• Tend assembly lines, performing a few of the many cuts needed to process a carcass. Supplemental · importance 4.5
• Shackle hind legs of animals to raise them for slaughtering or skinning. Supplemental · importance 4.5
• Slit open, eviscerate, and trim carcasses of slaughtered animals. Supplemental · importance 4.5
• Stun animals prior to slaughtering. Supplemental · importance 4.4
• Skin sections of animals or whole animals. Supplemental · importance 4.4
• Cut, trim, skin, sort, and wash viscera of slaughtered animals to separate edible portions from offal. Supplemental · importance 4.3
• Shave or singe and defeather carcasses, and wash them in preparation for further processing or packaging. Supplemental · importance 4.3
• Saw, split, or scribe carcasses into smaller portions to facilitate handling. Supplemental · importance 4.3
• Trim head meat, and sever or remove parts of animals' heads or skulls. Supplemental · importance 4.3
• Grind meat into hamburger, and into trimmings used to prepare sausages, luncheon meats, and other meat products. Supplemental · importance 4.2
• Trim, clean, or cure animal hides. Supplemental · importance 4.2
• Wrap dressed carcasses or meat cuts. Supplemental · importance 4.0
• Slaughter animals in accordance with religious law, and determine that carcasses meet specified religious standards. Supplemental

Work activities

• Cut meat products.
• Clean materials to prepare them for production.
• Process animal carcasses.
• Slaughter animals.
• Prepare meat products for sale or consumption.

Knowledge, skills & abilities

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

Abilities

Knowledge

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.

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

What to study: Culinary, Entertainment, and Personal Services . 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.

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 Agriculture career cluster.

• Meat, Poultry, and Fish Cutters and Trimmers · 51-3022.00 Compare →
• Butchers and Meat Cutters · 51-3021.00 Compare →
• Farmworkers, Farm, Ranch, and Aquacultural Animals · 45-2093.00 Compare →
• Graders and Sorters, Agricultural Products · 45-2041.00 Compare →
• Food Preparation Workers · 35-2021.00 Compare →
• Animal Breeders · 45-2021.00 Compare →
• Packers and Packagers, Hand · 53-7064.00 Compare →
• Cooks, Restaurant · 35-2014.00 Compare →
• Food and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders · 51-3091.00 Compare →
• Farmworkers and Laborers, Crop, Nursery, and Greenhouse · 45-2092.00 Compare →
• Animal Scientists · 19-1011.00 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 Slaughterers and Meat Packers — 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-51-3023-00,
  title  = {Slaughterers and Meat Packers},
  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-51-3023-00}
}