Often handed to AI
Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.
- Follow recipes to produce food products of specified flavor, texture, clarity, bouquet, or color. · 0.4%
Food Batchmakers
Occupation · SOC 51-3092.00
Set up and operate equipment that mixes or blends ingredients used in the manufacturing of food products. Includes candy makers and cheese makers.
Also called: Batching Operator · Blender · Mixer · Mixer Operator · Brewing Technician · Compounder · Dosier Operator · Dough Scaler · Mix Technician · Syrup Maker · Almond Paste Mixer · Back of House Team Member
Job family: Production Occupations
Take this to your AI
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-51-3092-00/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.
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.
- Formulate or modify recipes for specific kinds of food products. · 0.4%
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.
- Follow recipes to produce food products of specified flavor, texture, clarity, bouquet, or color. · 100.0% need a human
- Formulate or modify recipes for specific kinds of food products. · 94.6% need a human
Skills it runs on
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Market signal
Independent published positions, read together — not a forecast.
- About average employment outlook (+6.9% by 2034)
- 24,200 openings/yr
- Low AI exposure
- Observed AI use leans automation
- Median pay $40,790/yr
- Work is mostly on-site (not teleworkable)
- 2013 flagged this for computerization (58th); today's AI task-overlap is comparatively low (22nd)
22nd-percentile task overlap — yet about 24,200 openings a year (+6.9% projected, BLS), and observed AI use leans 1875% 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 | 38th | -0.4 | |
| LLM task exposure, γ (OpenAI / Eloundou) Low | 17th | 0.1 | |
| AI assistant applicability (Microsoft) Low | 19th | 0.1 |
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 0.1). 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 0.7 · 58th 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.
| Follow recipes to produce food products of specified flavor, texture, clarity, bouquet, or color. | 4.2% | |
| Formulate or modify recipes for specific kinds of food products. | 0.9% | |
| Record production and test data for each food product batch, such as the ingredients used, temperature, test results, and time cycle. | 0.2% | |
| Grade food products according to government regulations or according to type, color, bouquet, and moisture content. | 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 · +6.9% by 2034 |
| Projected annual openings | 24,200 |
| Employment 2024 → 2034 | 173,500 → 185,400 |
“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.
Food Batchmakers sits at the 30th percentile of 427
occupations on the global GenAI task-exposure gradient
— exposure rose from 2023 to 2025. Each dot is one occupation; the
ringed one is this work. Exposure is task overlap, not automation or jobs lost.
19% mean task exposure (2025)
30th percentile of 427 placed occupations
+3 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Dairy Products Makers · 7513 | 22% | Not exposed |
| Food and Related Products Machine Operators · 8160 | 15% | 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 | 18.8% working with AI · 26.3% handed to AI |
| Most common way people use AI here | Directive · AI does it; you give the instruction |
| Typical AI autonomy | 3.5 / 5 · higher = AI acts more independently |
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 |
|---|---|---|
| Follow recipes to produce food products of specified flavor, texture, clarity, bouquet, or color. | Directive | 0.4% |
| Formulate or modify recipes for specific kinds of food products. | Iteration | 0.4% |
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.
| Follow recipes to produce food products of specified flavor, texture, clarity, bouquet, or color. | 100.0% | |
| Formulate or modify recipes for specific kinds of food products. | 94.6% |
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 follow recipes to produce food products of specified flavor, texture, clarity, bouquet, or color.From: Follow recipes to produce food products of specified flavor, texture, clarity, bouquet, or color. · 0.4% of measured AI use · directive
-
Help me formulate or modify recipes for specific kinds of food products.From: Formulate or modify recipes for specific kinds of food products. · 0.4% of measured AI use · task iteration
Tasks
All 25 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Record production and test data for each food product batch, such as the ingredients used, temperature, test results, and time cycle. Core · importance 4.8
- Clean and sterilize vats and factory processing areas. Core · importance 4.7
- Test food product samples for moisture content, acidity level, specific gravity, or butter-fat content, and continue processing until desired levels are reached. Supplemental · importance 4.7
- Set up, operate, and tend equipment that cooks, mixes, blends, or processes ingredients in the manufacturing of food products, according to formulas or recipes. Core · importance 4.7
- Inspect vats after cleaning to ensure that fermentable residue has been removed. Supplemental · importance 4.6
- Mix or blend ingredients, according to recipes, using a paddle or an agitator, or by controlling vats that heat and mix ingredients. Core · importance 4.6
- Follow recipes to produce food products of specified flavor, texture, clarity, bouquet, or color. Core · importance 4.6
- Give directions to other workers who are assisting in the batchmaking process. Core · importance 4.6
- Select and measure or weigh ingredients, using English or metric measures and balance scales. Core · importance 4.5
- Fill processing or cooking containers, such as kettles, rotating cookers, pressure cookers, or vats, with ingredients, by opening valves, by starting pumps or injectors, or by hand. Supplemental · importance 4.5
- Press switches and turn knobs to start, adjust, and regulate equipment, such as beaters, extruders, discharge pipes, and salt pumps. Core · importance 4.5
- Determine mixing sequences, based on knowledge of temperature effects and of the solubility of specific ingredients. Core · importance 4.5
- Observe and listen to equipment to detect possible malfunctions, such as leaks or plugging, and report malfunctions or undesirable tastes to supervisors. Core · importance 4.5
- Manipulate products, by hand or using machines, to separate, spread, knead, spin, cast, cut, pull, or roll products. Supplemental · importance 4.5
- Observe gauges and thermometers to determine if the mixing chamber temperature is within specified limits, and turn valves to control the temperature. Core · importance 4.5
- Cool food product batches on slabs or in water-cooled kettles. Supplemental · importance 4.5
- Turn valve controls to start equipment and to adjust operation to maintain product quality. Core · importance 4.4
- Modify cooking and forming operations based on the results of sampling processes, adjusting time cycles and ingredients to achieve desired qualities, such as firmness or texture. Core · importance 4.4
- Place products on carts or conveyors to transfer them to the next stage of processing. Supplemental · importance 4.3
- Grade food products according to government regulations or according to type, color, bouquet, and moisture content. Supplemental · importance 4.3
- Homogenize or pasteurize material to prevent separation or to obtain prescribed butterfat content, using a homogenizing device. Supplemental · importance 4.3
- Operate refining machines to reduce the particle size of cooked batches. Supplemental · importance 4.3
- Examine, feel, and taste product samples during production to evaluate quality, color, texture, flavor, and bouquet, and document the results. Core · importance 4.3
- Formulate or modify recipes for specific kinds of food products. Supplemental · importance 4.3
- Inspect and pack the final product. Supplemental · importance 4.2
Work activities
- Record operational or production data.
- Monitor instruments to ensure proper production conditions.
- Adjust temperature controls of ovens or other heating equipment.
- Clean work areas.
- Sterilize food cooking or processing equipment.
- Operate mixing equipment.
- Watch operating equipment to detect malfunctions.
- Notify others of equipment repair or maintenance needs.
- Operate cooking, baking, or other food preparation equipment.
- Measure ingredients or substances to be used in production processes.
- Select production input materials.
- Determine food production methods.
- Load materials into production equipment.
- Operate pumping systems or equipment.
- Direct operational or production activities.
- Inspect production equipment.
- Inspect food products.
- Evaluate quality of food ingredients or prepared foods.
- Package products for storage or shipment.
- Move products, materials, or equipment between work areas.
- Shape clay or dough to create products.
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Knowledge
Abilities
| Information Ordering | 3.6 | |
| Near Vision | 3.6 | |
| Oral Comprehension | 3.3 | |
| Written Comprehension | 3.1 | |
| Problem Sensitivity | 3.1 | |
| Control Precision | 3.1 | |
| Oral Expression | 3.0 | |
| Category Flexibility | 3.0 | |
| Perceptual Speed | 3.0 | |
| Selective Attention | 3.0 | |
| Manual Dexterity | 3.0 | |
| Auditory Attention | 3.0 | |
| Speech Recognition | 3.0 | |
| Deductive Reasoning | 2.9 | |
| Inductive Reasoning | 2.9 | |
| Arm-Hand Steadiness | 2.9 | |
| Finger Dexterity | 2.9 | |
| Trunk Strength | 2.9 |
Transferable skills
| Operations Monitoring | 3.3 | |
| Coordination | 3.0 | |
| Operation and Control | 3.0 | |
| Complex Problem Solving | 2.9 | |
| Judgment and Decision Making | 2.9 |
Essential skills
| Reading Comprehension | 3.1 | |
| Critical Thinking | 3.1 | |
| Active Listening | 3.0 | |
| Speaking | 3.0 | |
| Writing | 2.9 | |
| Monitoring | 2.9 |
Skills in demand
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Tools & technology
| Example | Category | |
|---|---|---|
| Microsoft Office software | Office suite software | Hot technology |
| Edible Software | Inventory management software | |
| Plex Systems Plex Manufacturing Cloud | Enterprise resource planning ERP 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.
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: Agriculture, Agriculture Operations, and Related 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 | 37.8% | |
| Some College Courses | 26.7% | |
| Post-Secondary Certificate | 21.5% | |
| Less than a High School Diploma | 13.9% |
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Career interests (Holland / RIASEC)
| Realistic | 6.1 | |
| Conventional | 4.7 | |
| Enterprising | 2.6 | |
| Investigative | 2.1 | |
| Artistic | 1.9 |
Interest areas
| Physical/Manual Labor | 3.7 | |
| Culinary Art | 3.0 | |
| Mechanics/Electronics | 2.8 | |
| Engineering | 2.2 | |
| Mathematics/Statistics | 1.8 | |
| Transportation/Machine Operation | 1.8 | |
| Physical Science | 1.7 | |
| Management/Administration | 1.7 |
Work styles
| Dependability | 2.6 | |
| Attention to Detail | 2.4 | |
| Cautiousness | 1.8 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $30,850 |
| 25th percentile | $35,340 |
| Median (50th) | $40,790 |
| 75th percentile | $49,010 |
| 90th percentile | $57,800 |
| People employed | 171,660 |
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 |
|---|---|---|
| Manufacturing · Sector | 145,060 | $43,980 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 8,910 | $35,230 |
| Temporary Help Services · National industry | 8,740 | $35,220 |
| Accommodation and Food Services · Sector | 7,130 | $33,360 |
| Wholesale Trade · Sector | 5,320 | $38,170 |
| Retail Trade · Sector | 4,110 | $36,350 |
| Full-Service Restaurants · National industry | 620 | $40,280 |
| Transportation and Warehousing · Sector | 600 | $38,810 |
| Health Care and Social Assistance · Sector | 130 | $37,930 |
| Agriculture, Forestry, Fishing and Hunting · Sector | — | $35,110 |
| Management of Companies and Enterprises · Sector | — | $29,600 |
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 |
|---|---|---|
| Manufacturing · Sector | 10.21× | 145,060 |
| Temporary Help Services · National industry | 2.96× | 8,740 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 0.89× | 8,910 |
| Wholesale Trade · Sector | 0.79× | 5,320 |
| Accommodation and Food Services · Sector | 0.45× | 7,130 |
| Retail Trade · Sector | 0.24× | 4,110 |
| Full-Service Restaurants · National industry | 0.1× | 620 |
| Transportation and Warehousing · Sector | 0.07× | 600 |
Related occupations
Part of the Advanced Manufacturing career cluster.
- Food Cooking Machine Operators and Tenders · 51-3093.00 Compare →
- Food and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders · 51-3091.00 Compare →
- Mixing and Blending Machine Setters, Operators, and Tenders · 51-9023.00 Compare →
- Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders · 51-9012.00 Compare →
- Bakers · 51-3011.00 Compare →
- Graders and Sorters, Agricultural Products · 45-2041.00 Compare →
- Cooling and Freezing Equipment Operators and Tenders · 51-9193.00 Compare →
- Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders · 51-9192.00 Compare →
- Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders · 51-9021.00 Compare →
- Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders · 51-9041.00 Compare →
- Packaging and Filling Machine Operators and Tenders · 51-9111.00 Compare →
- Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders · 51-9051.00 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 Food Batchmakers — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Skills that travel
Capabilities this work builds that are used across many other occupations.
Adjacent roles
Occupations O*NET rates as related — the nearby moves on the map.
- Food Cooking Machine Operators and Tenders Compare →
- Food and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders Compare →
- Mixing and Blending Machine Setters, Operators, and Tenders Compare →
- Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders Compare →
- Bakers Compare →
Paths in
How people typically prepare for this work.
- High school diploma or equivalent · typical entry level
- Job zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed · preparation tier
- Agriculture, Agriculture Operations, and Related Sciences · field of study
Zoom out
On the global GenAI exposure gradient this work sits around the 30th percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Food Batchmakers show 22nd-percentile AI task overlap — and about 24,200 annual U.S. openings
- Food Batchmakers rank in the 22nd percentile (Low 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 24,200 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 (+6.9%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $40,790, across about 171,660 U.S. workers.BLS OEWS (May 2024)
- Of the AI use actually observed for this work, 19% 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
Food Batchmakers show 22nd-percentile AI task overlap — and about 24,200 annual U.S. openings • Food Batchmakers rank in the 22nd percentile (Low 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 24,200 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 (+6.9%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $40,790, across about 171,660 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 19% 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 — "Food Batchmakers". https://singulariki.com/roles/role-51-3092-00 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.
- 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.
Cite this page
Plain
Singulariki. "Food Batchmakers." 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-51-3092-00
APA
Singulariki. (2026). Food Batchmakers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-51-3092-00
BibTeX
@misc{singulariki-role-51-3092-00,
title = {Food Batchmakers},
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-51-3092-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.