Will AI replace Food Batchmakers?

Not as a job, but it is already doing parts of the work. Across the 25 official task statements scored for Food Batchmakers (United States, SOC 51-3092), 5% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 9 out of 100 (range 7–14, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.

Which tasks in “Food Batchmakers” stay human?

About 87% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Manipulate products, by hand or using machines, to separate, spread, knead, spin, cast, cut, pull, or roll products” (0/100, minimal); “Place products on carts or conveyors to transfer them to the next stage of processing” (0/100, minimal); “Operate refining machines to reduce the particle size of cooked batches” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.

What should someone working in “Food Batchmakers” do about AI?

Start from the ledger rather than the headline: 5% of this job's weighted core work is exposed, and roughly 87% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.

How is the AI exposure score for Food Batchmakers calculated?

Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 25 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

US dataswitch to UK

Food Batchmakers

recording production and testing data for each food product batch, selecting and measuring or weighing ingredients and pressing switches and turn knobs. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: setting up, operate and tend equipment that cooks, mixes, blends or processes ingredients in the manufacturing of food products is work software can't reach.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 5%
changing shape: 8%
staying human: 87%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 9 out of 100 (7–14 allowing for uncertainty): minimal exposure, across 25 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.

How we know this

What is measured: Every published task statement for food batchmakers is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.

What shifts is recording production and testing data for each food product batch: the paper around the work, not the work.

Your week, as this page understands it

Set up and operate equipment that mixes or blends ingredients used in the manufacturing of food products. Includes candy makers and cheese makers. The job title says “food batchmakers”. The real job is the part underneath: setting up, operate and tend equipment that cooks, mixes, blends or processes ingredients in the manufacturing of food products. That is the thing someone has to be right about.

The exposed part of this job is specific, and we won’t pretend it is coming back. But food batchmakers is not one task. It is 25 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is setting up, operate and tend equipment that cooks, mixes, blends or processes ingredients in the manufacturing of food products, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 5%
changing shape: 8%
staying human: 87%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

How we know this

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.

Your job, task by task

These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.

1 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Shifting to AI

1 task

Tasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.

Recording production and testing data for each food product batch
This is reading one thing and writing another: production in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Record production and test data for each food product batch, such as the ingredients used, temperature, test results, and time cycle.” (O*NET task statement)
How this row was scored
Exposure score: 61 out of 100 (54–68 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Batch records are structured data entry, much of it already logged automatically.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Changing shape

2 tasks

Tasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.

Determining mixing sequences, based on knowledge of temperature effects and of the solubility of specific ingredients
The software now makes the first pass at sequences, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · Core
Source: “Determine mixing sequences, based on knowledge of temperature effects and of the solubility of specific ingredients.” (O*NET task statement)
How this row was scored
Exposure score: 58 out of 100 (51–65 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: The science is documented, but the right sequence for this plant's product needs testing on the line.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Formulating or modifying recipes for specific kinds of food products
The software now makes the first pass at recipes, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Supplemental
Source: “Formulate or modify recipes for specific kinds of food products.” (O*NET task statement)
How this row was scored
Exposure score: 43 out of 100 (36–50 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Software suggests credible formulations, though every change has to be trialled and tasted.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Staying human

22 tasks

Tasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.

Setting up, operate and tend equipment that cooks, mixes, blends or processes ingredients in the manufacturing of food products
This work happens in the physical world: up, operate and tend equipment, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Set up, operate, and tend equipment that cooks, mixes, blends, or processes ingredients in the manufacturing of food products, according to formulas or recipes.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Running the cooking and mixing equipment means standing at it.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Cleaning and sterilizing vats and factory processing areas
This work happens in the physical world: vats, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Clean and sterilize vats and factory processing areas.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Cleaning tanks and processing areas is manual work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Following recipes to produce food products of specified flavor
This work happens in the physical world: recipes, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Follow recipes to produce food products of specified flavor, texture, clarity, bouquet, or color.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Making the product is physical production work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Observing and listening to equipment to detect possible malfunctions
This work happens in the physical world: equipment, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Observe and listen to equipment to detect possible malfunctions, such as leaks or plugging, and report malfunctions or undesirable tastes to supervisors.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Hearing a leak or tasting something off depends on being there.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Mixing or blending ingredients
This work happens in the physical world: ingredients, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Mix or blend ingredients, according to recipes, using a paddle or an agitator, or by controlling vats that heat and mix ingredients.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Mixing and blending is done at the vat.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Pressing switches and turn knobs
This work happens in the physical world: switches, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Press switches and turn knobs to start, adjust, and regulate equipment, such as beaters, extruders, discharge pipes, and salt pumps.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Operating the switches and knobs needs someone at the machine.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Observing gauges and thermometers to determine if the mixing chamber temperature is within specified limits
This work happens in the physical world: gauges, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Observe gauges and thermometers to determine if the mixing chamber temperature is within specified limits, and turn valves to control the temperature.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Reading gauges and turning valves is done at the equipment.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Show the other 15 tasks

Grading food products according to government regulations
staying human
This work happens in the physical world: food products, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Grade food products according to government regulations or according to type, color, bouquet, and moisture content.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (6–20 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Grading rules are written down, but the judgment is made on the product in front of you.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Modifying cooking and forming operations based on the results of sampling processes
staying human
This work happens in the physical world: operations, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “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.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (6–20 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: The adjustment follows documented rules, but the sampling and the controls are physical.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Testing food product samples for moisture content
staying human
This work happens in the physical world: food product samples, in a real place. Software cannot follow it there.
importance 5 · Supplemental
Source: “Test food product samples for moisture content, acidity level, specific gravity, or butter-fat content, and continue processing until desired levels are reached.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (3–17 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Samples are handled and tested by hand, even though the numbers are simple to interpret.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Giving directions to other workers who are assisting in the batchmaking process
staying human
This work happens in the physical world: directions, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Give directions to other workers who are assisting in the batchmaking process.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (0–13 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Directing helpers during a batch happens on the production floor.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Selecting and measuring or weighing ingredients
staying human
This work happens in the physical world: ingredients, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Select and measure or weigh ingredients, using English or metric measures and balance scales.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Weighing and handling ingredients is hands-on.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Turning valve controls to start equipment and to adjust operation to maintain product quality
staying human
This work happens in the physical world: valve controls, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Turn valve controls to start equipment and to adjust operation to maintain product quality.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Valves are turned by hand.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Filling processing or cooking containers
staying human
This work happens in the physical world: containers, in a real place. Software cannot follow it there.
importance 5 · Supplemental
Source: “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.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Filling kettles and vats is physical work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Homogenizing or pasteurizing material to prevent separation or to obtain prescribed butterfat content
staying human
This work happens in the physical world: material, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Homogenize or pasteurize material to prevent separation or to obtain prescribed butterfat content, using a homogenizing device.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Running the homogenizer is work at the machine.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Inspecting vats after cleaning to ensure that fermentable residue
staying human
This work happens in the physical world: vats, in a real place. Software cannot follow it there.
importance 5 · Supplemental
Source: “Inspect vats after cleaning to ensure that fermentable residue has been removed.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Checking inside a vat means climbing to it and looking.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Examining feel and taste product samples during production to evaluate quality, color, texture, flavor and bouquet and document the results
staying human
This work happens in the physical world: feel, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Examine, feel, and taste product samples during production to evaluate quality, color, texture, flavor, and bouquet, and document the results.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Tasting and feeling the product is something only a person who is present can do.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Inspecting and packing the final product
staying human
This work happens in the physical world: the final product, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Inspect and pack the final product.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Inspecting and packing is done by hand on the line.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Cooling food product batches on slabs or in water-cooled kettles
staying human
This work happens in the physical world: food product batches, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Cool food product batches on slabs or in water-cooled kettles.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Moving and cooling batches is physical.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Operating refining machines to reduce the particle size of cooked batches
staying human
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Operate refining machines to reduce the particle size of cooked batches.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Operating the refiner requires someone at the controls.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Placing products on carts or conveyors to transfer them to the next stage of processing
staying human
This work happens in the physical world: products, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Place products on carts or conveyors to transfer them to the next stage of processing.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Moving product between stages is lifting and carrying.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Manipulating products, by hand or using machines, to separate, spread, knead, spin, cast, cut, pull or roll products
staying human
This work happens in the physical world: products, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Manipulate products, by hand or using machines, to separate, spread, knead, spin, cast, cut, pull, or roll products.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Kneading, cutting and rolling product is manual work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.

What this job pays, and how many people do it

Median pay: $42,290a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
People doing this job: 174,520in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)

What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.

Why this is shifting

The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: production in, a record out. The rows above are exactly that shape: recording production and testing data for each food product batch and determining mixing sequences. What it cannot do is be there in the room, and that is still where up, operate and tend equipment get done. Which is why this page talks about your tasks changing, not your job ending.

Your move

Over a pint: what I’d tell you if you were my friend

Start with what does not change: setting up, operate and tend equipment that cooks, mixes, blends or processes ingredients in the manufacturing of food products is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 5% of this job's task weight sits in rows the software is already learning, 8% in rows that change shape rather than disappear, and 87% in rows it is nowhere near. That is the position, measured across 25 scored tasks. It is not a forecast about you.

So the thing worth your attention is not the job going away. It is the layer around it. Recording production and testing data for each food product batch is the part turning into software, and being the person who understands that layer is worth money.

This week: one thing

Ask the one question. Find whoever is bringing new software into your workplace (the manager, the office, whoever runs the system) and ask them what it is meant to do to production, and what it is not meant to touch. Ten minutes, this week, before anyone decides it for you.

What you end up holding: a straight answer about what is actually being rolled out, and when
How long it takes: ten minutes

If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether up, operate and tend equipment are in scope or not. Nothing to log into, no license needed.

Over the next 90 days

Get inside the tool rollout rather than waiting for it. Over the next ninety days, ask to be in the group that tests, checks or signs off whatever new system arrives near determining mixing sequences, based on knowledge of temperature effects and of the solubility of specific ingredients. It is usually an unglamorous seat that nobody fights for, and it is the one that decides how the software is used on your job rather than to it.

Over the next 12 months

On this evidence I would not retrain out of this job, and I will say that plainly rather than hedge it. The task list here is dominated by work that stays with a person. What I would do with a year is get formally recognised for the layer around it (the systems, the compliance, the planning), so you are the one who understands the software instead of the one it is done to. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.

The roads out of here, and why I am not sending you down them

I looked at the obvious moves out of this job, and here is what I found.

I checked the 12 nearest US occupations to food batchmakers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was food cooking machine operators and tenders: only about 15% of its durable work is work you already do and there are far fewer of those jobs than of yours. And on the numbers you do not need one. This job scores 9/100 here, with only 5% of the task list in the top band, and “set up, operate, and tend equipment that cooks, mixes, blends, or processes…” is not work that hands over cleanly. None of them beats deepening what you already have.

How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.

3 moves I checked and rejected

These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.

Food Cooking Machine Operators and Tenders
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already select and measure or weigh ingredients, using English or metric measures and balance…, and their equivalent is to measure or weigh ingredients, using scales or measuring containers. Across both published task lists that is about 15% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 15% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on. And it is a narrow door: about 31,250 of those jobs against 174,520 of yours (OEWS May 2025), 18% as many seats.
Look at that job’s page anyway →
Mixing and Blending Machine Setters, Operators, and Tenders
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already mix or blend ingredients, according to recipes, using a paddle or an agitator…, and their equivalent is to mix or blend ingredients by starting machines and mixing for specified times. Across both published task lists that is about 8% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 8% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
Food Science Technicians
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already set up, operate, and tend equipment that cooks, mixes, blends, or processes ingredients…, and their equivalent is to mix, blend, or cultivate ingredients to make reagents or to manufacture food or…. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step. And it is a narrow door: about 14,600 of those jobs against 174,520 of yours (OEWS May 2025), 8% as many seats.
Look at that job’s page anyway →

What I’d stop worrying about

A friend tells you what not to spend fear on. This is that list.

The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 5% of its task weight, across 25 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The headlines about your trade disappearing
They are usually about the technology, not the timetable. Changes to work like setting up, operate and tend equipment that cooks, mixes, blends or processes ingredients in the manufacturing of food products arrive through rules, insurance and money, slowly and visibly. This page tracks the task evidence and will move when it moves.
Retraining out of a job that is holding up
On this evidence I would not spend money leaving. Spend it on the layer around the job instead: the tools, the paperwork, the planning. That is where the change actually is.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Food, drink and tobacco process operatives is the closest. The pay and employment figures are not directly comparable, and we do not average them together.

Switch to the United Kingdom page →partial match

In UK official statistics this job is counted as Food, drink and tobacco process operatives. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.

Your route through this

Where to go next, and what it costs

Free, and complete

The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.

Anywhere in the US:
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No Space for this job, but one for what is happening to it

Nothing Collab365 runs is built for food batchmakers, and we are not going to point you at the nearest one and call it a fit.

There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 5% of the work on this page is already inside what they can do.

Try The AI Authority free →

7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.

The AI Authority is a general community about working with AI, not a course for food batchmakers. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.

Noted, and thank you. We’ll email you if a Space for food batchmakers launches. Nothing else.

That did not look like an email address, so nothing was saved. Have another go below.

We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.

No Space for food batchmakers yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

Collab365 launches new communities where the need is real. If one for food batchmakers existed, with researched problems, courses and people in the same boat, would you want in?

Your email

We use your email address for one thing: to tell you if a Space for food batchmakers launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.

Questions people ask about this job

Will AI replace Food Batchmakers?: Not as a job, but it is already doing parts of the work. Across the 25 official task statements scored for Food Batchmakers (United States, SOC 51-3092), 5% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 9 out of 100 (range 7–14, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
Which tasks in “Food Batchmakers” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Record production and test data for each food product batch, such as the ingredients used, temperature, test results, and time cycle” (61/100, high); “Determine mixing sequences, based on knowledge of temperature effects and of the solubility of specific ingredients” (58/100, partial); “Formulate or modify recipes for specific kinds of food products” (43/100, partial). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Food Batchmakers” stay human?: About 87% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Manipulate products, by hand or using machines, to separate, spread, knead, spin, cast, cut, pull, or roll products” (0/100, minimal); “Place products on carts or conveyors to transfer them to the next stage of processing” (0/100, minimal); “Operate refining machines to reduce the particle size of cooked batches” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
What should someone working in “Food Batchmakers” do about AI?: Start from the ledger rather than the headline: 5% of this job's weighted core work is exposed, and roughly 87% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
How is the AI exposure score for Food Batchmakers calculated?: Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 25 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

Worth knowing about these figures

The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
1 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Task statements: onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights: onet-db (im-rt)
Scores: Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-04.
Pay and employment: bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.

The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.

The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.

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Using these figures?

Cite this

Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.

Plain text

Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).

BibTeX

@misc{collab365futureproof2026q41,
  title        = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
  author       = {{Collab365}},
  year         = {2026},
  url          = {https://futureproof.collab365.com/data/2026-q4.1},
  note         = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published 2026-08-05. Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.