US dataswitch to UK

Food and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders

observing feel, observing flow of materials and listening for machine malfunctions, clearing or dislodging blockages in bins and signaling coworkers to synchronize flow of materials. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: observing feel, taste or otherwise examine products during and after processing to ensure conformance to standards 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: 14%
changing shape: 0%
staying human: 86%

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

Whole-job exposure score 11 out of 100 (9–15 allowing for uncertainty): minimal exposure, across 19 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 and tobacco roasting, baking, and drying machine operators and tenders 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 2026-08-05. Read the full method.

What shifts is recording production data, such as weight and amount of product. This page scores what today's tools actually do, not headlines.

Your week, as this page understands it

Operate or tend food or tobacco roasting, baking, or drying equipment, including hearth ovens, kiln driers, roasters, char kilns, and vacuum drying equipment. The job title says “food”, “tobacco roasting”, “baking”, “drying machine operators” or “tenders”: officially one job, several names. The real job is the part underneath: observing feel, taste or otherwise examine products during and after processing to ensure conformance to standards. 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 and tobacco roasting, baking, and drying machine operators and tenders is not one task. It is 19 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is observing feel, taste or otherwise examine products during and after processing to ensure conformance to standards, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 14%
changing shape: 0%
staying human: 86%

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 2026-08-05. 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.

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

2 tasks

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 data, such as weight and amount of product
This is reading one thing and writing another: production data in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Record production data, such as weight and amount of product processed, type of product, and time and temperature of processing.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (65–73 allowing for uncertainty): high exposure, high 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: Logging weights, times and temperatures is routine record-keeping that sensors and software already capture.
The five ratings: output a model can produce 4/4 · 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.
Reading work orders to determine quantities and types of products
This is reading one thing and writing another: work orders in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Read work orders to determine quantities and types of products to be baked, dried, or roasted.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (65–73 allowing for uncertainty): high exposure, high 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: Reading a work order to pull out quantities and product types is simple structured information handling.
The five ratings: output a model can produce 4/4 · 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.

Changing shape

0 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.

Nothing in this job’s scored task list landed in this group. That is the measurement, not an editorial choice, and it is worth knowing either way.

Staying human

17 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.

Observing feel, taste or otherwise examine products during and after processing to ensure conformance to standards
This work happens in the physical world: feel, taste or otherwise examine products, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Observe, feel, taste, or otherwise examine products during and after processing to ensure conformance to standards.” (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: Judging a product by feel, taste and smell needs a person right 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 1/4.
Observing flow of materials and listening for machine malfunctions
This work happens in the physical world: flow of materials, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Observe flow of materials and listen for machine malfunctions, such as jamming or spillage, and notify supervisors if corrective actions fail.” (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: Spotting a jam by sound and sight means standing at 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 1/4.
Observing temperature, humidity, pressure gauges and product samples and adjusting controls
This work happens in the physical world: temperature, humidity, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Observe temperature, humidity, pressure gauges, and product samples and adjust controls, such as thermostats and valves, to maintain prescribed operating conditions for specific stages.” (O*NET task statement)
How this row was scored
Exposure score: 14 out of 100 (7–21 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: Modern controls log much of this, but reading gauges and adjusting valves happens at the machine.
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 3/4.
Setting temperature and time controls
This work happens in the physical world: temperature, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Set temperature and time controls, light ovens, burners, driers, or roasters, and start equipment, such as conveyors, cylinders, blowers, driers, or 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: work that happens in the physical world.
The rating behind it: Lighting ovens and burners and starting equipment is hands-on machine 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.
Weighing or measuring products, using scale hoppers or scale conveyors
This work happens in the physical world: products, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Weigh or measure products, using scale hoppers or scale conveyors.” (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: Weighing product on scale hoppers and conveyors is physical handling.
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.
Signaling coworkers to synchronize flow of materials
This work happens in the physical world: coworkers, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Signal coworkers to synchronize flow of materials.” (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 value is that a specific person does it.
The rating behind it: Signaling coworkers to keep material flowing means being on the floor with them.
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 2/4 · how much data exists 1/4.
Operating or tending equipment that roasts
This work happens in the physical world: equipment, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Operate or tend equipment that roasts, bakes, dries, or cures food items such as cocoa and coffee beans, grains, nuts, and bakery 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: Operating roasting and drying equipment is hands-on machine tending.
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 1/4.
Filling or removing product from trays
This work happens in the physical world: product, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Fill or remove product from trays, carts, hoppers, or equipment, using scoops, peels, or shovels, 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: work that happens in the physical world.
The rating behind it: Filling and emptying trays and hoppers with scoops and shovels 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 1/4.

Show the other 9 tasks

Taking product samples during or after processing for laboratory analyses
staying human
This work happens in the physical world: product samples, in a real place. Software cannot follow it there.
importance 5 · Supplemental
Source: “Take product samples during or after processing for laboratory analyses.” (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: Taking physical samples off the line requires hands on the product.
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 1/4.
Opening valves, gates or chutes or using shovels to load or remove products from ovens or other equipment
staying human
This work happens in the physical world: valves, gates or chutes, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Open valves, gates, or chutes or use shovels to load or remove products from ovens or other equipment.” (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: Opening valves and shoveling product in and out is manual handling.
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 1/4.
Cleaning equipment with steam, hot water and hoses
staying human
This work happens in the physical world: equipment, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Clean equipment with steam, hot water, and hoses.” (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: Cleaning equipment with steam, hot water and hoses 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 1/4.
Clearing or dislodging blockages in bins
staying human
This work happens in the physical world: blockages, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Clear or dislodge blockages in bins, screens, or other equipment, using poles, brushes, or mallets.” (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: Clearing blockages with poles, brushes and mallets 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 1/4.
Pushing racks or carts to transfer products
staying human
This work happens in the physical world: racks, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Push racks or carts to transfer products to storage, cooling stations, or 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: Pushing racks and carts between stations is physical movement of goods.
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 1/4.
Starting conveyors to move roasted grain to cooling pans and agitate grain with rakes as blowers force air through perforated bottoms of pans
staying human
This work happens in the physical world: conveyors, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Start conveyors to move roasted grain to cooling pans and agitate grain with rakes as blowers force air through perforated bottoms of pans.” (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: Raking grain and running conveyors is hands-on work 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 1/4.
Smoothing out products in bins
staying human
This work happens in the physical world: products, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Smooth out products in bins, pans, trays, or conveyors, using rakes or shovels.” (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: Smoothing product with rakes and shovels 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 1/4.
Installing equipment, such as spray units, cutting blades or screens, using hand tools
staying human
This work happens in the physical world: equipment, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Install equipment, such as spray units, cutting blades, or screens, using hand tools.” (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: Fitting spray units, blades and screens with hand tools is physical installation.
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 1/4.
Testing products for moisture content
staying human
This work happens in the physical world: products, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Test products for moisture content, using moisture meters.” (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: Using a moisture meter on product means handling both the meter and the product.
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: $44,810a 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: 20,370in 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 data in, a record out. The rows above are exactly that shape: recording production data and reading work orders to determine quantities and types of products. What it cannot do is be there in the room, and that is still where feel, taste or otherwise examine products 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: observing feel, taste or otherwise examine products during and after processing to ensure conformance to standards is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 14% of this job's task weight sits in rows the software is already learning, 0% in rows that change shape rather than disappear, and 86% in rows it is nowhere near. That is the position, measured across 19 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 data 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 data, 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 feel, taste or otherwise examine products 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 observing feel, taste or otherwise examine products during and after processing to ensure conformance to standards. 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 and tobacco roasting, baking, and drying machine operators and tenders (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was food batchmakers: only about 7% of its durable work is work you already do. And on the numbers you do not need one. This job scores 11/100 here, with only 14% of the task list in the top band, and “observe, feel, taste, or otherwise examine products during and after processing to…” 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 Batchmakers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already push racks or carts to transfer products to storage, cooling stations, or the…, and their equivalent is to place products on carts or conveyors to transfer them to the next stage…. Across both published task lists that is about 7% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 7% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
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 observe flow of materials and listen for machine malfunctions, and their equivalent is to listen for malfunction alarms, and shut down equipment and notify supervisors when necessary. Across both published task lists that is about 7% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 7% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
Crushing, Grinding, and Polishing 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 test products for moisture content, using moisture meters, and their equivalent is to turn valves to regulate the moisture contents of materials. 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.
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: 14% of its task weight, across 19 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 observing feel, taste or otherwise examine products during and after processing to ensure conformance to standards 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 Fishmongers and poultry dressers 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

The other groups this work is counted across:

In UK official statistics this job is counted as Fishmongers and poultry dressers, Butchers and Food, drink and tobacco process operatives. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.

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:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
American Job Center locator
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
- CareerOneStop local help hub
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
CareerOneStop
Free

No Space for this job, but one for what is happening to it

Nothing Collab365 runs is built for food / tobacco roasting / baking / drying machine operators / tenders, 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 14% 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 / tobacco roasting / baking / drying machine operators / tenders. 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 / tobacco roasting / baking / drying machine operators / tenders launches. Nothing else.

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No Space for food / tobacco roasting / baking / drying machine operators / tenders 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 / tobacco roasting / baking / drying machine operators / tenders existed, with researched problems, courses and people in the same boat, would you want in?

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We use your email address for one thing: to tell you if a Space for food / tobacco roasting / baking / drying machine operators / tenders 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 and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders?: Not as a job, but it is already doing parts of the work. Across the 19 official task statements scored for Food and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders (United States, SOC 51-3091), 14% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 11 out of 100 (range 9–15, 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 and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Record production data, such as weight and amount of product processed, type of product, and time and temperature of processing” (69/100, high); “Read work orders to determine quantities and types of products to be baked, dried, or roasted” (69/100, high); “Observe temperature, humidity, pressure gauges, and product samples and adjust controls, such as thermostats and valves, to maintain prescribed operating con…” (14/100, minimal). 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 and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders” stay human?: About 86% 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: “Signal coworkers to synchronize flow of materials” (0/100, minimal); “Test products for moisture content, using moisture meters” (0/100, minimal); “Install equipment, such as spray units, cutting blades, or screens, using hand tools” (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 and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders” do about AI?: Start from the ledger rather than the headline: 14% of this job's weighted core work is exposed, and roughly 86% 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 and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders 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 19 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.
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 2026-08-05.
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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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.