Will AI replace Food Science Technicians?

Not as a job, but it is already doing parts of the work. Across the 16 official task statements scored for Food Science Technicians (United States, SOC 19-4013), 32% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 29 out of 100 (range 25–34, band: low). 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 Science Technicians” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Record or compile test results or prepare graphs, charts, or reports” (88/100, very high); “Compute moisture or salt content, percentages of ingredients, formulas, or other product factors, using mathematical and chemical procedures” (81/100, very high); “Analyze test results to classify products or compare results with standard tables” (72/100, high). 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 Science Technicians” stay human?

About 68% 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: “Prepare or incubate slides with cell cultures” (0/100, minimal); “Perform regular maintenance of laboratory equipment by inspecting, calibrating, cleaning, or sterilizing” (0/100, minimal); “Measure, test, or weigh bottles, cans, or other containers to ensure that hardness, strength, or dimensions meet specifications” (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 Science Technicians” do about AI?

Start from the ledger rather than the headline: 32% of this job's weighted core work is exposed, and roughly 68% 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 Science Technicians 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 16 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 Science Technicians

tasting or smelling foods or beverages to ensure that flavors meet specifications or to select samples with specific characteristics, mixing blend and training newly hired laboratory personnel. If that's your week, this page is about your job.

The honest answer

AI is already taking a real slice of the routine work here: recording or compiling test results or preparing graphs. That is a slice of tasks, not of you.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 32%
changing shape: 0%
staying human: 68%

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

Whole-job exposure score 29 out of 100 (25–34 allowing for uncertainty): low exposure, across 16 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 science technicians 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.

That slice is not coming back; the core of the job, tasting or smelling foods or beverages to ensure that flavors meet specifications or to select samples with specific characteristics, stays yours. The tools change, the responsibility doesn't.

Your week, as this page understands it

Work with food scientists or technologists to perform standardized qualitative and quantitative tests to determine physical or chemical properties of food or beverage products. Includes technicians who assist in research and development of production technology, quality control, packaging, processing, and use of foods. The job title says “food science technicians”. The real job is the part underneath: tasting or smelling foods or beverages to ensure that flavors meet specifications or to select samples with specific characteristics. 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 science technicians is not one task. It is 16 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is tasting or smelling foods or beverages to ensure that flavors meet specifications or to select samples with specific characteristics, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 32%
changing shape: 0%
staying human: 68%

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.

One row is marked low confidence, so treat it as a ballpark rather than a fine measurement.
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

5 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.

Maintaining records of testing results or other documents as required by state or other governing agencies
This is reading one thing and writing another: records of testing results in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Maintain records of testing results or other documents as required by state or other governing agencies.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 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: Keeping required test records in order is document work software handles well, with a responsible person accountable for what is filed.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Analyzing test results to classify products or compare results with standard tables
This is reading one thing and writing another: test results in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Analyze test results to classify products or compare results with standard tables.” (O*NET task statement)
How this row was scored
Exposure score: 72 out of 100 (65–79 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: Comparing test results with standard tables is rule-following that software does quickly and consistently.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Recording or compiling test results or preparing graphs
This is reading one thing and writing another: test results in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Record or compile test results or prepare graphs, charts, or reports.” (O*NET task statement)
How this row was scored
Exposure score: 88 out of 100 (84–92 allowing for uncertainty): very 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: Turning test numbers into charts and written reports is exactly the kind of document work software already does well.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Ordering supplies needed to maintain inventories in laboratories or in storage facilities of food or beverage processing plants
This is reading one thing and writing another: supplies in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Order supplies needed to maintain inventories in laboratories or in storage facilities of food or beverage processing plants.” (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: Tracking stock levels and placing reorders is routine ordering work, though someone still checks the store room.
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.
Computing moisture or salt content
This is reading one thing and writing another: moisture in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Compute moisture or salt content, percentages of ingredients, formulas, or other product factors, using mathematical and chemical procedures.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very 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: Working out moisture, salt or ingredient percentages is arithmetic and chemistry that software computes reliably.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · 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

11 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.

Tasting or smelling foods or beverages to ensure that flavors meet specifications or to select samples with specific characteristics
This work happens in the physical world: foods, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Taste or smell foods or beverages to ensure that flavors meet specifications or to select samples with specific characteristics.” (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 smelling samples needs a human mouth and nose, which no software has.
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.
Monitoring and controlling temperature of products
This work happens in the physical world: temperature of products, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Monitor and control temperature of products.” (O*NET task statement)
How this row was scored
Exposure score: 29 out of 100 (22–36 allowing for uncertainty): low 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: Temperature data is easy to track, but keeping product at the right temperature still involves the plant floor.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Conducting standardized tests on food
This work happens in the physical world: standardized tests, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Conduct standardized tests on food, beverages, additives, or preservatives to ensure compliance with standards and regulations regarding factors such as color, texture, or nutrients.” (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 standardized tests on real food and drink samples happens physically in the lab.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Performing regular maintenance of laboratory equipment
This work happens in the physical world: regular maintenance of laboratory equipment, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Perform regular maintenance of laboratory equipment by inspecting, calibrating, cleaning, or sterilizing.” (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, calibrating and sterilizing lab instruments is hands-on maintenance at the bench.
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.
Training newly hired laboratory personnel
This work happens in the physical world: hired laboratory personnel, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Train newly hired laboratory personnel.” (O*NET task statement)
How this row was scored
Exposure score: 18 out of 100 (11–25 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: AI can build the training material, but showing a new starter how to work safely at the bench happens in person.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.

Show the other 6 tasks

Providing assistance to food scientists or technologists in research and development
staying human
This work happens in the physical world: assistance, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Provide assistance to food scientists or technologists in research and development, production technology, or quality control.” (O*NET task statement)
How this row was scored
Exposure score: 21 out of 100 (9–33 allowing for uncertainty): low exposure, low confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Helping scientists mixes desk analysis with bench work, so only part of it can be handed to software.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Supervising other food science technicians
staying human
This work happens in the physical world: other food science technicians, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Supervise other food science technicians.” (O*NET task statement)
How this row was scored
Exposure score: 11 out of 100 (4–18 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: Supervising people means being present, allocating work and dealing with staff issues face to face.
The five ratings: output a model can produce 1/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Examining chemical or biological samples to identify cell structures or to locate bacteria or extraneous material
staying human
This work happens in the physical world: chemical, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Examine chemical or biological samples to identify cell structures or to locate bacteria or extraneous material, using a microscope.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–7 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Preparing samples and working a microscope needs hands on 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 2/4.
Mixing blend or cultivate ingredients to make reagents or to manufacture food or beverage products
staying human
This work happens in the physical world: blend, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Mix, blend, or cultivate ingredients to make reagents or to manufacture food or beverage 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: Mixing and cultivating real ingredients is hands-on laboratory 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.
Measuring, testing or weighing bottles, cans or other containers to ensure that hardness, strength or dimensions meet specifications
staying human
This work happens in the physical world: bottles, cans or other containers, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Measure, test, or weigh bottles, cans, or other containers to ensure that hardness, strength, or dimensions meet specifications.” (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: Measuring and weighing real containers means physically handling them at the bench.
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.
Preparing or incubating slides with cell cultures
staying human
This work happens in the physical world: slides, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Prepare or incubate slides with cell cultures.” (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: Preparing and incubating slides is careful physical work with real samples.
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: $52,130a 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: 14,600in 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: test results in, a record out. The rows above are exactly that shape: recording or compiling test results or preparing graphs and maintaining records of testing results or other documents as required by state or other governing agencies. What it cannot do is be there in the room, and that is still where foods 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

Your week is splitting in two, and which half fills it is the whole question. Recording or compiling test results or preparing graphs is going; tasting or smelling foods or beverages to ensure that flavors meet specifications or to select samples with specific characteristics is not.

So, given all that: 32% of this job's task weight sits in rows the software is already learning, 0% in rows that change shape rather than disappear, and 68% in rows it is nowhere near. That is the position, measured across 16 scored tasks. It is not a forecast about you.

The occupation is an average and you are not, so the first job below is finding out where your own week actually sits.

This week: one thing

Print the task ledger above and put your own hours next to each row. Roughly, in pen, in one sitting. Then look at which group your hours actually pile up in. Twenty minutes, this week.

What you end up holding: your own week, on one page, sorted into what is shifting and what is not
How long it takes: about twenty minutes

If printing it isn’t practical: Read the rows off this page and write the same list on the back of an envelope. Same twenty minutes, same page, and it works just as well said out loud to someone who knows the job. The point is your hours next to the rows, not the paper it is on.

Over the next 90 days

Volunteer toward the durable end, visibly. Over the next ninety days put your hand up for the work in the bottom group (tasting or smelling foods or beverages to ensure that flavors meet specifications or to select samples with specific characteristics) and let people see you doing it. Not a new project: the same job, with your mix deliberately tilted. The point is that when the rota or the reorganisation comes, the version of you people picture is the one doing the part that stays.

Over the next 12 months

Claim a specialism at the durable end and let the other end go. Over a year, deliberately become the person who handles tasting or smelling foods or beverages to ensure that flavors meet specifications or to select samples with specific characteristics, and deliberately stop being the first choice for the rows in the top group. That trade costs something, because the exposed work is often the comfortable work. Decide it on purpose rather than by drift. 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 science technicians (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 6% of its durable work is work you already do and it pays 18.9% less. And on the numbers you do not need one. This job scores 29/100 here, with only 32% of the task list in the top band, and “taste or smell foods or beverages to ensure that flavors meet specifications…” 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 monitor and control temperature of products, and their equivalent is to observe gauges and thermometers to determine if the mixing chamber temperature is within…. 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. It is a pay cut, in those words: $42,290 against your $52,130, 18.9% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Food Service Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already taste or smell foods or beverages to ensure that flavors meet specifications or…, and their equivalent is to test cooked food by tasting and smelling it to ensure palatability and flavor…. Across both published task lists that is about 4% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 4% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
Materials Engineers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already provide assistance to food scientists or technologists in research and development, production technology…, and their equivalent is to supervise the work of technologists, technicians, and other engineers and scientists. Across both published task lists that is about 4% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 4% of the durable side of that job. That is a different job, not a next step. The pay gap is the market pricing a barrier: $112,860 against your $52,130 is 2.17× (OEWS May 2025 (both)), and you would be crossing it holding about 4% of their durable work. A gap that size with an overlap that small is a wish, not a route.
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: 32% of its task weight, across 16 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The whole-job doom story
Nothing on this page says this job ends. It says the mix inside it moves. Half the rows above are unchanged or changing shape, and the plan is about which half your week sits in. That is a very different problem, and a solvable one.
Panic-buying a course
Do the twenty-minute sorting exercise first. Most people who buy a course before they have done it buy the wrong one, and the free services listed below will tell you the same thing without charging for it.
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 Laboratory technicians 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 Laboratory technicians, Farmers and Horticultural trades. 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 science technicians, 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 32% 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 science technicians. 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 science technicians launches. Nothing else.

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No Space for food science technicians 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 science technicians 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 science technicians 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 Science Technicians?: Not as a job, but it is already doing parts of the work. Across the 16 official task statements scored for Food Science Technicians (United States, SOC 19-4013), 32% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 29 out of 100 (range 25–34, band: low). 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 Science Technicians” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Record or compile test results or prepare graphs, charts, or reports” (88/100, very high); “Compute moisture or salt content, percentages of ingredients, formulas, or other product factors, using mathematical and chemical procedures” (81/100, very high); “Analyze test results to classify products or compare results with standard tables” (72/100, high). 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 Science Technicians” stay human?: About 68% 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: “Prepare or incubate slides with cell cultures” (0/100, minimal); “Perform regular maintenance of laboratory equipment by inspecting, calibrating, cleaning, or sterilizing” (0/100, minimal); “Measure, test, or weigh bottles, cans, or other containers to ensure that hardness, strength, or dimensions meet specifications” (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 Science Technicians” do about AI?: Start from the ledger rather than the headline: 32% of this job's weighted core work is exposed, and roughly 68% 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 Science Technicians 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 16 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

One row is marked low confidence, so treat it as a ballpark rather than a fine measurement.
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.