Will AI replace Agricultural Inspectors?

Not as a job, but it is already doing parts of the work. Across the 16 official task statements scored for Agricultural Inspectors (United States, SOC 45-2011), 3% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 13 out of 100 (range 7–19, 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 “Agricultural Inspectors” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Compare product recipes with government-approved formulas or recipes to determine acceptability” (69/100, high); “Write reports of findings and recommendations and advise farmers, growers, or processors of corrective action to be taken” (36/100, low); “Provide consultative services in areas such as equipment or product evaluation, plant construction or layout, or food safety systems” (28/100, low). 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 “Agricultural Inspectors” stay human?

About 97% 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: “Collect samples from animals, plants, or products and route them to laboratories for microbiological assessment, ingredient verification, or other testing” (0/100, minimal); “Testify in legal proceedings” (1/100, minimal); “Take emergency actions, such as closing production facilities, if product safety is compromised” (4/100, 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 “Agricultural Inspectors” do about AI?

Start from the ledger rather than the headline: 3% of this job's weighted core work is exposed, and roughly 97% 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 Agricultural Inspectors 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

Agricultural Inspectors

inspecting food products and processing procedures to determine whether products are safe, interpreting and enforcing government acts and regulations and explaining required standards to agricultural workers and verifying that transportation and handling procedures meet regulatory requirements. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: inspecting food products and processing procedures to determine whether products are safe to eat 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: 3%
changing shape: 0%
staying human: 97%

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

Whole-job exposure score 13 out of 100 (7–19 allowing for uncertainty): minimal 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 agricultural inspectors 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 comparing product recipes with government-approved formulas or recipes to determine acceptability. This page scores what today's tools actually do, not headlines.

Your week, as this page understands it

Inspect agricultural commodities, processing equipment, and facilities, and fish and logging operations, to ensure compliance with regulations and laws governing health, quality, and safety. The job title says “agricultural inspectors”. The real job is the part underneath: inspecting food products and processing procedures to determine whether products are safe to eat. 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 agricultural inspectors 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 inspecting food products and processing procedures to determine whether products are safe to eat, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 3%
changing shape: 0%
staying human: 97%

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.
2 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.

Comparing product recipes with government-approved formulas or recipes to determine acceptability
This is reading one thing and writing another: product recipes in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Compare product recipes with government-approved formulas or recipes to determine acceptability.” (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; someone qualified has to answer for it.
The rating behind it: Comparing a recipe against an approved formula is a straightforward document check.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · 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

15 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.

Inspecting food products and processing procedures to determine whether products are safe to eat
This work happens in the physical world: food products, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Inspect food products and processing procedures to determine whether products are safe to eat.” (O*NET task statement)
How this row was scored
Exposure score: 5 out of 100 (0–12 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Judging whether food is safe means being in the plant, and only an authorized inspector can make the call.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 3/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Interpreting and enforcing government acts and regulations and explaining required standards to agricultural workers
This work happens in the physical world: government acts, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Interpret and enforce government acts and regulations and explain required standards to agricultural workers.” (O*NET task statement)
How this row was scored
Exposure score: 24 out of 100 (17–31 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Regulations are written down and easy to explain, but enforcement is an official act carried out on site.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 2/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Inspecting agricultural commodities or related operations
This work happens in the physical world: agricultural commodities, in a real place. Software cannot follow it there.
importance 5 · Supplemental
Source: “Inspect agricultural commodities or related operations, as well as fish or logging operations, for compliance with laws and regulations governing health, quality, and safety.” (O*NET task statement)
How this row was scored
Exposure score: 5 out of 100 (0–12 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Compliance inspections happen at the site, and the finding must come from an authorized inspector.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 3/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Inspecting the cleanliness and practices of establishment employees
This work happens in the physical world: the cleanliness, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Inspect the cleanliness and practices of establishment employees.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (2–10 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Watching how staff actually handle food has to be done in the room with them.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Collecting samples from animals, plants or products and routing them to laboratories for microbiological assessment, ingredient verification or other testing
This work happens in the physical world: samples, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Collect samples from animals, plants, or products and route them to laboratories for microbiological assessment, ingredient verification, or other testing.” (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; someone qualified has to answer for it.
The rating behind it: Collecting samples from animals, plants and products is physical work in the field.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Verifying that transportation and handling procedures meet regulatory requirements
This work happens in the physical world: transportation, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Verify that transportation and handling procedures meet regulatory requirements.” (O*NET task statement)
How this row was scored
Exposure score: 22 out of 100 (15–29 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Much of this is checking paperwork against rules, though transport and handling still get looked at directly.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Examining, weighing and measuring commodities, such as poultry, eggs, meat or seafood to certify qualities, grades and weights
This work happens in the physical world: commodities, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Examine, weigh, and measure commodities, such as poultry, eggs, meat, or seafood to certify qualities, grades, and weights.” (O*NET task statement)
How this row was scored
Exposure score: 5 out of 100 (0–12 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Examining and weighing produce is hands-on, and the certified grade must come from an authorized inspector.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 3/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Writing reports of findings and recommendations and advising farmers
The rules require a named, qualified person to answer for reports of findings, and that person cannot be a piece of software.
importance 4 · Supplemental
Source: “Write reports of findings and recommendations and advise farmers, growers, or processors of corrective action to be taken.” (O*NET task statement)
How this row was scored
Exposure score: 36 out of 100 (29–43 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Writing up findings and corrective advice is document work software drafts well, with the inspector accountable for what is issued.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Monitoring the grading performed by company employees to verify conformance to standards
This work happens in the physical world: this work, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Monitor the grading performed by company employees to verify conformance to standards.” (O*NET task statement)
How this row was scored
Exposure score: 9 out of 100 (2–16 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Checking that company graders are doing it right means watching them work.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.

Show the other 6 tasks

Providing consultative services in areas
staying human
This work happens in the physical world: consultative services, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Provide consultative services in areas such as equipment or product evaluation, plant construction or layout, or food safety systems.” (O*NET task statement)
How this row was scored
Exposure score: 28 out of 100 (16–40 allowing for uncertainty): low exposure, low 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: Advice on layout, equipment and food safety systems is well documented, though it usually follows a site visit.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Labelling and sealing graded products and issuing official grading certificates
staying human
This work happens in the physical world: graded products, in a real place. Software cannot follow it there.
importance 5 · Supplemental
Source: “Label and seal graded products and issue official grading certificates.” (O*NET task statement)
How this row was scored
Exposure score: 9 out of 100 (2–16 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; someone qualified has to answer for it.
The rating behind it: Grading certificates are documents, but the sealing is physical and only an authorized inspector may issue them.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 3/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Inspecting or testing horticultural products or livestock to detect harmful diseases
staying human
This work happens in the physical world: horticultural products, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Inspect or test horticultural products or livestock to detect harmful diseases, chemical residues, or infestations and to determine the quality of products or animals.” (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; someone qualified has to answer for it.
The rating behind it: Detecting disease or infestation means examining real plants and animals in the field.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Monitoring the operations and sanitary conditions of slaughtering or meat processing plants
staying human
This work happens in the physical world: the operations, in a real place. Software cannot follow it there.
importance 5 · Supplemental
Source: “Monitor the operations and sanitary conditions of slaughtering or meat processing plants.” (O*NET task statement)
How this row was scored
Exposure score: 5 out of 100 (1–9 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Watching how a plant actually runs and how clean it is requires being there in person.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 3/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Taking emergency actions, such as closing production facilities, if product safety
staying human
This work happens in the physical world: emergency actions, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Take emergency actions, such as closing production facilities, if product safety is compromised.” (O*NET task statement)
How this row was scored
Exposure score: 4 out of 100 (0–8 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Shutting a facility is a legal power given to an appointed official, exercised on site.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 4/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Testifying in legal proceedings
staying human
This work happens in the physical world: legal proceedings, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Testify in legal proceedings.” (O*NET task statement)
How this row was scored
Exposure score: 1 out of 100 (0–5 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Giving evidence is something only a person can do, because it is their own word being tested.
The five ratings: output a model can produce 0/4 · needs a body in a room 3/4 · needs an accountable person 4/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.

What this job pays, and how many people do it

Median pay: $49,940a 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: 14,410in 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: product recipes in, a record out. The rows above are exactly that shape: comparing product recipes with government-approved formulas or recipes to determine acceptability. What it cannot do is be answerable: food products need a named person the rules will accept, and software cannot be that person. 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: inspecting food products and processing procedures to determine whether products are safe to eat is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 3% of this job's task weight sits in rows the software is already learning, 0% in rows that change shape rather than disappear, and 97% in rows it is nowhere near. That is the position, measured across 16 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. Comparing product recipes with government-approved formulas or recipes to determine acceptability 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 product recipes, 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 food 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 inspecting food products and processing procedures to determine whether products are safe to eat. 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 agricultural inspectors (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was first-line supervisors of transportation and material moving workers, except aircraft cargo handling supervisors: only about 4% of its durable work is work you already do. And on the numbers you do not need one. This job scores 13/100 here, with only 3% of the task list in the top band, and “inspect food products and processing procedures to determine whether products are safe…” 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.

First-Line Supervisors of Transportation and Material Moving Workers, Except Aircraft Cargo Handling Supervisors
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already inspect the cleanliness and practices of establishment employees, and their equivalent is to inspect work areas or operating equipment to ensure conformance to established standards in…. 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 →
Weighers, Measurers, Checkers, and Samplers, Recordkeeping
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already collect samples from animals, plants, or products and route them to laboratories for…, and their equivalent is to collect product samples and prepare them for laboratory analysis or testing. Across both published task lists that is about 3% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
Chefs and Head Cooks
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already inspect the cleanliness and practices of establishment employees, and their equivalent is to monitor sanitation practices to ensure that employees follow standards and regulations. Across both published task lists that is about 3% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 3% 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: 3% of its task weight, across 16 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 inspecting food products and processing procedures to determine whether products are safe to eat 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 Inspectors of standards and regulations 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:

Environmental health professionals

In UK official statistics this job is counted as Inspectors of standards and regulations and Environmental health professionals. 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

Why there is no community here

Collab365, who build this site, run paid Spaces for a small number of subjects, and none of them is built for this job. We are not going to point you at the nearest one and call it a fit.

So the free services listed on this page are the whole answer, and it is the same answer we would give a friend.

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

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No Space for agricultural inspectors 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 agricultural inspectors 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 agricultural inspectors 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 Agricultural Inspectors?: Not as a job, but it is already doing parts of the work. Across the 16 official task statements scored for Agricultural Inspectors (United States, SOC 45-2011), 3% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 13 out of 100 (range 7–19, 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 “Agricultural Inspectors” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Compare product recipes with government-approved formulas or recipes to determine acceptability” (69/100, high); “Write reports of findings and recommendations and advise farmers, growers, or processors of corrective action to be taken” (36/100, low); “Provide consultative services in areas such as equipment or product evaluation, plant construction or layout, or food safety systems” (28/100, low). 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 “Agricultural Inspectors” stay human?: About 97% 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: “Collect samples from animals, plants, or products and route them to laboratories for microbiological assessment, ingredient verification, or other testing” (0/100, minimal); “Testify in legal proceedings” (1/100, minimal); “Take emergency actions, such as closing production facilities, if product safety is compromised” (4/100, 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 “Agricultural Inspectors” do about AI?: Start from the ledger rather than the headline: 3% of this job's weighted core work is exposed, and roughly 97% 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 Agricultural Inspectors 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

About the data on this page

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.
2 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 2026-08-05.
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.