Will AI replace Animal Trainers?

Not as a job, but it is already doing parts of the work. Across the 15 official task statements scored for Animal Trainers (United States, SOC 39-2011), 8% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 9 out of 100 (range 7–14, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.

Which tasks in “Animal Trainers” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Keep records documenting animal health, diet, or behavior” (75/100, high); “Advise animal owners regarding the purchase of specific animals” (26/100, low); “Organize or conduct animal shows” (21/100, 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 “Animal Trainers” stay human?

About 92% 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: “Conduct training programs to develop or maintain desired animal behaviors for competition, entertainment, obedience, security, riding, or related purposes” (0/100, minimal); “Retrain horses to break bad habits, such as kicking, bolting, or resisting bridling or grooming” (0/100, minimal); “Train dogs in human assistance or property protection duties” (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 “Animal Trainers” do about AI?

Start from the ledger rather than the headline: 8% of this job's weighted core work is exposed, and roughly 92% 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 Animal Trainers 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 15 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

Animal Trainers

training horses or other equines, administering prescribed medications to animals and keeping records documenting animal health. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: talk to or interact with animals to familiarize them to human voices or contact 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: 8%
changing shape: 0%
staying human: 92%

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

Whole-job exposure score 9 out of 100 (7–14 allowing for uncertainty): minimal exposure, across 15 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 animal trainers 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 keeping records documenting animal health: the overhead at the edges, not the middle you trained for.

Your week, as this page understands it

Train animals for riding, harness, security, performance, or obedience, or for assisting persons with disabilities. Accustom animals to human voice and contact, and condition animals to respond to commands. Train animals according to prescribed standards for show or competition. May train animals to carry pack loads or work as part of pack team. The job title says “animal trainers”. The real job is the part underneath: talk to or interact with animals to familiarize them to human voices or contact. 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 animal trainers is not one task. It is 15 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is talk to or interact with animals to familiarize them to human voices or contact, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 8%
changing shape: 0%
staying human: 92%

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.

3 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.

Keeping records documenting animal health
This is reading one thing and writing another: records documenting animal health in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Keep records documenting animal health, diet, or behavior.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 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: Once the observations exist, writing and organizing health, diet and behavior records is straightforward computer work.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

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

14 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.

Talk to or interact with animals to familiarize them to human voices or contact
This work happens in the physical world: or interact, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Talk to or interact with animals to familiarize them to human voices or contact.” (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: Getting an animal used to human voice and touch only works with a real human present.
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.
Evaluating animals to determine their temperaments
This work happens in the physical world: animals, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Evaluate animals to determine their temperaments, abilities, or aptitude for training.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (0–13 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Judging one animal's temperament comes from handling it, and much of that skill is never written down.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Conducting training programs to develop or maintain desired animal behaviors
This work happens in the physical world: programs, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Conduct training programs to develop or maintain desired animal behaviors for competition, entertainment, obedience, security, riding, or related purposes.” (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: Running a training program is hands-on work with a live animal, though written plans can be drafted.
The five ratings: output a model can produce 1/4 · 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.
Feeding or exercising animals or providing other general care
This work happens in the physical world: animals, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Feed or exercise animals or provide other general care, such as cleaning or maintaining holding or performance areas.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Feeding, exercising and cleaning are jobs for hands in the enclosure, not a computer.
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.
Observing animals' physical conditions to detect illness or unhealthy conditions requiring medical care
This work happens in the physical world: animals' physical conditions, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Observe animals' physical conditions to detect illness or unhealthy conditions requiring medical care.” (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: Spotting illness means being beside the animal and noticing how it looks, moves and behaves.
The five ratings: output a model can produce 1/4 · 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.
Evaluate animals for trainability and ability to perform
This work happens in the physical world: animals, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Evaluate animals for trainability and ability to perform.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (0–13 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Judging whether an animal will train comes from working with it, and much of the skill is unwritten.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Cuing or signaling animals during performances
This work happens in the physical world: animals during performances, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Cue or signal animals during performances.” (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: Signalling an animal mid-performance means a person standing there with it, so software cannot take the cue over.
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.
Administering prescribed medications to animals
This work happens in the physical world: prescribed medications, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Administer prescribed medications to animals.” (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: Giving an animal its medicine is a physical act, and a vet's instructions govern how it is done.
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.
Advising animal owners regarding the purchase of specific animals
The value here is that a specific person handles animal owners regarding the purchase of specific animals and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Advise animal owners regarding the purchase of specific animals.” (O*NET task statement)
How this row was scored
Exposure score: 26 out of 100 (19–33 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: General buying guidance can be drafted, but owners want a trusted person who has seen the animal.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.

Show the other 5 tasks

Organizing or conducting animal shows
staying human
This work happens in the physical world: animal shows, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Organize or conduct animal shows.” (O*NET task statement)
How this row was scored
Exposure score: 21 out of 100 (14–28 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Planning and paperwork for a show can be drafted, but running the day needs someone on site.
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.
Training horses or other equines
staying human
This work happens in the physical world: horses, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Train horses or other equines for riding, harness, show, racing, or other work, using knowledge of breed characteristics, training methods, performance standards, and the peculiarities of each animal.” (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: Training a horse to ride, show or race happens in the arena with the animal.
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.
Using oral, spur, rein or hand commands to condition horses to carry riders or to pull horse-drawn equipment
staying human
This work happens in the physical world: oral, spur, rein or hand commands, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Use oral, spur, rein, or hand commands to condition horses to carry riders or to pull horse-drawn 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: Riding and command work is physical horsemanship.
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.
Training dogs in human assistance or property protection duties
staying human
This work happens in the physical world: dogs, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Train dogs in human assistance or property protection duties.” (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: Teaching a dog assistance or protection work requires a handler physically working with that dog.
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.
Retraining horses to break bad habits
staying human
This work happens in the physical world: horses, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Retrain horses to break bad habits, such as kicking, bolting, or resisting bridling or grooming.” (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: Correcting a horse's habits is ridden and handled 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.

What this job pays, and how many people do it

Median pay: $39,990a 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: 18,770in 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: records documenting animal health in, a record out. The rows above are exactly that shape: keeping records documenting animal health. What it cannot do is be there in the room, and that is still where or interact gets 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: talk to or interact with animals to familiarize them to human voices or contact is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 8% of this job's task weight sits in rows the software is already learning, 0% in rows that change shape rather than disappear, and 92% in rows it is nowhere near. That is the position, measured across 15 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. Keeping records documenting animal health 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 records documenting animal health, 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 or interact is 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 talk to or interact with animals to familiarize them to human voices or contact. 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 animal trainers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was animal caretakers: only about 9% of its durable work is work you already do and it pays 11.6% less. And on the numbers you do not need one. This job scores 9/100 here, with only 8% of the task list in the top band, and “talk to or interact with animals to familiarize them to human voices…” 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.

Animal Caretakers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already feed or exercise animals or provide other general care, and their equivalent is to feed and water animals according to schedules and feeding instructions. Across both published task lists that is about 9% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 9% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $35,360 against your $39,990, 11.6% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Veterinary Assistants and Laboratory Animal Caretakers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already administer prescribed medications to animals, and their equivalent is to administer medication, immunizations, or blood plasma to animals as prescribed by veterinarians. Across both published task lists that is about 9% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 9% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
Animal Breeders
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already feed or exercise animals or provide other general care, and their equivalent is to exercise animals to keep them in healthy condition. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step. And it is a narrow door: about 1,330 of those jobs against 18,770 of yours (OEWS May 2025), 7% as many seats.
Look at that job’s page anyway →

What I’d stop worrying about

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

The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 8% of its task weight, across 15 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 talk to or interact with animals to familiarize them to human voices or contact 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 nearest United Kingdom equivalent is Animal care services occupations n.e.c. It is a close match rather than an identical one: the two countries draw the boundary of the job in slightly different places.

Switch to the United Kingdom page →close match

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 animal trainers, 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 8% 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 animal trainers. 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 animal trainers launches. Nothing else.

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

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No Space for animal trainers 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 animal trainers 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 animal trainers 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 Animal Trainers?: Not as a job, but it is already doing parts of the work. Across the 15 official task statements scored for Animal Trainers (United States, SOC 39-2011), 8% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 9 out of 100 (range 7–14, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
Which tasks in “Animal Trainers” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Keep records documenting animal health, diet, or behavior” (75/100, high); “Advise animal owners regarding the purchase of specific animals” (26/100, low); “Organize or conduct animal shows” (21/100, 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 “Animal Trainers” stay human?: About 92% 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: “Conduct training programs to develop or maintain desired animal behaviors for competition, entertainment, obedience, security, riding, or related purposes” (0/100, minimal); “Retrain horses to break bad habits, such as kicking, bolting, or resisting bridling or grooming” (0/100, minimal); “Train dogs in human assistance or property protection duties” (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 “Animal Trainers” do about AI?: Start from the ledger rather than the headline: 8% of this job's weighted core work is exposed, and roughly 92% 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 Animal Trainers 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 15 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

3 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.