Will AI replace Forest Fire Inspectors and Prevention Specialists?

Not as a job, but it is already doing parts of the work. Across the 16 official task statements scored for Forest Fire Inspectors and Prevention Specialists (United States, SOC 33-2022), 13% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 22 out of 100 (range 17–28, 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.

What should someone working in “Forest Fire Inspectors and Prevention Specialists” do about AI?

Start from the ledger rather than the headline: 13% of this job's weighted core work is exposed, and roughly 80% 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 Forest Fire Inspectors and Prevention Specialists 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

Forest Fire Inspectors and Prevention Specialists

relaying messages about emergencies, accidents, extinguishing smaller fires with portable extinguishers and patrolling assigned areas. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: extinguishing smaller fires with portable extinguishers 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: 13%
changing shape: 7%
staying human: 80%

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

Whole-job exposure score 22 out of 100 (17–28 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 forest fire inspectors and prevention specialists 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 compiling and reporting meteorological data. This page scores what today's tools actually do, not headlines.

Your week, as this page understands it

Enforce fire regulations, inspect forest for fire hazards, and recommend forest fire prevention or control measures. May report forest fires and weather conditions. The job title says “forest fire inspectors” or “prevention specialists”: officially one job, two names. The real job is the part underneath: extinguishing smaller fires with portable extinguishers. 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 forest fire inspectors and prevention specialists 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 extinguishing smaller fires with portable extinguishers, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 13%
changing shape: 7%
staying human: 80%

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 task scored differently between repeat runs, so its range on this page is wider. We would rather show the wobble than hide it.
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

2 tasks

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

Compiling and reporting meteorological data
This is reading one thing and writing another: meteorological data in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Compile and report meteorological data, such as temperature, relative humidity, wind direction and velocity, and types of cloud formations.” (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: Weather readings are already instrument-recorded, so compiling and reporting them is routine data work.
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 4/4.
Maintaining records and logbooks
This is reading one thing and writing another: records in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Maintain records and logbooks.” (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: Keeping records and logbooks is routine documentation software handles well.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Changing shape

1 task

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.

Educating the public about fire safety and prevention
The software now makes the first pass at the public, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Educate the public about fire safety and prevention.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (49–57 allowing for uncertainty): partial 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: Fire safety education draws on well-published guidance that software drafts into usable materials and talks.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 4/4.

Staying human

13 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.

Relaying messages about emergencies, accidents, locations of crew and personnel and fire hazard conditions
This work happens in the physical world: messages, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Relay messages about emergencies, accidents, locations of crew and personnel, and fire hazard conditions.” (O*NET task statement)
How this row was scored
Exposure score: 19 out of 100 (9–29 allowing for uncertainty): minimal exposure, medium confidence, and it moved between repeat runs, so the range is widened.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Relaying emergency messages is quick communication that depends on being out there with the crews.
The five ratings: output a model can produce 2/4 · 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 2/4.
Conducting wildland firefighting training
This work happens in the physical world: wildland, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Conduct wildland firefighting training.” (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: the same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Fire training materials can be drafted, but the hands-on parts are taught in person by qualified instructors.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Estimating sizes and characteristics of fires
This work happens in the physical world: sizes, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Estimate sizes and characteristics of fires, and report findings to base camps by radio or telephone.” (O*NET task statement)
How this row was scored
Exposure score: 7 out of 100 (3–11 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Sizing up a fire and calling it in depends on someone seeing it from where it is burning.
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 1/4 · how much data exists 2/4.
Extinguishing smaller fires with portable extinguishers
This work happens in the physical world: smaller fires, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Extinguish smaller fires with portable extinguishers, shovels, and axes.” (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: Putting out a fire with an extinguisher, shovel or axe is physical work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Directing crews working on firelines during forest fires
This work happens in the physical world: crews, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Direct crews working on firelines during forest fires.” (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: Directing crews on a fireline is command work done at the fire.
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.
Locating forest fires on area maps
This work happens in the physical world: forest fires, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Locate forest fires on area maps, using azimuth sighters and known landmarks.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (6–20 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Fixing a fire’s position with a sighting instrument requires standing at the lookout using it.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Patrolling assigned areas, looking for forest fires, hazardous conditions and weather phenomena
This work happens in the physical world: assigned areas, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Patrol assigned areas, looking for forest fires, hazardous conditions, and weather phenomena.” (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: Patrolling forest land for fires and hazards is a job done on foot or from a vehicle.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.

Show the other 6 tasks

Directing maintenance and repair of firefighting equipment
staying human
This work happens in the physical world: maintenance, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Direct maintenance and repair of firefighting equipment, or requisition new equipment.” (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; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Ordering equipment is straightforward, but directing repairs means being around the kit and the people fixing it.
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 3/4.
Administering regulations regarding sanitation, fire prevention, violation corrections and related forest regulations
staying human
This work happens in the physical world: regulations regarding sanitation, fire prevention, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Administer regulations regarding sanitation, fire prevention, violation corrections, and related forest regulations.” (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: 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: Applying forest and fire regulations mixes documented rules with on-the-ground enforcement by an authorized officer.
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 1/4 · how much data exists 3/4.
Restricting public access and recreational use of forest lands during critical fire seasons
staying human
This work happens in the physical world: public access, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Restrict public access and recreational use of forest lands during critical fire seasons.” (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: 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: Deciding and enforcing seasonal closures uses documented fire-danger measures but needs an official acting on the ground.
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 1/4 · how much data exists 3/4.
Examining and inventorying firefighting equipment
staying human
This work happens in the physical world: equipment, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Examine and inventory firefighting equipment, such as axes, fire hoses, shovels, pumps, buckets, and fire extinguishers, to determine amount and condition.” (O*NET task statement)
How this row was scored
Exposure score: 14 out of 100 (7–21 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Counting and checking firefighting kit means physically going through the equipment store.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Inspecting forest tracts and logging areas for fire hazards
staying human
This work happens in the physical world: forest tracts, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Inspect forest tracts and logging areas for fire hazards such as accumulated wastes or mishandling of combustibles, and recommend appropriate fire prevention measures.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (6–20 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Spotting fire hazards in forest and logging areas depends on being in the trees looking at the fuel.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Inspecting camp sites to ensure that campers are in compliance with forest use regulations
staying human
This work happens in the physical world: camp sites, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Inspect camp sites to ensure that campers are in compliance with forest use regulations.” (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: Checking campsites for rule-breaking means walking the site and dealing with the people there.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.

What this job pays, and how many people do it

Median pay: $56,870a 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: 2,780in 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: meteorological data in, a record out. The rows above are exactly that shape: compiling and reporting meteorological data and maintaining records and logbooks. What it cannot do is be there in the room, and that is still where smaller fires get done. Which is why this page talks about your tasks changing, not your job ending.

Your move

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

Start with what does not change: extinguishing smaller fires with portable extinguishers is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 13% of this job's task weight sits in rows the software is already learning, 7% in rows that change shape rather than disappear, and 80% 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. Compiling and reporting meteorological data is the part turning into software, and being the person who understands that layer is worth money.

This week: one thing

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

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

If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether smaller fires 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 educating the public about fire safety and prevention. 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 forest fire inspectors and prevention specialists (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 firefighting and prevention workers: only about 6% of its durable work is work you already do. And on the numbers you do not need one. This job scores 22/100 here, with only 13% of the task list in the top band, and “relay messages about emergencies, accidents, locations of crew and personnel, and fire…” 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 Firefighting and Prevention Workers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already direct maintenance and repair of firefighting equipment, or requisition new equipment, and their equivalent is to perform maintenance and minor repairs on firefighting equipment. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
Fire Inspectors and Investigators
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already educate the public about fire safety and prevention, and their equivalent is to teach public education programs on fire safety and prevention. 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 →
Forest and Conservation Technicians
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already educate the public about fire safety and prevention, and their equivalent is to manage forest protection activities. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% 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: 13% 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 extinguishing smaller fires with portable extinguishers 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 Quality assurance 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 Quality assurance technicians, Science, engineering and production technicians n.e.c., Fire service officers (watch manager and below) and Senior officers in fire, ambulance, prison and related services. 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 forest fire inspectors / prevention specialists, 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 13% 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 forest fire inspectors / prevention specialists. 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 forest fire inspectors / prevention specialists launches. Nothing else.

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No Space for forest fire inspectors / prevention specialists 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 forest fire inspectors / prevention specialists 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 forest fire inspectors / prevention specialists 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 Forest Fire Inspectors and Prevention Specialists?: Not as a job, but it is already doing parts of the work. Across the 16 official task statements scored for Forest Fire Inspectors and Prevention Specialists (United States, SOC 33-2022), 13% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 22 out of 100 (range 17–28, 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 “Forest Fire Inspectors and Prevention Specialists” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Compile and report meteorological data, such as temperature, relative humidity, wind direction and velocity, and types of cloud formations” (75/100, high); “Maintain records and logbooks” (69/100, high); “Educate the public about fire safety and prevention” (53/100, partial). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Forest Fire Inspectors and Prevention Specialists” stay human?: About 80% 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: “Patrol assigned areas, looking for forest fires, hazardous conditions, and weather phenomena” (0/100, minimal); “Extinguish smaller fires with portable extinguishers, shovels, and axes” (0/100, minimal); “Direct crews working on firelines during forest fires” (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 “Forest Fire Inspectors and Prevention Specialists” do about AI?: Start from the ledger rather than the headline: 13% of this job's weighted core work is exposed, and roughly 80% 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 Forest Fire Inspectors and Prevention Specialists 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 task scored differently between repeat runs, so its range on this page is wider. We would rather show the wobble than hide it.
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.