Will AI replace Forest and Conservation Technicians?

Not as a job, but it is already doing parts of the work. Across the 20 official task statements scored for Forest and Conservation Technicians (United States, SOC 19-4071), 14% 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 and Conservation Technicians” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Develop and maintain computer databases” (83/100, very high); “Map forest tract data using digital mapping systems” (75/100, high); “Keep records of the amount and condition of logs taken to mills” (69/100, high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.

Which tasks in “Forest and Conservation Technicians” stay human?

About 83% 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: “Inspect trees and collect samples of plants, seeds, foliage, bark, and roots to locate insect and disease damage” (0/100, minimal); “Conduct laboratory or field experiments with plants, animals, insects, diseases, and soils” (0/100, minimal); “Perform reforestation or forest renewal, including nursery and silviculture operations, site preparation, seeding and tree planting programs, cone collection…” (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 and Conservation Technicians” do about AI?

Start from the ledger rather than the headline: 14% of this job's weighted core work is exposed, and roughly 83% 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 and Conservation Technicians calculated?

Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 20 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 and Conservation Technicians

keeping records of the amount and condition of logs taken, training and leading forest and conservation workers in seasonal activities and supervising forest nursery operations, timber harvesting. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: thinning and spacing trees and controlling weeds and undergrowth is work software can't reach.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 14%
changing shape: 2%
staying human: 83%

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 20 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 and conservation technicians is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.

What shifts is developing and maintaining computer databases: the overhead at the edges, not the middle you trained for.

Your week, as this page understands it

Provide technical assistance regarding the conservation of soil, water, forests, or related natural resources. May compile data pertaining to size, content, condition, and other characteristics of forest tracts under the direction of foresters, or train and lead forest workers in forest propagation and fire prevention and suppression. May assist conservation scientists in managing, improving, and protecting rangelands and wildlife habitats. The job title says “forest” or “conservation technicians”: officially one job, two names. The real job is the part underneath: thinning and spacing trees and controlling weeds and undergrowth. 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 and conservation technicians is not one task. It is 20 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is thinning and spacing trees and controlling weeds and undergrowth, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 14%
changing shape: 2%
staying human: 83%

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

3 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.

Mapping forest tract data using digital mapping systems
This is reading one thing and writing another: forest tract data in, a record out. That is the shape today's tools are built for.
importance 3 · Core
Source: “Map forest tract data using digital mapping systems.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Mapping tract data in a digital system is software work already, so AI can do much of the drawing and checking.
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.
Developing and maintaining computer databases
This is reading one thing and writing another: computer databases in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Develop and maintain computer databases.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Building and maintaining databases is standard, well-documented technical work that AI drafts strongly.
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 4/4.
Keeping records of the amount and condition of logs taken to mills
This is reading one thing and writing another: records of the amount in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Keep records of the amount and condition of logs taken to mills.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (62–76 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Recording log volumes and condition delivered to mills is routine record-keeping that 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.

Issuing fire permits, timber permits and other forest use licenses
The software now makes the first pass at fire permits, but someone has to be answerable for the result, and it cannot be the software. So the job becomes checking and deciding rather than producing.
importance 3 · Supplemental
Source: “Issue fire permits, timber permits, and other forest use licenses.” (O*NET task statement)
How this row was scored
Exposure score: 59 out of 100 (52–66 allowing for uncertainty): partial 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: Permits are standard documents checked against written rules, though issuing one is an authorized officer act.
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 1/4 · how much data exists 3/4.

Staying human

16 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.

Thinning and spacing trees and controlling weeds and undergrowth
This work happens in the physical world: trees, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Thin and space trees and control weeds and undergrowth, using manual tools and chemicals, or supervise workers performing these tasks.” (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: Thinning trees and clearing undergrowth with tools and chemicals is physical work in the forest.
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 1/4 · how much data exists 1/4.
Providing information about and enforcing regulations, such as those concerning environmental protection, resource utilization, fire safety and accident prevention
This work happens in the physical world: information, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Provide information about, and enforce, regulations, such as those concerning environmental protection, resource utilization, fire safety, and accident prevention.” (O*NET task statement)
How this row was scored
Exposure score: 20 out of 100 (13–27 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 value is that a specific person does it.
The rating behind it: The rules are written down and easy to explain, but enforcing them needs an authorized officer present.
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 2/4 · how much data exists 3/4.
Training and leading forest and conservation workers in seasonal activities
This work happens in the physical world: forest, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Train and lead forest and conservation workers in seasonal activities, such as planting tree seedlings, putting out forest fires, and maintaining recreational facilities.” (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; the value is that a specific person does it.
The rating behind it: Training seasonal crews to plant, fight fire and maintain sites happens outdoors alongside them.
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 2/4 · how much data exists 2/4.
Managing forest protection activities, including fire control
This work happens in the physical world: forest protection activities, including fire control, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Manage forest protection activities, including fire control, fire crew training, and coordination of fire detection and public education programs.” (O*NET task statement)
How this row was scored
Exposure score: 15 out of 100 (8–22 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Running fire control and crew training is coordination of people and equipment across real ground.
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 2/4.
Patrolling park or forest areas to protect resources and prevent damage
This work happens in the physical world: park, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Patrol park or forest areas to protect resources and prevent damage.” (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 a forest to spot and prevent damage means physically covering the ground.
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 1/4 · how much data exists 1/4.
Providing forestry education and general information
The value here is that a specific person handles forestry education and stands behind it. That is earned, not computed.
importance 3 · Supplemental
Source: “Provide forestry education and general information, advice, and recommendations to woodlot owners, community organizations, and the general public.” (O*NET task statement)
How this row was scored
Exposure score: 39 out of 100 (32–46 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; the value is that a specific person does it.
The rating behind it: Forestry advice is well documented and easy to draft, but landowners tend to want it from a person they know.
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 2/4 · how much data exists 3/4.

Show the other 10 tasks

Providing technical support to forestry research programs in areas
staying human
This work happens in the physical world: technical support, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Provide technical support to forestry research programs in areas such as tree improvement, seed orchard operations, insect and disease surveys, or experimental forestry and forest engineering research.” (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: Research support mixes desk analysis with fieldwork in orchards, plots and survey sites.
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.
Planning and supervising construction of access routes and forest roads
staying human
This work happens in the physical world: construction of access routes, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Plan and supervise construction of access routes and forest roads.” (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: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Road layout can be drafted from maps, but supervising the build means being on the route as it is cut.
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 3/4.
Monitoring activities of logging companies and contractors
staying human
This work happens in the physical world: activities of logging companies, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Monitor activities of logging companies and contractors.” (O*NET task statement)
How this row was scored
Exposure score: 19 out of 100 (12–26 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: Checking what loggers and contractors are actually doing means going and looking at the cut.
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.
Surveying, measuring and map access roads and forest areas, burns, cut-over areas, experimental plots and timber sales sections
staying human
This work happens in the physical world: map access roads, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Survey, measure, and map access roads and forest areas such as burns, cut-over areas, experimental plots, and timber sales sections.” (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: Surveying and measuring forest areas requires being out on the ground, even though mapping the results is desk work.
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.
Selecting and marking trees for thinning or logging
staying human
This work happens in the physical world: trees, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Select and mark trees for thinning or logging, drawing detailed plans that include access roads.” (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: Marking individual trees for cutting means walking the stand and judging each tree, even though plans are drawn at a desk.
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.
Supervising forest nursery operations, timber harvesting, land use activities, livestock grazing and disease or insect control programs
staying human
This work happens in the physical world: forest nursery operations, timber harvesting, land use activities, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Supervise forest nursery operations, timber harvesting, land use activities such as livestock grazing, and disease or insect control programs.” (O*NET task statement)
How this row was scored
Exposure score: 7 out of 100 (0–14 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Supervising nursery, harvesting and grazing operations means being where the work is happening.
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.
Measuring distances, clean sightlines and recording data to help survey crews
staying human
This work happens in the physical world: distances, clean sightlines and recording data, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Measure distances, clean sightlines, and record data to help survey crews.” (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: Chaining distances and clearing sightlines for a survey crew is physical fieldwork.
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.
Performing reforestation or forest renewal
staying human
This work happens in the physical world: reforestation, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Perform reforestation or forest renewal, including nursery and silviculture operations, site preparation, seeding and tree planting programs, cone collection, and tree improvement.” (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: Site preparation, seeding and tree planting is physical outdoor work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Conducting laboratory or field experiments with plants
staying human
This work happens in the physical world: laboratory, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Conduct laboratory or field experiments with plants, animals, insects, diseases, and soils.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–7 allowing for uncertainty): minimal exposure, high confidence, and it moved between repeat runs, so the range is widened.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Running experiments with plants, animals and soils is hands-on laboratory and field 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 3/4.
Inspecting trees and collecting samples of plants
staying human
This work happens in the physical world: trees, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Inspect trees and collect samples of plants, seeds, foliage, bark, and roots to locate insect and disease damage.” (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: Inspecting trees and collecting bark, seed and root samples is hands-on fieldwork.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.

What this job pays, and how many people do it

Median pay: $54,560a 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: 30,410in 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: computer databases in, a record out. The rows above are exactly that shape: developing and maintaining computer databases and mapping forest tract data using digital mapping systems. What it cannot do is be there in the room, and that is still where trees 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: thinning and spacing trees and controlling weeds and undergrowth is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 14% of this job's task weight sits in rows the software is already learning, 2% in rows that change shape rather than disappear, and 83% in rows it is nowhere near. That is the position, measured across 20 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. Developing and maintaining computer databases 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 computer databases, 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 trees 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 issuing fire permits, timber permits and other forest use licenses. 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 and conservation technicians (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was foresters: only about 10% 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 14% of the task list in the top band, and “thin and space trees and control weeds and undergrowth, using manual tools…” 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.

Foresters
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already provide forestry education and general information, advice, and recommendations to woodlot owners, community…, and their equivalent is to provide advice and recommendations, as a consultant on forestry issues, to private woodlot…. Across both published task lists that is about 10% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 10% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
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 manage forest protection activities, and their equivalent is to inspect and test fire protection or fire detection systems to verify that such…. 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 Workers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already thin and space trees and control weeds and undergrowth, using manual tools and…, and their equivalent is to thin or space trees, using power thinning saws. 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. It is a pay cut, in those words: $43,680 against your $54,560, 19.9% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice. And it is a narrow door: about 6,050 of those jobs against 30,410 of yours (OEWS May 2025), 20% 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: 14% of its task weight, across 20 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 thinning and spacing trees and controlling weeds and undergrowth 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.

If you run a team doing this job

If you run a team doing this job, the conversation you owe them is the one on this page, and sooner than feels comfortable. Show them the ledger rather than a reassurance: the rows moving toward the software are developing and maintaining computer databases, and the rows that are not are where you want your people visible. Ask each of them to do the this-week move and bring the list to your next one-to-one. It turns a rumour into a piece of work, and it tells you which parts of your team's week are actually at stake. And say the thing out loud that a team lead usually leaves unsaid: a shrinking team is your exposure too, so do the move yourself as well.

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Agricultural and fishing trades n.e.c. 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

In UK official statistics this job is counted as Agricultural and fishing trades n.e.c.. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.

Your route through this

Where to go next, and what it costs

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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.

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

Nothing Collab365 runs is built for forest / conservation technicians, and we are not going to point you at the nearest one and call it a fit.

There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 14% of the work on this page is already inside what they can do.

Try The AI Authority free →

7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.

The AI Authority is a general community about working with AI, not a course for forest / conservation technicians. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.

Noted, and thank you. We’ll email you if a Space for forest / conservation technicians launches. Nothing else.

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No Space for forest / conservation technicians yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

Collab365 launches new communities where the need is real. If one for forest / conservation technicians existed, with researched problems, courses and people in the same boat, would you want in?

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We use your email address for one thing: to tell you if a Space for forest / conservation technicians launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.

Questions people ask about this job

Will AI replace Forest and Conservation Technicians?: Not as a job, but it is already doing parts of the work. Across the 20 official task statements scored for Forest and Conservation Technicians (United States, SOC 19-4071), 14% 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 and Conservation Technicians” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Develop and maintain computer databases” (83/100, very high); “Map forest tract data using digital mapping systems” (75/100, high); “Keep records of the amount and condition of logs taken to mills” (69/100, high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Forest and Conservation Technicians” stay human?: About 83% 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: “Inspect trees and collect samples of plants, seeds, foliage, bark, and roots to locate insect and disease damage” (0/100, minimal); “Conduct laboratory or field experiments with plants, animals, insects, diseases, and soils” (0/100, minimal); “Perform reforestation or forest renewal, including nursery and silviculture operations, site preparation, seeding and tree planting programs, cone collection…” (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 and Conservation Technicians” do about AI?: Start from the ledger rather than the headline: 14% of this job's weighted core work is exposed, and roughly 83% 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 and Conservation Technicians calculated?: Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 20 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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Cite this

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.