Skip to content
man bot
Compare

Skilled agricultural, forestry and fishery workers · code 6210

Forestry and related workers

AI can spot a forest fire from a satellite. Putting it out still takes a crew.

AI barely touches any of the ten tasks, from felling trees with a chainsaw and moving logs to watching for fires and training new workers.

What they actually do (and what else they get called)

Forestry and related workers plan, organize and perform operations to cultivate, conserve and exploit natural and plantation forests. Tasks include - (a) assessing sites for reforestation, selecting seedlings and planting trees using manual planting tools and establishing and caring for forest stands; (b) locating trees to be felled and estimating volume of timber; (c) operating chainsaw and other power saws to thin young forest stands, trim, top and fell trees and saw them into logs; (d) shaping rough wooden products from logs at felling site; (e) stacking logs and loading them in chutes or floating them down rivers; (f) keeping watch to detect forest fires, participating in fire fighting operations, completing fire fighting reports and maintaining fire fighting equipment; (g) controlling weeds and undergrowth in regenerating forest stands using manual tools and chemicals; (h) operating and maintaining a skidder, bulldozer or other prime mover to pull a variety of scarification or site preparation equipment over areas to be regenerated; (i) collecting seed cones, pruning trees, assisting in planting surveys and marking trees for subsequent operations; (j) training and supervising other workers in forestry procedures, including forestry labourers and plant operators. Examples of the occupations classified here: - Charcoal burner - Logger - Logging climber - Skilled forestry worker - Timber cruiser - Tree feller Some related occupations classified elsewhere: - Silviculturist - 2132 - Forestry technician - 3143 - Tree faller operator - 8341 - Forestry labourer - 9215

  • forest ranger

Job titles from the EU's ESCO list. Search for any of them on the jobs page and you'll end up here.

88 / 100

Strongly holds up against AI

Most of this job still needs an actual human. AI would mostly be making the tea.

Not the odds you keep your job. It's how much of the everyday work still needs a person, out of 100.

← Robot's turf Human's turf →

The ILO's official verdict: Not Exposed

27 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 10 tasks for how much of it AI could do. They average 11.9 out of 100, so it holds up at 100 − 11.9 = 88.1.

That's the whole sum. No black box, no secret sauce: you can check it against the ILO's study. ILO group “Not Exposed”: None of this job's tasks scored high enough to count.

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Pinch of salt: this number is probably too rosy

It only asks what AI software could do, and this job is 70 out of 100 hands-on. A chatbot can't do that part. A robot might, one day, and nobody measures how close that is.

Man v Bot · compare

Who holds up better?

Forestry and related workers 88

v

Go on, try your partner's job. Or your sibling's. We won't tell.

Man v Bot

Where the bot helps, and where the human wins

Bot

What AI takes

The routine stuff. You may not miss it.

  • Estimating timber volumes from aerial images.
  • Fire detection and reporting.
  • Planning planting and harvesting with maps.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Operating heavy machinery safely on slopes.
  • Fighting fires alongside a crew.
  • Judging which trees to fell and where to plant.

Both

What's arriving

New work that only exists because the AI does.

  • Drone surveys and seeding.
  • Reforestation and carbon projects.
  • Machines with more automated controls.

Written by a human, from the ILO's task ratings below. Yes, we see the irony. Read it as one person's take.

Meet your two rivals

They're not the same machine. AI software can only reach the parts of a job done on a screen or in words. A chatbot can't climb a ladder. A robot might, one day, if it doesn't fall off.

The AI

Lives in a screen. Never had to fix a boiler.

Threat level: Low

20 could do without you

It could take over very little of the work on its own. It could help with 41 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Higher

70 how hands-on

Most of this job needs hands, a body or turning up. That's robot territory, and nobody measures how far robots have got well enough to give you a number. We won't make one up.

So could a robot do the physical part?

Less fine handwork and fewer cramped spaces than a typical hands-on job. That clears one hurdle for machines. It doesn't mean machines are doing this work yet.

Robots hate fiddly work in awkward spaces. On that measure, this job scores 38 out of 100. The higher it is, the longer the robot waits.

70 How
hands-on
38 How fiddly
and cramped

Kind of hands-on work Hands-on, less fiddly work

The small print

They aren't a robot score, and nothing on this page is. Nobody measures how far robots have got well enough to put a number on it. These are the closest thing we have: how hands-on the job is, and how much fiddly, cramped work it takes, which is what Frey and Osborne found hardest for machines.

From O*NET, averaged across 5 US jobs .

finger skill 38.9 · hand skill 41.8 · cramped spaces 33.9 · averaged · rule: hands-on 50+ · fiddly under 39.4

estimated O*NET 31.0 BLS occupational crosswalk chain ISCO-08 to 2010 SOC (Aug 2012, rev. Jun 2015); 2010 to 2018 SOC (Nov 2017); O*NET-SOC 2019 taxonomy data 0.8-observed-use measures chosen by Frey & Osborne (2017) · the grouping is a sorting, not a measurement

The job, task by task

Every job is really a bundle of tasks. Here's this one unpacked, with how much of each AI could do, out of 100. The score is the average, and averages can hide a lot.

AI's favourite task here scores 22, against an average of 12. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 22

    Training and supervising other workers in forestry procedures, including forestry labourers and plant operators.

    Very Low
  2. 17

    Keeping watch to detect forest fires, participating in firefighting operations, completing firefighting reports and maintaining firefighting equipment

    Very Low
  3. 17

    Operating and maintaining a skidder, bulldozer or other prime mover to pull a variety of scarification or site preparation equipment over areas to be regenerated

    Very Low
  4. 13

    Assessing sites for reforestation, selecting seedlings and planting trees using manual planting tools and establishing and caring for forest stands

    Very Low
  5. 12

    Locating trees to be felled and estimating volume of timber

    Very Low
  6. 11

    Collecting seed cones, pruning trees, assisting in planting surveys and marking trees for subsequent operations

    Very Low
  7. 8

    Controlling weeds and undergrowth in regenerating forest stands using manual tools and chemicals

    Very Low
  8. 8

    Shaping rough wooden products from logs at felling site

    Very Low
  9. 7

    Operating chainsaw and other power saws to thin young forest stands, trim, top and fell trees and saw them into logs

    Very Low
  10. 7

    Stacking logs and loading them in chutes or floating them down rivers

    Very Low

10 tasks · the High to Very Low labels are the ILO's

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Sidekick or replacement?

A tool that makes you faster is a very different thing from one that does the task while you watch. Jobs and Skills Australia measured the two separately, which is handy.

AI helps you
41
AI does it for you
20

Sidekick wins, by 21 points.

How good a match is this?

1 Australian jobs match this one, and we've simply averaged them. Treat the numbers as a rough guide.

estimated Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.8-observed-use matched to Australian jobs 3631, 8431, 8999

Meanwhile, in real life…

Everything else here is about what AI could do. This is what people actually did. When they asked Claude for help with this kind of work, did they hand the whole task over, or roll their sleeves up and work through it together?

Tiny sample alert This job makes up under 0.01% of all the conversations: a tiny slice, mostly the paperwork rather than the work. People using AI for a job isn't the same as AI being able to do it.

Out of every 100 conversations

52
48

worked through it together handed it over

The small print

Anthropic publishes this about its own product, and it doesn't feed into any score here. These are people who use Claude, not workers in general, so little use here doesn't mean AI can't reach the job.

These two add up to 100. The bars elsewhere on the page don't, because each is scored on its own, so we don't put them side by side.

observed use Anthropic Economic Index release 2026-06-26, period beginning 2026-05-01 data 0.8-observed-use 2026-05-01 · Anthropic, about its own product

So, should forestry workers be worried?

Short answer: not really. Put the kettle on.

No. Every task rates Very Low; the most exposed is training and supervising other workers. Mapping, fire detection and machinery are becoming more high-tech, but the work happens outdoors, on rough ground.

Worth remembering: we score tasks, not jobs. When AI takes on a task, the job usually reshuffles around it rather than vanishing.

Where this job could lead

None of the new jobs we've written up start from here yet. Could be you're the first. See the jobs AI is creating →

How are the neighbours doing?

There are 18 jobs in the “Skilled agricultural, forestry and fishery workers” family, averaging 83. This one is above the family average.

Pit this against any other job →