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Craft and related trades workers · code 7317

Handicraft workers in wood, basketry and related materials

AI can dream up a carving. The chisel is still in your hand.

AI barely touches any of the eleven tasks, from preparing wood, straw and reeds to carving, weaving and painting the finished piece.

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

Handicraft workers in wood, basketry and related materials apply traditional techniques such as seasoning or impregnation to prepare wood, straw, rattan, reeds, clay, shells and other materials, and carve, mould, assemble, weave or paint and decorate various articles for personal or household use or for decorative purposes. Basketry weavers, brush makers and related workers select and prepare materials such as bristles, nylon, fibre, bass, whisker and wire to make wicker furniture, brushes and brooms, and to weave various kinds of baskets. Tasks include - (a) preparing wood, straw, rattan, reeds, shells or similar materials; (b) carving floral and artistic designs on wooden surfaces for decorative purposes; (c) painting freehand decorative designs on glass and pottery or porcelain ware; (d) carving, assembling, weaving, painting and decorating various articles for personal or household use such as salad bowls, serving spoons, cutting-boards, trays, vases, jugs, baskets, straw hats, straw mats and similar objects; (e) carving, assembling, weaving and painting various decorative articles such as statues and other sculptures, chess pieces, jewellery and similar objects; (f) making wicker furniture from peeled and softened rattan, reeds, rushes, willow branches and similar materials; (g) making various kinds of baskets by interlacing osier, rattan, reeds, rushes and similar materials; (h) forming bottoms of baskets by interlacing strips of rattan, wood veneer or other materials with frameworks of rods of material such as willow; (i) inserting rods around bottom edges between woven sections of bottom and bending them upright to serve as framework for sides; (j) selecting and preparing brush materials such as bristles, nylon, fibres and wire, and setting them in brush base; (k) selecting and preparing materials such as broom corn, bass, whisker and fibre, and fastening them to broom handles. Examples of the occupations classified here: - Basket maker - Brush maker - Reed weaving handicraft worker - Wicker furniture maker - Wooden articles handicraft worker Some related occupations classified elsewhere: - Machine operator (wood products) - 7523

  • artisan papermaker
  • woodcarver
  • basketmaker
  • wicker furniture maker
  • toymaker
  • brush maker

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

86 / 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

54 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 11 tasks for how much of it AI could do. They average 13.6 out of 100, so it holds up at 100 − 13.6 = 86.4.

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 50 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?

Handicraft workers in wood, basketry and related materials 86

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.

  • Design ideas and sketches.
  • Product photos and online listings.
  • Pricing and orders.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Carving, weaving and finishing by hand.
  • Choosing and preparing natural materials.
  • Making one-off pieces.

Both

What's arriving

New work that only exists because the AI does.

  • Selling to buyers worldwide online.
  • Commissions and personalised pieces.
  • Teaching crafts online.

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

21 could do without you

It could take over very little of the work on its own. It could help with 58 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Some

50 how hands-on

A fair part of this job is hands-on, so robots would be needed for that part, not just software.

So could a robot do the physical part?

More fine handwork, and more time in cramped or awkward spots, than a typical hands-on job. That is exactly what machines find hardest: they do best in tidy, predictable places.

Robots hate fiddly work in awkward spaces. On that measure, this job scores 40 out of 100. The higher it is, the longer the robot waits.

50 How
hands-on
40 How fiddly
and cramped

Kind of hands-on work Hands-on, fiddly work in tight spaces

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, matched to one US job .

finger skill 58.9 · hand skill 50 · cramped spaces 9.7 · averaged · rule: hands-on 50+ · fiddly 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 15, against an average of 14. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 15

    Painting freehand decorative designs on glass and pottery or porcelain ware

    Very Low
  2. 15

    Carving, assembling, weaving, painting and decorating various articles for personal or household use such as salad bowls, serving-spoons, cutting-boards, trays, vases, jugs, baskets, straw hats, straw mats and similar objects

    Very Low
  3. 15

    Carving, assembling, weaving and painting various decorative articles such as statues and other sculptures, chess pieces, jewellery and similar objects

    Very Low
  4. 14

    Making various kinds of baskets by interlacing osier, rattan, reeds, rushes or similar materials

    Very Low
  5. 14

    Carving floral and artistic designs on wooden surfaces for decorative purposes

    Very Low
  6. 14

    Making wicker furniture from peeled and softened rattan, reeds, rushes, willow branches and similar materials

    Very Low
  7. 14

    Selecting and preparing brush materials, such as bristles, nylon, fibres and wire, and setting them in brush base

    Very Low
  8. 13

    Forming bottom of basket by interlacing strips of rattan, wood veneer or other material with framework of rods of material such as willow

    Very Low
  9. 13

    Inserting rods around edge of bottom between woven sections of bottom and bending them upright to serve as framework for sides

    Very Low
  10. 12

    Preparing wood, straw, rattan, reeds, shells, or similar materials

    Very Low
  11. 12

    Selecting and preparing materials, such as broom corn, bass, whisker and fibre, and fastening them to broom handles.

    Very Low

11 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
58
AI does it for you
21

Sidekick wins, by 37 points.

How good a match is this?

2 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 2114, 3942

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?

Out of every 100 conversations

58
42

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. This job makes up 0.14% of the conversations.

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 woodcarvers and basket makers be worried?

Short answer: not really. Put the kettle on.

No. Every task rates Very Low, from carving to basket weaving and brush making. AI helps with designs, photos and selling online; the craft itself is the product.

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 65 jobs in the “Craft and related trades workers” family, averaging 83. This one is above the family average.

Pit this against any other job →