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Craft and related trades workers · code 7318

Handicraft workers in textile, leather and related materials

AI can generate a pattern. Weaving it by hand is still the craft.

AI barely touches any of the fourteen tasks, from grading and washing fibres to spinning, dyeing, crocheting and braiding by hand.

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

Handicraft workers in textile, leather and related materials apply traditional techniques and patterns to produce woven fabrics, knitted, embroidered, woven and other garments and articles for household use, as well as traditional footwear, handbags, belts and other accessories. Tasks include - (a) spinning and dyeing with natural dyestuffs, wool, cotton and other fibres; (b) lace-making, and weaving, knitting or embroidering various garments and articles for household use; (c) preparing and dyeing hides with natural dyestuffs and making traditional footwear or handbags, belts and other accessories; (d) spinning and winding yarn by hand; (e) drawing warp threads into looms by hand; (f) weaving plain or figured cloth, tapestry, lace, carpets or other fabrics on hand looms; (g) making carpets by using a knotting technique; (h) knitting garments and other articles on hand-operated machines or by hand; (i) crocheting or making braid by hand; (j) making nets by hand; (k) grading and classifying natural textile fibres; (l) washing wool fibres; (m) cleaning and fluffing textile fibres; (n) forming fibres into sliver, combing them, combining sliver into sliver laps or forming sliver into rove. Examples of the occupations classified here: - Carpets handicraft worker - Carpet weaver - Cloth weaver - Knitter - Leather handicraft worker - Loom threader - Textile fibre comber - Textile fibre drawer - Textile fibre rover - Textiles handicraft worker - Thread and yarn spinner Some related occupations classified elsewhere: - Textile fibre combing machine operator - 8151 - Knitting machine operator - 8152 - Weaving machine operator - 8152

  • fishing net maker
  • carpet handicraft worker
  • leather goods artisanal worker
  • knitter
  • carpet weaver
  • weaver

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

87 / 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

51 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 14 tasks for how much of it AI could do. They average 13.3 out of 100, so it holds up at 100 − 13.3 = 86.7.

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 59 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 textile, leather and related materials 87

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.

  • Pattern and design ideas.
  • Selling and marketing online.
  • Pricing and orders.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Spinning, weaving and knitting by hand.
  • Dyeing with natural materials.
  • Knowing fibres by feel.

Both

What's arriving

New work that only exists because the AI does.

  • Selling handmade goods to buyers worldwide.
  • Provenance stories customers pay for.
  • Teaching crafts through videos and workshops.

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

16 could do without you

It could take over very little of the work on its own. It could help with 57 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Some

59 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?

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 37 out of 100. The higher it is, the longer the robot waits.

59 How
hands-on
37 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, matched to one US job .

finger skill 44.6 · hand skill 44.6 · cramped spaces 20.8 · 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 21, against an average of 13. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 21

    Grading and classifying natural textile fibres

    Very Low
  2. 15

    Spinning and dyeing with natural dyestuffs, wool, cotton and other fibres

    Very Low
  3. 15

    Preparing and dyeing hides with natural dyestuffs and making traditional footwear or handbags, belts and other accessories

    Very Low
  4. 14

    Lace-making and weaving, knitting or embroidering various garments and articles for household use

    Very Low
  5. 14

    Making carpets by using a knotting technique

    Very Low
  6. 14

    Weaving plain or figured cloth, tapestry, lace, carpet or other fabrics on hand looms

    Very Low
  7. 14

    Knitting garments and other articles on hand-operated machine or by hand

    Very Low
  8. 14

    Making nets by hand

    Very Low
  9. 13

    Forming fibres into sliver, combing them, combining sliver into sliver laps or forming sliver into rove.

    Very Low
  10. 12

    Spinning and winding yarn by hand

    Very Low
  11. 12

    Cleaning and fluffing textile fibres

    Very Low
  12. 11

    Drawing warp threads into loom by hand

    Very Low
  13. 11

    Crocheting or making braid by hand

    Very Low
  14. 8

    Washing wool fibres

    Very Low

14 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
57
AI does it for you
16

Sidekick wins, by 41 points.

How good a match is this?

One Australian job matches this one closely, so this is about as reliable as it gets here.

estimated Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.8-observed-use matched to Australian jobs 2114

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

39
61

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 textile and leather craftspeople be worried?

Short answer: not really. Put the kettle on.

No. Every task rates Very Low, from grading fibres to weaving, knitting and dyeing by hand. AI can help with designs, selling online and pricing. Handmade is the whole point of 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 →