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Craft and related trades workers · code 7532

Garment and related pattern-makers and cutters

AI can grade a pattern into ten sizes. Judging how the fabric stretches still takes experience.

Drawing pattern pieces on screen and working out pattern sizes are mostly out of AI's reach. It barely touches ten of the twelve tasks, including cutting leather and trimming finished products.

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

Garment and related patternmakers and cutters create precision master patterns for the production of garments and other textile, leather or fur products. They mark, cut, shape and trim textiles, light leather and other materials according to blueprints or specifications in the manufacture of garments, hats and caps, gloves and miscellaneous products. Tasks include - (a) creating a master pattern for each size within a range of garment sizes, using charts, drafting instruments, computers and/or grading devices; (b) creating the blueprint or pattern pieces for a particular apparel design with the aid of a computer; (c) calculating dimensions of patterns according to sizes, considering stretching of material; (d) drawing details on outlined parts to indicate where parts are to be joined, as well as the positions of pleats, pockets, buttonholes on garments, decorative stitching on shoe parts or eyelets on canvas products, using computers or drafting instruments; (e) positioning templates or measuring materials to locate specified points of cuts or to obtain maximum yields, and marking fabric accordingly; (f) laying out the master pattern on fabric and cutting sample patterns; (g) testing patterns by making and fitting sample garments; (h) placing patterns on top of layers of fabric and cutting fabric following patterns, using electric or manual knives, cutters or computer numerically controlled cutting devices; (i) cutting fabric or fur pelts to make parts for garments and other fur articles; (j) trimming excess material or cutting threads off finished products, such as cutting loose ends of a finished product; (k) positioning leather on the cutting bed of machines, maximizing usage according to skin grain, skin flaws and skin stretch; (l) performing patternmaking, marking and cutting tasks in the manufacture of other products such as soft furnishings and canvas goods. Examples of the occupations classified here: - Fur patternmaker - Garment cutter - Garment patternmaker - Glove cutter Some related occupations classified elsewhere: - Textile patternmaking machine operator - 8159

  • clothing product grader
  • wearing apparel patternmaker
  • leather goods patternmaker
  • clothing CAD patternmaker
  • clothing cutter
  • leather goods CAD patternmaker
  • digital prototyper

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

83 / 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

91 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 12 tasks for how much of it AI could do. They average 16.8 out of 100, so it holds up at 100 − 16.8 = 83.2.

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 52 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?

Garment and related pattern-makers and cutters 83

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.

  • Grading patterns across sizes with software.
  • Calculating dimensions and fabric use.
  • Automated cutting of layered fabric.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Testing patterns on sample garments.
  • Judging stretch, grain and drape.
  • Cutting leather and fur around flaws.

Both

What's arriving

New work that only exists because the AI does.

  • 3D virtual samples before anything is cut.
  • Made-to-measure patterns from body scans.
  • Less waste through smarter cutting.

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

27 could do without you

It could take over very little of the work on its own. It could help with 60 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Some

52 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 34 out of 100. The higher it is, the longer the robot waits.

52 How
hands-on
34 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 3 US jobs .

finger skill 44.6 · hand skill 42.9 · cramped spaces 13.7 · 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 25, against an average of 17. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 25

    Creating the blueprint or pattern pieces for a particular apparel design with the aid of a computer

    Low
  2. 25

    Calculating dimensions of patterns according to sizes, considering stretching of material

    Low
  3. 22

    Drawing details on outlined parts to indicate where parts are to be joined, as well as the positions of pleats, pockets, buttonholes on garments, decorative stitching on shoe parts or eyelets on canvas products using computers or drafting instruments

    Very Low
  4. 17

    Testing patterns by making and fitting sample garments

    Very Low
  5. 15

    Positioning templates or measuring materials to locate specified points of cuts or to obtain maximum yields and mark fabric accordingly

    Very Low
  6. 15

    Creating a master pattern for each size within a range of garment sizes, using charts, drafting instruments, computers, and/or grading devices

    Very Low
  7. 15

    Performing patternmaking, marking and cutting tasks in the manufacture of other products such as soft furnishings and canvas goods.

    Very Low
  8. 14

    Placing patterns on top of layers of fabric and cutting fabric following patterns, using electric or manual knives, cutters or computer numerically controlled cutting devices

    Very Low
  9. 14

    Cutting fabric or fur pelts to make parts for garments and other fur articles

    Very Low
  10. 14

    Laying out master pattern on fabric and cutting sample pattern

    Very Low
  11. 14

    Positioning leather on cutting bed of machine, maximizing usage according to skin grain, skin flaws and skin stretch

    Very Low
  12. 13

    Trimming excess material or cutting threads off finished products, such as cutting loose ends of a finished product

    Very Low

12 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
60
AI does it for you
27

Sidekick wins, by 33 points.

How good a match is this?

One Australian job matches, but only partly: not everyone in it does this job. 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 3932

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

53
47

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 pattern makers and cutters be worried?

Short answer: not really. Put the kettle on.

Parts of it are changing. Computer pattern making and calculating dimensions are the most exposed tasks, both Low; testing samples and cutting rate Very Low. Pattern software keeps getting smarter, so the craft shifts towards fit, testing and judgement.

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 84. This one is below the family average.

Pit this against any other job →