Will AI replace Pressers, Textile, Garment, and Related Materials?
Most of the work in Pressers, Textile, Garment, and Related Materials still leans on things AI struggles with — research rates its theoretical AI reach at only ~1%, and real-world use lower still.
O*NET-SOC 51-6021
How your 20 core tasks split
Top = what GPT-4 judged AI could speed up. Bottom = how much AI was actually used for these tasks (Anthropic's March 2026 report, usage from Aug & Nov 2025). The gap is the real story.
Back in 2023, GPT-4 judged AI could, in theory, assist with a relatively low share of this job's tasks (~1%). By late 2025, real-world AI use had reached about 0% of its task activity (still rare). The gap between that 2023 forecast and today is the real story.
Where this job sits among 738 jobs
Each dot is one of 738 U.S. jobs. Right = AI can do more of it. Up = AI is actually used more.
The signals here line up
Theoretical reach (~1%), real-world use (~0%) and the task-level picture mostly agree — so this read is more reliable than for jobs where the signals contradict each other. Even so, AI-risk estimates shift by model (a 2026 study saw the "high-risk" share swing 2.7%–51.5%), so treat these as directional, not destiny.
See all 20 tasks, ratedBased on real task-level AI scores — click to collapse
- None — AI cannot fully do any core task alone yet.
- No tasks in this middle tier.
- Hang, fold, package, and tag finished articles for delivery to customers.
- Operate steam, hydraulic, or other pressing machines to remove wrinkles from garments and flatwork items, or to shape, form, or patch articles.
- Straighten, smooth, or shape materials to prepare them for pressing.
- Remove finished pieces from pressing machines and hang or stack them for cooling, or forward them for additional processing.
- Finish pleated garments, determining sizes of pleats from evidence of old pleats or from work orders, using machine presses or hand irons.
- Lower irons, rams, or pressing heads of machines into position over material to be pressed.
- Identify and treat spots on garments.
- Shrink, stretch, or block articles by hand to conform to original measurements, using forms, blocks, and steam.
- Finish fancy garments such as evening gowns and costumes, using hand irons to produce high quality finishes.
- Push and pull irons over surfaces of articles to smooth or shape them.
- Finish pants, jackets, shirts, skirts and other dry-cleaned and laundered articles, using hand irons.
- Slide material back and forth over heated, metal, ball-shaped forms to smooth and press portions of garments that cannot be satisfactorily pressed with flat pressers or hand irons.
- Select appropriate pressing machines, based on garment properties such as heat tolerance.
- Spray water over fabric to soften fibers when not using steam irons.
- Position materials such as cloth garments, felt, or straw on tables, dies, or feeding mechanisms of pressing machines, or on ironing boards or work tables.
- Moisten materials to soften and smooth them.
- Clean and maintain pressing machines, using cleaning solutions and lubricants.
- Press ties on small pressing machines.
- Block or shape knitted garments after cleaning.
- Activate and adjust machine controls to regulate temperature and pressure of rollers, ironing shoes, or plates, according to specifications.
How we measured this — and how fresh it is
AI's theoretical reach data: 2023
From GPTs-are-GPTs (Eloundou et al.), where GPT-4 rated how much of each task an AI tool could meaningfully speed up. This is the most recent open, commercially-usable occupation-level potential dataset — it dates to 2023. Newer multi-model re-runs exist but swing wildly (one 2026 study saw "high-risk" jobs range 2.7%–51.5% by model) and aren't openly licensed, so we show the stable 2023 baseline and pair it with newer real-world data.
Real-world AI use 2026 report
From the Anthropic Economic Index, which observes how real Claude conversations map onto each occupation's tasks. Published in Anthropic's March 2026 labor-market report, based on usage measured in Aug & Nov 2025 (Sonnet 4 / 4.5).
Task list & ratings O*NET 30.3
Tasks come from O*NET 30.3. Each task's "AI can do / speeds up / still on you" tier uses the real task-level exposure scores from GPTs-are-GPTs (E1 / E2 / E0) — not a guess from keywords.
Sources: O*NET 30.3 (CC BY 4.0) · GPTs-are-GPTs (MIT, 2023) · Anthropic Economic Index (CC BY, Aug & Nov 2025). Page compiled June 2026. "O*NET" is a trademark of the U.S. Department of Labor.
This page is for general informational purposes only and is not career, financial, or employment advice. AI exposure reflects research estimates of task overlap, not predictions about any individual's job, employer, or future employment.