How much of this occupation’s work can be materially affected by current AI systems.
How likely exposure is to translate into reduced human demand.
Includes provisional estimates for AI adoption pressure and labour-market resilience. How this is measured
Confidence reflects task coverage, mapping and capability-evidence quality, and how much of the score rests on provisional inputs.
What is driving the score?
Occupation scores are built from the task mix—not a single prediction about a job title.
Where AI can do more
Routine, digitized, and highly repeatable tasks face the greatest pressure.
- Straighten, smooth, or shape materials to prepare them for pressing.81
- Push and pull irons over surfaces of articles to smooth or shape them.81
- Spray water over fabric to soften fibers when not using steam irons.81
- Insert heated metal forms into ties and touch up rough places with hand irons.81
Where people still matter
These tasks score lowest on automation feasibility—physical presence, judgement, accountability and real-world variability all resist end-to-end automation.
- Brush materials made of suede, leather, or felt to remove spots or to raise and smooth naps.01
- Finish pants, jackets, shirts, skirts and other dry-cleaned and laundered articles, using hand irons.02
- Measure fabric to specifications, cut uneven edges with shears, fold material, and press it with an iron to form a heading.03
- Operate steam, hydraulic, or other pressing machines to remove wrinkles from garments and flatwork items, or to shape, form, or patch articles.04
- Remove finished pieces from pressing machines and hang or stack them for cooling, or forward them for additional processing.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
Laundry and Dry-Cleaning Workers
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Related work
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Related work
Compare these careers →Adoption and labour-market outlook
Structural factors are kept separate from raw capability so you can see what actually resists automation. Adoption pressure and labour-market resilience are still provisional models—25% of this occupation’s replacement-risk weight rests on them.
O*NET 30.3 occupational data interpreted through the JobsVsAI capability, automation and structural-constraint models.
