Headline risk
24%
Moderate RiskPressers, textile, garment, and related materials
United States AI Work Index tracks this occupation on the shared structural baseline and then layers on local demand resilience, wages, and confidence.
Why This Score
Share of job tasks that overlap with current AI capabilities
Median annual wage
Projected employment change over 10 years
Typical preparation needed for this occupation
Occupation profile
Press or shape articles by hand or machine.
Task evidence
100% weighted task match · 0% effective coverage
Scores combine AI task overlap, human advantages, and local demand. How it works
United States Now
Median Wage
USD 33,880
Employment 2024
28.4K
Projected Change (2024–34)
-13.5%
Openings (2024–34)
2.8K
Wage distribution
Demand outlook
Projections published, but no prose outlook available.
Role Profile
Tasks
- 1. Operate steam, hydraulic, or other pressing machines to remove wrinkles from garments and flatwork items, or to shape, form, or patch articles. AI use: 0%
- 2. Remove finished pieces from pressing machines and hang or stack them for cooling, or forward them for additional processing. AI use: 0%
- 3. Straighten, smooth, or shape materials to prepare them for pressing. AI use: 0%
- 4. Lower irons, rams, or pressing heads of machines into position over material to be pressed. AI use: 0%
- 5. Push and pull irons over surfaces of articles to smooth or shape them. AI use: 0%
- 6. Finish pleated garments, determining sizes of pleats from evidence of old pleats or from work orders, using machine presses or hand irons. AI use: 0%
Technologies
Requirements
Work context
Worker profile
Median age 42.1 · 40M employed
Under 25: 8% · 25–54: 71% · 55+: 21%
Related
No direct US role match is available yet for this occupation.
Source coverage
11/11 source families · O*NET 30.2 / OEWS 2024 / ORS 2025 / OOH 2025-08-28 / Projections 2024-34 / CPS 2025 / Anthropic task penetration
Mapping quality
major_group_fallback · employment series present
Narrative & sources
Published limitations
This page shows the local country layer, not realised individual job outcomes. The global structural baseline is shared across countries; only the local demand and wage layer changes here.
Built from O*NET occupation descriptions, task statements, technology skills, work context, Job Zones, Anthropic task penetration, BLS OEWS wages, BLS projection tables, BLS ORS requirements, BLS OOH narrative content, BLS skills data, and BLS CPS occupation age tables.