Headline risk
14%
Low RiskTailors, dressmakers, and custom sewers
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
Design, make, alter, repair, or fit garments.
Task evidence
100% weighted task match · 3% effective coverage
Scores combine AI task overlap, human advantages, and local demand. How it works
United States Now
Median Wage
USD 40,860
Employment 2024
38.8K
Projected Change (2024–34)
-4.5%
Openings (2024–34)
5.0K
Wage distribution
Demand outlook
Projections published, but no prose outlook available.
Role Profile
Tasks
- 1. Sew garments, using needles and thread or sewing machines. AI use: 0%
- 2. Trim excess material, using scissors. AI use: 0%
- 3. Remove stitches from garments to be altered, using rippers or razor blades. AI use: 0%
- 4. Measure parts, such as sleeves or pant legs, and mark or pin-fold alteration lines. AI use: 0%
- 5. Repair or replace defective garment parts, such as pockets, zippers, snaps, buttons, and linings. AI use: 0%
- 6. Make garment style changes, such as tapering pant legs, narrowing lapels, and adding or removing padding. AI use: 0%
Technologies
Requirements
Work context
Worker profile
Median age 54.6 · 60K employed
Under 25: 0% · 25–54: 47% · 55+: 55%
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
title_match · 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.