Headline risk
2%
Very Low RiskPaperhangers
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
Cover interior walls or ceilings of rooms with decorative wallpaper or fabric, or attach advertising posters on surfaces such as walls and billboards. May remove old materials or prepare surfaces to be papered.
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 48,260
Employment 2024
2.3K
Projected Change (2024–34)
5.3%
Openings (2024–34)
0.2K
Wage distribution
Demand outlook
Projections published, but no prose outlook available.
Role Profile
Tasks
- 1. Smooth strips or sections of paper with brushes or rollers to remove wrinkles and bubbles and to smooth joints. AI use: 0%
- 2. Place strips or sections of paper on surfaces, aligning section edges and patterns. AI use: 0%
- 3. Check finished wallcoverings for proper alignment, pattern matching, and neatness of seams. AI use: 0%
- 4. Apply adhesives to the backs of paper strips, using brushes, or dunk strips of prepasted wallcovering in water, wiping off any excess adhesive. AI use: 0%
- 5. Trim excess material at ceilings or baseboards, using knives. AI use: 0%
- 6. Trim rough edges from strips, using straightedges and trimming knives. AI use: 0%
Technologies
Requirements
Work context
Worker profile
Median age 43.1 · 485K employed
Under 25: 11% · 25–54: 65% · 55+: 25%
Related
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.