Headline risk
2%
Very Low RiskRiggers
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
Set up or repair rigging for construction projects, manufacturing plants, logging yards, ships and shipyards, or for the entertainment industry.
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 62,060
Employment 2024
24.6K
Projected Change (2024–34)
3.2%
Openings (2024–34)
2.5K
Wage distribution
Demand outlook
Projections published, but no prose outlook available.
Role Profile
Tasks
- 1. Test rigging to ensure safety and reliability. AI use: 0%
- 2. Signal or verbally direct workers engaged in hoisting and moving loads to ensure safety of workers and materials. AI use: 0%
- 3. Tilt, dip, and turn suspended loads to maneuver over, under, or around obstacles, using multi-point suspension techniques. AI use: 0%
- 4. Control movement of heavy equipment through narrow openings or confined spaces, using chainfalls, gin poles, gallows frames, and other equipment. AI use: 0%
- 5. Attach loads to rigging to provide support or prepare them for moving, using hand and power tools. AI use: 0%
- 6. Select gear, such as cables, pulleys, and winches, according to load weights and sizes, facilities, and work schedules. AI use: 0%
Technologies
Requirements
Work context
Related
Source coverage
10/11 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.