Headline risk
5%
Very Low RiskComputer, automated teller, and office machine repairers
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
Weighted task overlap from O*NET
Median annual from BLS OEWS
BLS employment projections
O*NET job zone level
Occupation profile
Repair, maintain, or install computers, word processing systems, automated teller machines, and electronic office machines, such as duplicating and fax machines.
Task evidence
100% weighted task match · 16% effective coverage
Method contract
structural_pressure = exposure × (1 - bottleneck)
headline_risk = structural_pressure × (1 - country_demand_resilience)
United States Now
Median Wage
USD 46,860
Employment 2024
79.1K
Projected Change
-0.9%
Openings
7.6K
Wage distribution
Demand outlook
Projections published, but no prose outlook available.
Role Profile
Tasks
- 1. Converse with customers to determine details of equipment problems. AI 0%
- 2. Reassemble machines after making repairs or replacing parts. AI 0%
- 3. Advise customers concerning equipment operation, maintenance, or programming. AI 0%
- 4. Reinstall software programs or adjust settings on existing software to fix machine malfunctions. AI 96%
- 5. Travel to customers' stores or offices to service machines or to provide emergency repair service. AI 0%
- 6. Operate machines to test functioning of parts or mechanisms. AI 0%
Technologies
Requirements
Work context
Worker profile
Median age 42.6 · 132K employed
Under 25: 9% · 25–54: 64% · 55+: 27%
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.