Headline risk
67%
Very High RiskTelemarketers
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
Solicit donations or orders for goods or services over the telephone.
Task evidence
100% weighted task match · 37% effective coverage
Scores combine AI task overlap, human advantages, and local demand. How it works
United States Now
Median Wage
USD 34,410
Employment 2024
67.4K
Projected Change (2024–34)
-22.1%
Openings (2024–34)
6.5K
Wage distribution
Demand outlook
Projections published, but no prose outlook available.
Role Profile
Tasks
- 1. Explain products or services and prices, and answer questions from customers. AI use: 96%
- 2. Obtain customer information such as name, address, and payment method, and enter orders into computers. AI use: 99%
- 3. Record names, addresses, purchases, and reactions of prospects contacted. AI use: 0%
- 4. Maintain records of contacts, accounts, and orders. AI use: 0%
- 5. Answer telephone calls from potential customers who have been solicited through advertisements. AI use: 0%
- 6. Deliver prepared sales talks, reading from scripts that describe products or services, to persuade potential customers to purchase a product or service or to make a donation. AI use: 0%
Technologies
Requirements
Work context
Related
No direct US role match is available yet for this occupation.
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
crosswalk_exact · 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.