Opportunity Data Occupation AI Exposure Index

How exposed is each occupation to AI-driven task automation? Lower composite scores = more protected from displacement.

772
Occupations scored from O*NET work characteristics
122.6M
Workers employed in scored occupations (BLS OES May 2024)
9
O*NET work characteristic variables across 3 dimensions
4
Tiers: AI-protected, Moderate, AI-exposed, Highly exposed

How the Index Works

The index measures each occupation's net exposure to AI displacement by balancing digital work intensity against two protective factors. Each occupation is scored directly from O*NET 23.1 survey data and paired with BLS national employment estimates. This is the foundation layer: the academic program index aggregates these occupation scores to the program level via the NCES crosswalk.

Digital Intensity

How computer- and data-intensive is the core work?

Interacting With Computers
Analyzing Data or Information
Processing Information

Human Contact

Does the job require empathy, direct care, or constant interpersonal contact?

Assisting and Caring for Others
Working Directly with the Public
Contact With Others

Physical Anchor

Is the work grounded in physical presence, manual skill, or safety responsibility?

Spend Time Using Hands
Spend Time Sitting (inverted)
Responsible for Others' Health and Safety
DII v2 = ( Digital Intensity − Human Contact − Physical Anchor + 2 ) / 3

Each dimension is computed from O*NET 23.1 survey data, normalized to a 0-1 scale. Employment figures are from the BLS Occupational Employment and Wage Statistics, May 2024 national estimates. A score of 0 means fully protected; 1 means fully exposed.

Data sources: O*NET 23.1 (U.S. Department of Labor), BLS Occupational Employment and Wage Statistics (May 2024). Full methodology, limitations, and reproducibility details →

Opportunity Data Occupation AI Exposure Index

Search by occupation name or SOC code. Sort by any dimension. 772 occupations with BLS employment estimates.

AI exposure level
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772 occupations
Reading this table: AI exposure scores closer to 0 mean the occupation's tasks are harder for AI to perform. High human-contact and physical-task scores indicate work that requires in-person, hands-on skills, making those occupations more resistant to AI automation. Employment shows BLS May 2024 national estimates.

Download and Citation

The full index is available as a CSV file for research and institutional use.

Suggested citation:
Rowe, B. (2026). Opportunity Data Occupation AI Exposure Index. Opportunity Data. opportunitydata.org/ai-exposure-occupations

Methodology documentation: GitHub repository (soc_dii_v2_methodology.md). Data sources: O*NET 23.1, BLS OES May 2024.

Data Sources
O*NET 23.1 (U.S. Department of Labor) · BLS OES (May 2024)
Index construction: 9 O*NET work characteristic variables across 3 dimensions, scored directly at the occupation (SOC) level. Employment from BLS national estimates.
Opportunity Data · AI Exposure Index