About the AI Risk Radar
The AI Risk Radar is a data-driven tool that evaluates the relative susceptibility of various occupations and industries to automation and disruption from artificial intelligence. Designed for policymakers, business leaders, and researchers, this index helps users understand which sectors face the greatest risks—and opportunities—as AI capabilities accelerate. Our goal is to provide a transparent, evidence-based view of how AI might reshape the future of work.
Methodology Overview
Our analysis begins with the occupational classification data from O*NET, a comprehensive dataset maintained by the U.S. Department of Labor that outlines the skills, tasks, and knowledge areas associated with hundreds of job titles. We identify key attributes—such as routine task intensity, cognitive complexity, and social intelligence—that are highly relevant when evaluating susceptibility to automation. These attributes are then weighted and scored based on alignment with the capabilities of current and emerging AI models.
Risk Scoring Framework
Each occupation receives a composite AI Risk Score derived from a blend of expert-defined criteria and machine learning model outputs. We evaluate how easily core job functions can be replicated by state-of-the-art AI systems, drawing on both academic research and empirical benchmarks (e.g., model performance on text, image, and speech tasks). The result is a percentile-based ranking system that allows for easy comparison across job titles. We further aggregate this data to the industry level using NAICS codes, mapping occupations to the sectors where they are most frequently employed.
Aggregation and Industry Mapping
Because occupations often span multiple industries, we use employment distribution data from the U.S. Bureau of Labor Statistics to estimate how each role contributes to specific NAICS sectors. This enables us to generate industry-level risk scores at the 2-digit, 3-digit, and 6-digit NAICS levels, offering users a nuanced view of AI’s potential economic impact. The methodology ensures that both highly specialized industries and broad sectors can be meaningfully compared, helping stakeholders prioritize workforce planning, investment, and upskilling initiatives.