Scores 774 occupations by AI exposure using task-level data from O*NET and the AI Occupational Exposure (AIOE) framework, ranking which jobs face the most automation pressure.
Problem
Workers, employers, and policymakers are uncertain about how AI will reshape the labor market. Many occupations face varying levels of automation risk, but these risks are difficult to quantify without analysing task-level job content. Without clarity, career planning and workforce development rely on speculation, leaving individuals and organisations unprepared.
Overview
This project scores how exposed different occupations are to AI-driven automation. It combines O*NET's task and skill descriptors with the AI Occupational Exposure (AIOE) framework to produce interpretable exposure scores across 774 occupations, making it possible to see which roles face the most pressure and why.
How It Works (Approach)
Using O*NET occupational data, the system analyses the tasks and abilities that make up each job and applies the AIOE methodology to map them onto AI capabilities, producing a per-occupation exposure score. Occupations are then ranked and compared, surfacing the roles and skill profiles most and least susceptible to automation.
Impact / Value
Workers gain insight into which parts of their role are most exposed, enabling proactive skill development. Employers can better anticipate changing skill needs, and policymakers can target workforce programmes. The project turns abstract concerns about automation into concrete, occupation-level scores grounded in published research.
Key Features
- AI exposure scoring across 774 occupations
- Task- and ability-level analysis via O*NET descriptors
- AIOE-based methodology grounded in published research
- Ranked comparison of high- vs. low-exposure roles
- BLS wage and employment overlays planned for a future release