How our AI job risk calculator works

A task-level approach to measuring automation exposure, powered by real labor market data.

Dr. Sarah Chen

Dr. Sarah Chen

AI & Labor Market Researcher

|4 min read·January 5, 2026
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Data analytics dashboard showing AI-powered workforce analysis
Our calculator cross-references your tasks against real automation benchmarks from leading labor market institutions.Photo: Unsplash / Luke Chesser

Why task-level analysis matters

Most automation risk tools rely on job titles alone. That's a problem — two "marketing managers" can have completely different workflows. One might spend 80% of their time on data reporting (high automation risk), while another focuses on stakeholder relationships (low risk).

Our calculator analyzes the actual tasks within your role. It maps each task against real-world automation benchmarks from the Bureau of Labor Statistics, O*NET occupation database, and the World Economic Forum's Future of Jobs Report, producing a risk score that reflects what you do, not just what you're called.

Step 1: Enter your job details

You provide your job title, experience level, industry, and country. The more specific, the more accurate the analysis. Optional career interests help the AI refine transition recommendations.

Step 2: AI analyzes your tasks

Our model identifies the core tasks in your role and cross-references each one against automation benchmarks. It evaluates factors like task repetitiveness, data dependency, physical requirements, and cognitive complexity — the same criteria used by McKinsey Global Institute and OECD in their workforce automation studies.

Step 3: Get actionable insights

You receive a risk score from 0–100, a task-by-task breakdown, a skill strategy (which skills to reinforce vs. learn), career transition paths with salary and demand data, curated learning resources, and a 12-month action roadmap.

The result is a personalized report with everything you need to make informed decisions about your professional future — not a generic prediction, but a roadmap built on your specific circumstances.

What data sources power the analysis

Our model draws on the Bureau of Labor Statistics Occupational Outlook Handbook, the O*NET occupation database (maintained by the U.S. Department of Labor), the World Economic Forum's Future of Jobs Report 2025, McKinsey Global Institute's research on generative AI and the future of work, and the OECD Employment Outlook. These are the same sources used by governments and Fortune 500 companies to plan workforce strategy.

844Occupational tasks analyzedStanford SALT Lab WORKBank
5Leading research institutionsBLS, O*NET, WEF, McKinsey, OECD
0-100Personalized risk scoreTask-level analysis

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