Our Methodology
How we calculate risk scores and identify transition paths
How It Works
Collect Data
We aggregate research from Oxford, WEF, McKinsey, and BLS to assess each occupation.
Score Risk
AI capability, task repetitiveness, and economic factors combine into a 0-100 risk score.
Map Transitions
Skill overlap analysis finds the safest, most practical career moves for each job.
Research Sources
Our AI automation risk scores are derived from multiple authoritative sources and research studies:
Oxford Martin School
Frey & Osborne's foundational 2013 study analyzing 702 occupations for susceptibility to computerization. Updated with modern AI capabilities.
World Economic Forum
"Future of Jobs Report 2023" with global workforce displacement projections and emerging role analysis.
McKinsey Global Institute
Research on automation potential by work activity, considering technical feasibility and economic viability.
Bureau of Labor Statistics
Official US employment data, salary statistics, and occupational outlook projections.
Risk Level Categories
Significant automation pressure. Transition planning recommended.
Partial automation likely. Upskilling recommended.
Strong human element required. Good transition target.
Transition Path Analysis
We identify transition paths by analyzing:
Mapping transferable skills between occupations using O*NET database classifications.
Prioritizing paths that move to lower automation risk scores.
Considering earning potential and growth outlook.
Estimating time and resources needed for transition.
Limitations
Our risk scores are estimates based on current research and AI capabilities. The actual pace of automation varies by industry, region, and company. Use these insights as one input in your career planning, not as definitive predictions.