O*NET Skills, Abilities & Knowledge Index
Which skills are most exposed to AI automation — and which are AI-resistant? Explore 120 O*NET elements ranked by AI impact scores derived from occupation data across 1,000+ US jobs.
AI Impact Distribution
Browse by Element Type
Skills
35 elementsBasic, social, technical, systems, and resource management skills across all occupations.
Explore Skills →Abilities
52 elementsCognitive, psychomotor, physical, and sensory abilities that underpin occupational performance.
Explore Abilities →Knowledge Areas
33 elementsOccupational knowledge domains from computers and electronics to arts and psychology.
Explore Knowledge Areas →Most AI-Impacted Elements
Elements where heavy-AI-exposure occupations rely most intensely.
Most AI-Resistant Elements
Elements that predominate in low-AI-exposure occupations.
How AI Impact Is Calculated
Each element's AI Impact Scoreis derived entirely from existing O*NET occupation data — no external scoring or LLM calls required. For each element, we find every occupation that uses it, then compute a weighted average of that occupation's AI exposure score, weighted by the element's importance and level within that occupation.
A skill used intensely (high importance, high level) by heavily-automated occupations earns a high AI impact score. A skill used primarily by AI-resistant occupations earns a low score, even if it appears in many jobs.
Occupation AI exposure scores come from a composite of five dimensions: O*NET task routine-intensity, the academic AIOE exposure index, physical-presence requirements, cognitive-complexity scoring, and current technology-adoption signals.
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Meo Advisors helps enterprise teams identify which skills to retrain, augment, and protect as AI reshapes the workforce — grounded in the same data powering this index.
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