The Global AI Job Explorer is an open, interactive intelligence tool that makes the impact of artificial intelligence on the global workforce tangible, personalized, and actionable. Instead of abstract statistics, it lets anyone — from individual professionals to C-suite leaders — explore how AI is transforming specific occupations in their country, and what concrete steps they can take to stay ahead.
Key Findings (2025-2026 Data)
25%
of the global workforce is in occupations with GenAI exposure — but only 3.3% face high automation risk (ILO 2025)
+78M
net new jobs expected by 2030: 170M created vs. 92M displaced (WEF Future of Jobs 2025)
300M
jobs globally could be disrupted by AI over the next decade (Goldman Sachs 2025-26)
78%
of organizations now using AI, up from 55% the prior year (Stanford HAI AI Index 2025)
27%
of OECD employment is in occupations at highest risk of automation (OECD Employment Outlook 2025)
15%
projected labor productivity boost when generative AI is fully adopted (Goldman Sachs economists)
Methodology: GAJEM
Scores are calculated using the Global AI Job Exposure Methodology (GAJEM), a multi-factor framework that evaluates each occupation across seven weighted dimensions to produce an Occupational AI Exposure Score (O-AIES) from 0-100.
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Task Analysis (35%)
10 characteristics per task: repetitiveness, data dependency, cognitive complexity, creativity, human interaction, physical dexterity, context variability, judgment, emotional intelligence, domain specificity
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Skills (20%)
Skill resilience scoring: technical-replaceable vs. social-emotional, creative-complex, physical-adaptive, and cognitive-judgment skills
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Context (15%)
Work environment factors: remote viability, physical presence requirements, safety-critical considerations
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Economic (12%)
Automation incentive: salary levels, labor cost arbitrage, ROI of automation investment
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Industry (10%)
Sector adoption curves: technology, finance, healthcare, manufacturing, education each follow different S-curve timelines
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Adoption (5%)
Technology trajectory based on AI capability growth rates: language, code, vision, reasoning, physical, creative
Country adjustments apply a Country Adjustment Factor (CAF) based on economic development tier (GDP/capita), digital infrastructure index, and AI regulatory stringency — meaning the same occupation may have different exposure levels in the US vs. India vs. Nigeria.
How It Was Built
This is a single-file HTML/CSS/JavaScript application — no frameworks, no build tools, no server required. It runs entirely in your browser. The physics-based galaxy visualization uses HTML5 Canvas with real-time collision detection, gravity simulation, and specular rendering. All 51 ISCO-08 occupations have hand-curated AI exposure scores, task breakdowns, rationale, and personalized AI career guidance.
The tool features dual framing (risk vs. opportunity), a 10-question career assessment, side-by-side comparison, country-correlated employment data with PPP salary adjustments, and inline source citations throughout.
Task decomposition (AIOE), US occupational data (O*NET), European skills taxonomy (ESCO), employment statistics (ILOSTAT), labor force data (World Bank).
Built with GAJEM methodology · Data updated March 2026 · Created by Remco Vroom
