Artificial Intelligence, Automation and the Restructuring of the Global Labour Market: Occupational Displacement, Human-AI Complementarity and the Future of Work Beyond 2035

Abstract

Artificial intelligence (AI), generative artificial intelligence (GenAI), and robotics are transforming employment at a speed that challenges conventional assumptions about occupational security, higher education, and workforce preparation. The current study comparatively examines occupational fields likely to experience significant automation, employment contraction, or task restructuring over the next decade, and identifies occupations expected to remain resilient or expand beyond 2035. This integrative comparative review draws on peer-reviewed labour-economics scholarship and evidence from the International Labour Organisation (ILO), International Monetary Fund (IMF), Organisation for Economic Co-operation and Development (OECD), World Bank, and World Economic Forum (WEF). This study develops an Occupational AI Vulnerability-Resilience Model based on task routineness, digitisation, predictability, interpersonal intensity, physical-world complexity, accountability, domain expertise, and complementarity with technology. Labour market areas such as clerical administration, data entry, routine bookkeeping, transcription, elementary editing, translation, basic content creation, telemarketing, routine customer service, cashiering, and selected elementary programming functions demonstrate comparatively high substitution in the future labour market. Conversely, nursing, medicine, counselling, skilled trades, infrastructure maintenance, cybersecurity, AI governance, scientific research, advanced education, entrepreneurship, and occupations involving complex physical environments, responsibility, trust, creativity, and contextual judgement demonstrate greater resilience in the future labour market. The present analysis finds substantial differences between advanced and developing economiesin the future labour market discourse because capital costs, wages, digital infrastructure, informality, education, and technological readiness influence the feasibility of automation. For Jamaica and the Caribbean, the principal challenge is not merely preventing technological unemployment but transitioning from routine service employment towards AI-augmented professional, technical, creative, healthcare, green-economy, and knowledge-intensive work. This study, therefore, concludes that future employability will increasingly depend on human-AI complementarity rather than occupational titles.