Published November 2025 | Version v1.0.0
Dissertation Open

The Impact of AI Adoption on the Global Workforce: A Systematic Review of Secondary Data

  • 1. International Business School, Budapest

Contributors

  • 1. International Business School, Budapest

Description

This research examines the impact of Artificial Intelligence (AI) adoption on the global workforce, a subject of growing significance for policymakers, business leaders, and labor market stakeholders. Moving beyond speculative projections, the study systematically synthesizes secondary data spanning 2018 to 2025, drawing on empirical evidence from leading international organizations, including the World Economic Forum (WEF), the Organization for Economic Co-operation and Development (OECD), and the International Labour Organization (ILO).
The analysis reveals a consistent pattern of profound labor market restructuring characterized by substantial job churn. Routine-based occupations particularly in administrative support and data processing are experiencing displacement, while new employment opportunities are emerging across AI-intensive, technology-driven, and green economy sectors. Although aggregate projections indicate a marginally positive net employment effect, this overall figure obscures a deeper structural misalignment. Newly created roles frequently differ in sectoral location, skill composition, and geographical distribution from those being displaced.
A central finding of the study is the structural decline in demand for routine cognitive and manual competencies, accompanied by a sharp increase in requirements for technological proficiencies—such as AI literacy and data analytics—as well as higher-order human capabilities, including creativity, resilience, and critical thinking. Despite widespread recognition of these shifts, the research identifies a significant global “implementation gap,” wherein reskilling and upskilling initiatives are not keeping pace with the speed of technological transformation.
Furthermore, the workforce impact of AI adoption is highly uneven across sectors and regions. While manufacturing sectors face heightened automation exposure, fields such as healthcare demonstrate task augmentation dynamics. More critically, an emerging global AI divide is evident: advanced economies possess stronger adaptive capacity, whereas developing economies face risks of premature deindustrialization.
The study concludes that the workforce consequences of AI integration are neither technologically deterministic nor institutionally predetermined. Rather, outcomes will depend on the strategic effectiveness of education systems, the scale of reskilling infrastructures, and the robustness of public policy frameworks designed to manage technological transition.

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