Recent Research Reviews Journal
IRO Journals
Volume 4 — Issue 2 — December 2025

Generative AI and Job Vulnerability: A Global Review

https://doi.org/10.36548/rrrj.2025.2.006
Sangita Pokhrel
Lecturer, York St John University, London, United Kingdom
Sanjaya Poudel
Research Assistant, Auburn University, Alabama, USA
Shiv Raj Banjade
Lecturer, QA Higher, London, United Kingdom
Swathi Ganesan
Senior Lecturer, York St John University, London, United Kingdom
Chathurika Goonawardane
Lecturer, QA Higher, Manchester, United Kingdom
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Pokhrel, Sangita, Sanjaya Poudel, Shiv Raj Banjade, Swathi Ganesan, and Chathurika Goonawardane. 2025. “Generative AI and Job Vulnerability: A Global Review”. Recent Research Reviews Journal 4 (2): 281-305. https://doi.org/10.36548/rrrj.2025.2.006.
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Keywords

— AI Automation
— Job Displacement
— Sectoral Vulnerability
— Generative AI
— Future of Work
— Workforce Adaptation
Published: 25-11-2025

Abstract

The rapid acceleration in the development of AI-in particular, generative AI-is changing the nature of work globally, with great significant for job creation, transformation, and displacement. Drawing on major studies, employer forecasts, and AI-driven evaluations, this review paper synthesizes evidence to investigate sectoral vulnerabilities to automation across different economic contexts. In doing so, it identifies professions and sectors that have emerged as particularly vulnerable to disruption by AI, with a specific focus on developments relating to generative AI since 2023. Clerical, administrative, financial, and customer service jobs are currently identified as those globally at the highest risk, while knowledge-based and creative jobs that have been considered hitherto safe are increasingly vulnerable. Conversely, occupations that are physically and emotionally demanding and unpredictable, such as health care, skilled trades, and hospitality, remain comparatively resilient. This review also explores regional variation in risks from automation, approaches to the methodological assessment of risk, and the strategic responses from employers across industries. Conclusively, this study emphasizes a set of policy recommendations targeting concerted upskilling, AI governance, and inclusive transition strategies in efforts to prevent labor markets from becoming more unequal. This systematic literature review used information obtained from peer-reviewed journals, policy reports, and organizational datasets published between 2013 and 2025. Altogether, 52 studies were thematically analyzed and comparatively mapped across sectors in line with predetermined inclusion criteria targeted at AI-driven automation and workforce vulnerability across sectors.

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