Analyzing the Impact of AI on Job Reallocation: A Bibliometric Perspective on Lost and Emerging Careers (2010–2025)

A Bibliometric Perspective on Lost and Emerging Careers (2010–2025)

Abstract

The research analyzes academic publications about artificial intelligence (AI) on labor reallocation through bibliometric analysis spanning a period of time from 2010 to 2025. The research examines 999 articles from Web of Science Core Collection  to study thematic progressions alongside research collaboration patterns and the intellectual organization of this interdisciplinary field. The authors use VOSviewer software to produce visualizations which show keyword co-occurrence and co-authorship by country and citation analysis of top publications. The study identifies four primary thematic clusters: automation and job displacement, digital reskilling and workforce transformation, policy responses to labor disruption, and innovation in employment systems. The main contributors to this field are the United States, China, the United Kingdom and Germany, while new research are increasingly coming from India, Brazil and South Africa. The research presents the first bibliometric investigation which concentrates on the effects of AI on job reallocation analysing both the risks of job replacement and new occupational possibilities. The work reveals ongoing research challenges because it lacks thorough examinations of labor transitions across specific sectors and regional inequalities and extended reskilling effects. The authors propose three recommendations to build adaptive skill-building systems while protecting labor rights in algorithmic work environments and promoting inclusive research participation across underrepresented regions. Overall, the study provides a complete meta-level evaluation of AI’s impact on employment systems and institutional resilience and inclusive economic adaptation pathways.