Purpose – This study aims to map the evolution of scholarly research on the relationship between artificial intelligence (AI) and workforce dynamics, specifically focusing on job creation, displacement and reskilling, through a comprehensive bibliometric analysis spanning 2005–2024. Design/methodology/approach – A quantitative bibliometric methodology was adopted using data extracted from the Scopus database. The study applies performance analysis and science mapping techniques using Bibliometrix (R) and VOSviewer to examine publication trends, influential authors, institutional collaboration, keyword co-occurrence and thematic clusters within the AI-employment discourse. Findings – The study identifies a sharp increase in scholarly interest post-2015, with notable research clusters in AI-enabled job creation, task-based displacement and workforce reskilling strategies. The USA, China and the UK are the leading contributors, while European countries demonstrate higher international collaboration rates. Influential theoretical frameworks include Schumpeter’s Creative Destruction, Dynamic Skill theory and task-based AI substitution models. Originality/value – Unlike prior fragmented or sector-specific studies, this research provides a holistic bibliometric synthesis of AI and workforce literature. It bridges empirical trends with theoretical insights and offers policy-relevant perspectives on reskilling, education and inclusive labor strategies. The study contributes a scalable research map for future investigations into AI-driven labor market transformations. © 2025 Elsevier B.V., All rights reserved.