Workforce planning optimization utilising AI to improve firm performance: a systematic literature review using VOSviewer

Purpose Workforce planning is a crucial component of strategic human resource management (HRM) to ensure organisations have the right talent at the right time to achieve strategic goals. The rapid development of artificial intelligence (AI) offers new opportunities to optimise workforce planning by enabling data-driven and analytical decision-making, as well as improving efficiency, accuracy and responsiveness. At the same time, AI adoption raises human-centric concerns such as employee well-being, job security and data ethics. This study aims to explore the dual role of AI in workforce planning by examining both its contributions to firm performance and its implications for employees. Design/methodology/approach A systematic literature review (SLR) following PRISMA (identification, screening, eligibility, inclusion). Literature was collected from Scopus, Web of Science, IEEE Xplore and Google Scholar for English-language studies (2014–2024) on AI in workforce planning and its links to firm/organisational performance. After deduplication and screening, 50 articles were analysed in depth. Bibliometric mapping (VOSviewer) was combined with thematic coding. Findings The results reveal five dominant themes: demand forecasting, workforce scheduling, skill gap analysis, recruitment and employee retention. Across these areas, AI enhances predictive accuracy, resource allocation and organisational flexibility. However, emerging concerns include algorithmic bias, job insecurity, labour relations and employee well-being. This indicates that AI adoption represents not only a technical improvement but also a socio-organisational transformation. Originality/value Unlike earlier reviews that primarily emphasised efficiency, this study highlights the shift towards human-centric issues, including fairness, ethics and labour governance. It also contributes methodologically by combining bibliometric mapping with thematic coding to quantify theme prevalence and validate findings.