ABSTRACT. This research looks into the transformation in the management of work of human capital and talent as a result of the automation for recruiting and retention. With the growing intricacy of talent ecosystems, there are new avenues for artificial intelligence (AI) innovations to transform how human resources are managed. This work examines the patterns of use, strategic approaches, and results of AI application in empirical workforce management through an extensive analysis of contemporary literature and many case studies. The evidence suggests that firms that apply AI in their talent management processes reap operational benefits. The study also underscores that to achieve meaningful change, organizations require a comprehensive approach rather than simplified elusive strategies employing technology. These findings highlight the significance of achieving alignment between technological resources with strategic goals and a people-driven implementation approach. This research advances theory and practice by formulating an integrative model of AI technology application within workforce management systems, incorporating technology and human strategy equally and balancing innovation with people-centric frameworks while upholding ethics, and supporting implementation through organizational strategy. Such understanding is relevant for organizations grappling with the impact of AI technology on workforce solutions in the context of post-pandemic business challenges.
JEL codes: E24; J21; J54; J64

Keywords: artificial intelligence; talent acquisition; employee retention; digital transformation; workforce analytics; human resource management

How to cite: Tucmeanu, E. R., and Almasan, C. M. (2024). “Digital Transformation of Workforce Management: Artificial Intelligence-driven Strategies for Talent Acquisition and Retention,” Economics, Management, and Financial Markets 19(4): 58–78. doi: 10.22381/emfm19420244.

Received 17 July 2024 • Received in revised form 15 December 2024
Accepted 26 December 2024 • Available online 30 December 2024