Revolutionizing HRM Practices with Cutting-Edge Technology for Sustainable Organizational Growth

Abstract The rapid evolution of cutting-edge technologies is fundamentally transforming human resource management (HRM) practices, enabling organizations to achieve sustainable growth through enhanced efficiency, data-driven decision-making, and strategic workforce optimization. This study examines...

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Bibliographic Details
Published inINTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT Vol. 9; no. 8; pp. 1 - 9
Main Authors RANI K, Dr VEENA, P V, PRAVEEN KUMAR
Format Journal Article
LanguageEnglish
Published 15.08.2025
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Summary:Abstract The rapid evolution of cutting-edge technologies is fundamentally transforming human resource management (HRM) practices, enabling organizations to achieve sustainable growth through enhanced efficiency, data-driven decision-making, and strategic workforce optimization. This study examines the integration of artificial intelligence (AI), machine learning (ML), and advanced analytics in HRM, highlighting their impact on recruitment, talent development, performance management, and employee engagement. Despite their transformative potential, significant challenges persist, including resistance to technological adoption, data privacy concerns, and systemic integration complexities. Through a comprehensive analysis of contemporary literature and industry trends, this paper identifies key strategies for overcoming these barriers, emphasizing the critical role of change management, upskilling initiatives, and ethical AI deployment. The findings underscore the imperative for organizations to embrace digital HRM innovations to maintain competitive advantage in an increasingly dynamic global business landscape. This research contributes to the ongoing discourse on technology-driven HRM by providing actionable insights for practitioners and scholars alike. Keywords: Artificial Intelligence, Digital HR Transformation, Workforce Analytics, Sustainable Organizational Growth, Ethical AI in HRM
ISSN:2582-3930
2582-3930
DOI:10.55041/IJSREM51844