HUMAN-CENTRIC DIGITAL TRANSFORMATION AND HRM INNOVATION: EXPLORING THE FUTURE OF WORKFORCE MANAGEMENT IN TECHNOLOGY-DRIVEN ORGANIZATIONS

Artificial intelligence AI, automation, people analytics, and integrated digital platforms are transforming human resource management (HRM). This narrative review explores the impact of these technologies on HRM and suggests principles for effective, human-centric HRM innovation. It brings together conceptual, empirical, and policy literature on digital recruitment, workforce analytics, development of employees, hybrid working, algorithmic management, and ethical governance. The reviewed evidence suggests that digital HRM has the potential to enhance efficiency in HR administration, recruitment assistance, workforce planning, learning, and flexibility at work. These benefits are contingent on the quality of the data, readiness of the organization, a proper work design, and employee involvement. Issues such as algorithmic bias, intrusive monitoring, privacy risks, inequitable access to digital skills, and decreased autonomy for employees are still significant issues. Evidence for AI-supported work tasks and hybrid work has been found in certain contexts but not universally in other work contexts or organizations. The review suggests an integrated solution of strategic alignment, human-centered work design, workforce capability development, responsible technology governance, and continuous performance evaluation. Along with organizational performance, there are centerpiece evaluation criteria that focus on employee well-being, fairness, transparency, trust, and human oversight. The suggested approach is a conceptual model that does not validate the sustainable workforce management approach through empirical evidence. It necessitates consistently auditing, having an easily accessible grievance procedure, training and retaining diverse employees, and communicating with employees in a transparent manner. Longitudinal, comparative, and intervention studies in a variety of organizational settings should be used to evaluate long-term effects in future research.