Keywords
human resource management
artificial intelligence
data analytics
HR policy
organizational change
HR 5.0
How to Cite
Abstract
The purpose of this study is to conduct a comprehensive analysis of digital transformation processes in human resource management policies through the strategic integration of artificial intelligence and data analytics technologies, to identify implementation mechanisms, determine success factors and barriers, and develop a scientifically grounded conceptual framework for effective digitalization of workforce management in contemporary organizations. The study employs systematic literature review methods with elements of conceptual modeling. The empirical base comprises 47 scientific publications and 14 industry reports for the period 2019–2025. Content analysis was used to identify thematic categories, empirical data were analyzed using the constant comparative method, statistical verification was conducted through triangulation across multiple sources, and conceptual synthesis was employed to construct an integrated theoretical model. Four groups of digital transformation drivers in HR management were identified and systematized: technological (with efficiency indicators of 87–95.8%), organizational (leadership correlation with readiness 0.92), external (regulatory, competitive), and social (ESG principles, human-centricity). Five categories of critical implementation barriers were revealed: technical limitations (44% of organizations), organizational obstacles (67% implementations without process changes), ethical-legal challenges (36% algorithmic bias), financial constraints (51% inability to measure ROI), and security threats (projected losses of $10.5 trillion). Digital technology's impact on HR management effectiveness was established across four key areas: recruitment (92.3% NLP screening accuracy), personnel development (218% increase in revenue per employee), workforce planning (95.8% turnover prediction accuracy), and engagement and retention (18–43% reduction in turnover). An integrated conceptual framework was developed comprising a four-phase process model, a three-level coordination system, five critical success factors with corresponding KPIs, and adaptive mechanisms for different organizational contexts. A comprehensive conceptual framework has been constructed, bringing together four distinct implementation phases, three organizational coordination levels, five critical success factors with measurable KPIs, and flexible mechanisms tailored to diverse organizational contexts. The framework synthesizes technological capabilities, organizational dynamics, ethical considerations, and human-centered elements into a unified theoretical model operating within the HR 5.0 paradigm. Analysis revealed a striking paradox: despite 65% of organizations having adopted AI tools, merely 1% have achieved full implementation maturity. This disparity between technological uptake and organizational readiness explains why 70% of digital transformation projects fail to meet their objectives. The framework equips HR practitioners with a structured methodology for AI implementation, incorporating concrete performance metrics and strategies tailored to organizations of different sizes, industries, and digital maturity levels.
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