A Study on the Transformative Effects of Artificial Intelligence Technology on Human Resource Management Models

Abstract

The penetration of artificial intelligence technology into the field of human resource management is gradually reshaping the operational logic of traditional management models. Following the progressive logic of "deconstruction-transition-evolution," this study systematically deduces the transformative path of artificial intelligence technology on human resource management models. First, at the level of deconstruction, the study analyzes the substitution boundaries of algorithmic decision-making, the trend toward disaggregation in job analysis, and the mechanism of performance weight redistribution. Second, at the level of transition, the study elucidates the morphological reconstruction of management models from linear processes to dynamic networks, the migration path of managerial authority in human-machine collaboration, and the logic of setting elastic thresholds under real-time data stream feedback. Finally, at the level of evolution, the study deduces the nonlinear improvement in matching accuracy in automated recruitment, the knowledge emergence effect triggered by intelligent recommendation systems, and the correction mechanism of turnover prediction models on human capital retention curves. Overall, through theoretical deduction, this study reveals that artificial intelligence technology does not simply replace the existing functions of human resource management; instead, it reshapes the endogenous logic of management models through the three-tier progressive path of deconstruction, transition, and evolution. This research aims to provide actionable theoretical propositions and an analytical framework for subsequent empirical testing.