Optimizing HR Efficiency and Employee Well-being through AI-Driven Automation

Modern organizations are implementing artificial intelligence (AI) applications to improve human resource management practices through increased decision transparency and improved efficiency outcomes. Decision flexibility, employee acceptance issues, and the risk of bias create significant challenges in AI adoption in HR. This study examines the implementation of AI in the HR function to evaluate its impact on operational efficiency and employee well-being, using a comprehensive theoretical framework integrating Task-Technology Fit, Sociotechnical Systems, Job Demands-Resources, and Explainable AI. The study implemented a mixed-methods design involving explanatory sequential elements to analyze the effectiveness of AI systems and employee perceptions. Quantitative analysis revealed that AI technology improves HR efficiency by up to 45% despite posing challenges in decision-making flexibility. Qualitative exploration identified differences in AI acceptance across organizational levels, with 80% of employees indicating limited understanding of AI decision mechanisms and 60% concerned about potential bias. The study findings suggest that optimal AI implementation requires a collaborative human-AI model that combines algorithmic analysis with human oversight, especially for complex scenarios that require contextual interpretation. This study highlights the importance of algorithmic transparency and targeted communication strategies to increase technology adoption. Organizations should develop comprehensive Explainable AI mechanisms that effectively communicate decision rationales in accessible language, while simultaneously implementing monitoring protocols to evaluate and address potential patterns of bias. This research contributes to the understanding of specific factors that influence successful AI implementation in the HR context, emphasizing that technology optimization and human-centered design represent complementary priorities.