From Decision-Makers to Algorithm Interpreters: How Artificial Intelligence Is Reshaping the HR Manager’s Role

Abstract

The research situates the inquiry within contemporary transformations in talent management, performance assessment, and workforce planning influenced by data and machine learning. This examines the transformation of the human resource manager's position from a conventional decision-maker to an interpreter and overseer of algorithm-driven systems due to artificial intelligence. The study addresses a clear issue: firms employ algorithm-assisted decision-making without fully understanding its impact on HR managers' job satisfaction, professional identity, and competency needs. The study aims to examine the effects of the transition from human-centered to algorithm-supported decision-making on HR managers and to identify the organizational, technological, and human factors that promote ethical and effective implementation. How does algorithm-supported decision-making influence perceived professional identity, requisite abilities, and job satisfaction in the contexts of hiring, performance management, and workforce planning? What organizational, technical, and human factors affect an HR manager's capacity to effectively utilize AI while ensuring control and equity? The duties of human resource managers will be redefined to interpret algorithms; new technical and ethical competencies will be essential, and work satisfaction outcomes will vary based on support and organizational context, as indicated by the study's qualitative hypotheses. The study employed a qualitative design utilizing secondary academic sources and thematic synthesis to analyze evidence across many areas. The results indicate a significant transformation in roles, five essential competency areas including ethical judgment and algorithmic literacy, an identity conflict between strategic opportunity and automation apprehension, varied job satisfaction influenced by training and co-design, and the emergence of specialized hybrid positions. The study not only gives valuable guidance for policymakers and practitioners about investments in training, inclusive system design, bias detection, and governance structures, but also presents a conceptual framework linking competencies, governance, and organizational practices. Limitations encompass restricted spatial specificity and reliance on secondary data; future research should prioritize direct empirical studies, cross-country comparisons, and intervention evaluations. The report finishes by advocating for additional empirical research and practical measures to align technical capabilities with human-centered principles.