The Emergence of Algorithmic Leadership: A Grounded Theory Exploration of Managerial Authority in AI-Augmented Organizations

Abstract

This study aimed to develop a grounded theory explaining how managerial authority is reconstructed when artificial intelligence systems become embedded in decision-making, monitoring, coordination, and performance-management processes in AI-augmented organizations. This qualitative study used a grounded theory design to explore the emergence of algorithmic leadership in organizations located in Tehran. Data were collected through semi-structured interviews with 26 participants, including senior managers, middle managers, HR specialists, data and AI managers, and team leaders working in AI-augmented organizations. Participants were selected through purposive and theoretical sampling, and interviews continued until theoretical saturation was achieved. The interviews focused on experiences of AI-supported decision-making, changes in managerial roles, perceived legitimacy of algorithmic recommendations, accountability, employee trust, and leadership adaptation. Interviews were audio-recorded with consent, transcribed verbatim, and analyzed using open, axial, and selective coding. NVivo software was used to organize codes, compare categories, retrieve quotations, and develop the final grounded-theory model. The analysis generated five main categories: algorithmic delegation of managerial judgment, redistribution of authority between humans and AI systems, datafied visibility and control, leadership sensemaking and algorithmic literacy, and governance of algorithmic legitimacy. The core category was identified as calibrated algorithmic authority, referring to the dynamic process through which managers gradually transfer selected managerial functions to AI systems while retaining responsibility for interpretation, ethical judgment, exception handling, and employee meaning-making. Participants described AI as neither a neutral tool nor a full substitute for leadership, but as a new authority-bearing actor that reshapes how decisions are justified, contested, and implemented. The study suggests that algorithmic leadership emerges when AI systems become active participants in organizational authority rather than merely technical decision-support tools. In AI-augmented organizations, effective leadership depends on managers’ ability to calibrate the relationship between algorithmic recommendations and human judgment. The proposed grounded theory highlights that algorithmic leadership requires technical literacy, ethical reflexivity, procedural transparency, accountability structures, and communicative competence. Organizations that treat AI as an unquestioned authority risk weakening trust and human agency, whereas organizations that govern AI as a transparent and contestable partner may strengthen decision quality, coordination, and adaptive leadership.