Designing an Artificial Intelligence–Enabled Succession Planning Model for Islamic Azad University Branches in Tehran Province: A Thematic Analysis Approach

Abstract

This study aimed to develop a context-specific model of artificial intelligence–enabled succession planning for Islamic Azad University branches in Tehran Province. This applied study adopted a qualitative design based on thematic analysis. The participants comprised 18 academic experts and professionals in human resource management, public administration, public policymaking, and organizational behavior, who were selected through purposive sampling. Eligibility criteria included at least ten years of relevant academic or professional experience, experience in university or human resource management, familiarity with succession planning, and sufficient knowledge of organizational applications of artificial intelligence. Data were collected through semi-structured interviews addressing succession planning practices, successor identification criteria, competency assessment, employee development, technological requirements, organizational culture, and implementation challenges. Theoretical saturation was reached after the sixteenth interview, and two additional interviews were conducted to confirm data stability. The data were analyzed according to Braun and Clarke’s six-stage thematic analysis procedure using MAXQDA 22. Trustworthiness was enhanced through continuous code review, expert evaluation, comparison of the extracted themes with the theoretical foundations, and recoding of selected interviews. The Cohen’s kappa coefficient was 0.678, indicating an acceptable level of coding agreement. The analysis generated six overarching themes, 21 organizing components, and 94 indicators. The principal dimensions were training and development, institutionalization strategy, evaluation and monitoring, cultural-organizational factors, technology-oriented factors, and strategic planning. The findings indicated that artificial intelligence can support succession planning through human resource data analysis, identification of high-potential employees, competency-based ranking, prediction of successor readiness, personalized development planning, career-path design, and continuous performance monitoring. The proposed model therefore shifts succession decisions from subjective and informal judgments toward a systematic, transparent, competency-based, and data-driven process. Effective implementation of artificial intelligence–enabled succession planning requires robust technological and information infrastructures, senior management support, transparent promotion and appointment procedures, institutionalized meritocracy, a learning-oriented culture, intergenerational knowledge transfer, clearly defined career pathways, and compliance with ethical principles relating to employee privacy, fairness, accountability, and data confidentiality.