Examining the Barriers to and Strategies for Artificial Intelligence Adoption in Audit Institutions: A Qualitative Grounded Theory Study

Abstract

This study aimed to investigate the process of artificial intelligence adoption in public-sector audit institutions using the qualitative grounded theory method. The required data were collected through in-depth semi-structured interviews with senior managers, auditors, and information technology specialists and were analyzed using the three-stage process of open, axial, and selective coding. The findings indicated that the central phenomenon underlying this process was the “transition from traditional auditing to intelligent auditing.” This phenomenon is shaped by causal conditions, including increasing pressure for efficiency and transparency, as well as intervening conditions, such as role conflict and the absence of adequate infrastructure. In response to this phenomenon, the principal actors employ a strategy of “stepwise implementation and proof of concept,” which ultimately leads to a transition toward an analytical–supervisory role and enhanced organizational legitimacy. The final model developed in this study, which emerged from the grounded theory analysis, can serve as a roadmap for policymakers and managers of audit institutions seeking to achieve the successful adoption of artificial intelligence.