AI in auditing: Drivers and barriers to its adoption and the sociomaterial reconfiguration of the auditor’s role

Abstract

Research Question: What are the drivers and inhibitors of Artificial Intelligence (AI) use in auditing, and how does AI reconfigure the auditor’s role? Motivation: The adoption of Artificial Intelligence (AI) in auditing has advanced rapidly, transforming processes, resources, and professional practices. Idea: The analysis is grounded in sociomateriality theory and examines how the introduction of AI reconfigures the auditor’s role, posing new challenges. Data: The study is based on a Systematic Literature Review (SLR) of 43 studies. Tools: The sociomaterial lens is used to analyze the interaction between auditors and AI tools, considering both technological capabilities and professionals’ engagement and adaptation. Findings: The results indicate that AI adoption in auditing is driven by efficiency, accuracy, real-time auditing, Big Data analytics and standardization. However, barriers such as resistance to change, algorithm aversion, heuristics and biases, transparency, expertise and training gaps, and complexity limit the full adoption of these technologies. This process is dynamic and ongoing: as technology evolves, organizational practices and arrangements also transform, rebalancing functions and responsibilities. Contribution: From this perspective, the benefits of AI in auditing can be more effectively realized when organizational practices support interaction between auditors and AI tools. Therefore, the sociomaterial lens allows us to observe that the auditor’s reconfiguration occurs dynamically and continuously, relying both on the evolution of technological capabilities (material agency) and on professionals’ engagement and adaptation (social agency).