Artificial intelligence in audit processes: a systematic literature review

Abstract

In an increasingly digitization-driven global environment, companies are integrating advanced technologies to innovate auditing processes and develop more effective solutions. New technologies are redefining the operational dynamics of organizations, providing innovative tools to enhance efficiency and optimize interactions with stakeholders. Among them, artificial intelligence (AI) is transforming the way companies analyze and process information, revolutionizing auditing activities as well. Auditors are encouraged to adopt a technology-centric strategic approach, aiming to improve access to business data, optimize analysis, and enhance audit quality. With AI, a deeper understanding of all the evidence can be gained, providing greater assurance to stakeholders of audited entities. However, despite the obvious benefits, the adoption of AI in auditing is proceeding slowly: on the one hand, technical procedures do not mandate its use, limiting its diffusion; on the other hand, the regulatory framework and the need for additional assurance drive companies to a cautious approach. Although AI is being studied in a variety of fields, its application in auditing remains an evolving field, with many issues yet to be investigated. This paper employs a Systematic Literature Review (SLR), supported by bibliometric and content analysis, to provide a detailed overview of AI adoption in audit processes. The analysis identifies five primary research clusters that constitute the current knowledge pathways in academic research. In addition to synthesizing the state of the art, this study identifies critical gaps in the existing literature and offers strategic directions for future research.