Artificial intelligence and the future of the internal audit function

Abstract

Abstract Artificial intelligence (AI) can support the company’s internal audit function (IAF) by delivering substantial strategic oversight, minimizing manual procedures, and making possible additional value-added auditing service. Currently, there are research gaps in the literature, such as limited studies on the topic, low AI adoption rates in the IAF across different countries and regions, and a shortage of comprehensive frameworks for effectively using AI in the IAF. Hence, this review work aims to fill the research gap by offering an outline of research avenues on the topic in the literature and suggesting a new compressive framework for the effective use of AI in the IAF. This paper undertakes a systematic literature review (SLR) approach and aspires to highlight the state of research on the use of AI in the IAF, to deliver insight for scholars and industry experts on the issue, and to reveal the implications for IAF of the new AI technology. Moreover, to quickly make artificial intelligence work in internal audit functions, the CACS framework was recommended with attributes such as commitment, access, capability, and skills development (CACS). This work provides significant contributions for guiding future research directions and the development of theoretical foundations for the IAF field. On a practical level, the work will help internal auditors to assess and understand the potential advantages and risks of implementing AI in their organization’s IAF. For regulators, this review should prove useful for updating regulations on internal auditing in the context of using advanced technology such as AI and for ensuring the compliance of internal auditing practices to the evolving technology. Organizations can also benefit from this review to decide whether AI investments in their IAF are justified. This review made an initial extensive SLR on AI use in the IAF as a basis for developing new research avenues in auditing and accounting.