Artificial Intelligence in Internal Auditing

Abstract

Artificial Intelligence (AI) has been the center of organizational transformation. Consequently, auditing in an organization has also been shifted from a traditional to an AI-based approach. However, limited research has been conducted in this sector. Therefore, this study investigates the use of AI technology in the internal audit function (IAF). Primarily, the researcher employed interpretive philosophy, using an inductive approach and a qualitative research strategy. Subsequently, the case study design was implemented. For primary data collection, open-ended questionnaires and interviews were conducted via Microsoft Teams with three banks and one audit firm from four countries; UK, UAE, Ireland, and Pakistan. Later, a thematic analysis was conducted to identify significant shared themes and subsequent global trends.

The findings indicate that AI is extensively used in auditing to increase efficiency and shift the auditor’s role towards strategic decision-making. However, its adoption encounters challenges, including transparency and confidentiality, reliance on prompt-dependent models, insufficient professional competencies, and potential bias in AI outputs. On the flip side, AI is expected to transform internal auditing through continuous and real-time monitoring. In addition, this research expanded the technology-organization-environment (TOE) framework by indicating that all factors combinedly affect AI adoption in auditing. In addition, AI competencies and regulatory uncertainty should be considered for further development of the model.

The study suggests that with the shifting role of the internal auditor, organizations should equip auditors with new skills and competencies that combine audit standards with AI expertise. Furthermore, the government should establish clear, transparent guidelines for the ethical and efficient use of AI within auditing worldwide.