Enhancing Audit Efficiency and Accuracy through Artificial Intelligence

Abstract

This research investigates the current state of adaptation of artificial intelligence (AI) adoption within the auditing sector, shedding light on the perspectives and practices of auditors across various roles and experience levels. The study employs a mixed-methods approach, combining quantitative data gathered through a structured questionnaire with qualitative insights derived from thematic analysis. The participant profile unveils a diverse representation of the auditing profession, encompassing roles from audit firm partners to supervisors and spanning experience levels from less than 5 years to more than 15 years. The distribution among small, medium, and large audit firms ensures a comprehensive exploration of AI adoption trends. Most participants find themselves in the initial stages of AI adoption, with a notable inclination toward tools like Chat GPT and Chat GPT Excel, signifying a sector in transition. Efficiency improvement emerges as the driving force behind AI adoption, aligning with the practical needs of audit firms seeking to enhance productivity. However, challenges such as integration issues, data privacy concerns, and skill gaps highlight the complexities associated with incorporating AI seamlessly into audit workflows. Qualitative findings further enrich the analysis, uncovering themes related to perceived efficiency gains and the varied impact of AI technologies on accuracy. Participants consistently emphasize the transformative effect of AI adoption on the efficiency of audit processes, indicating a positive shift in the pace and effectiveness of procedures. The exploration of accuracy reveals a spectrum of opinions, emphasizing the importance of a nuanced understanding of the contextual conditions influencing the relationship between AI adoption and audit outcomes. The implications of these findings extend to both practice and policy, offering valuable insights for auditors, audit firms, and policymakers involved in shaping responsible AI use in auditing. Recommendations guide auditors in strategic AI adoption and skill development, while educators are encouraged to incorporate AI-related skills into curricula. Acknowledging limitations, the research suggests avenues for future studies, including longitudinal analyses and cross-industry comparative studies, contributing to the ongoing discourse on AI's role in reshaping auditing practices.