Title: Auditing Innovation: A Model of Artificial Intelligence Adoption
Author(s) : Joseph O'Donnell
Abstract Auditors using artificial intelligence (AI) offers opportunities to drastically improve the effectiveness and efficiency of audits. The combination of vast amounts of data, advances in data analytics tools and AI provide the potential to transform the auditing process. Increasingly businesses are making data-driven decisions and auditors are looking to keep pace with the data-driven audit (AICPA, & Canada, 2020). AI enabled scans of client data sets provide the capability to effectively test 100 percent of the transactions and possibly identify trends that would go undetected by humans. Employing AI across audit phases offers opportunities to more efficiently perform routine and repetitive audit tasks. In addition, AI could be used to assist in audit risk assessment in the audit planning process and in identifying possibly fraudulent transactions (HO, 2023). Countering the sunstantial AI auditing benefits are significant hurdles to adoption such as investment in technology, a shortage of auditors skilled in the use of AI and implementation costs. While auditors have adopted some modern technologies (e.g., electronic audit work papers), many audits still include labor intensive tasks such as manually testing accounting transactions. Modern technologies provide robust testing capabilities but many auditors have been reluctant to use these technologies. The success of AI for auditing depends on the audit profession’s adoption of this transformative innovation. This paper develops a theoretical model of the antecedent factors that affect the adoption of AI for auditing utilizing diffusion of innovation theory and the Technology Acceptance Model (TAM). Diffusion of innovations is a rich area of research that involves the adoption of new ideas, inventions, and new ways of doing things (Rogers, 2003). TAM is a widely used theory that addresses perceived usefulness and perceived ease of use in adoption of information technology (Davis, 1989). The model addresses the influence of the characteristics of AI for auditing and attributes of auditors on the adoption of this innovation. The theoretical model provides a further understanding of technological adoption in the area of auditing innovations. Auditors benefit from the paper by using the theoretical model to develop strategies to promote the greater use of AI for auditing. References AICPA, & Canada, C. (2020). The Data-Driven Audit: How Automation and AI are Changing the Audit and the Role of the Auditor. Chartered Professional Accountants of Canada, 1-34. Davis, F. D. (1989), Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly 13 (3): 319–340. Ho, C. (2023, November 3). Can Artificial Intelligence Transform Auditing and Our Fear of That Transformation? Speech at Rutgers Business School 58th World Continuous Auditing & Reporting Symposium. https://pcaobus.org/news-events/speeches/speech-detail/can-artificial-intelligence-transform-auditing-and-our-fear-of-that-transformation Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). New York: Free Press.