Digital Transformation in Auditing

Digital transformation is reshaping the auditing profession, changing not only the tools auditors use but also the skills they need and the way professional judgment is exercised. This thesis examines this transformation by combining a review of the academic and professional literature with an exploratory single-case study based on an internship experience at Ernst & Young (EY). The first part of the work develops a theoretical framework describing digital transformation as a technological, organizational, and human phenomenon, and examines how enabling technologies, such as data analytics, automation, continuous auditing, blockchain, and Artificial Intelligence, are being integrated into audit methodologies and regulatory frameworks. The second part applies this framework to direct observations gathered during the internship, illustrating how these tools are used in practice and how their effectiveness depends on data quality and on the judgment of experienced team members. The findings suggest that digital transformation can improve audit efficiency and analytical capacity without displacing the auditor’s core function of providing independent, judgment-based assurance. At the same time, it introduces new risks, related to cybersecurity, data quality, over-reliance on automated outputs, and the “black box” nature of some AI systems, and raises workforce, market, and regulatory implications that require deliberate management, including the evolving role of internal audit under the EU Artificial Intelligence Act. Given the exploratory, single- case nature of the internship, these findings are not intended to be statistically generalizable, but they offer a contextualized, first-hand contribution to understanding how digital transformation is experienced in audit practice.