In their article “Exploring the Potential of GenAI in Qualitative Research: An Internal Audit Expert Study with Leading Multinational Corporations,” Annika Bonrath, Marc Eulerich, Benjamin Fligge, Ronja Krane, Vanessa López Kasper and David A. Wood explore how Generative Artificial Intelligence (GenAI) can enhance qualitative research and reshape the internal audit profession. Drawing on discussions with 15 senior auditors from multinational corporations, the authors examine both methodological innovations for researchers and practical implications for internal auditors.
The study begins by addressing a long-standing challenge in accounting research: qualitative methods often face criticism for being time-consuming, subjective, and lacking in methodological clarity. The authors demonstrate how GenAI tools can automate tasks such as anonymization, initial coding, and thematic categorization, reducing manual effort while increasing transparency. Although GenAI streamlines these processes, human oversight remains essential to capture nuance, context, and interpretative depth.
In the practical domain, the paper reveals how internal auditors perceive GenAI as a “co-pilot” that improves both efficiency and effectiveness. AI tools are already assisting in report writing, document analysis, and risk identification. Auditors see particular value in using GenAI for brainstorming, preparing interviews, or analyzing large amounts of unstructured data, tasks that traditionally require extensive time and expertise. Notably, participants emphasized how GenAI can support junior auditors by accelerating learning and providing structured insights.
However, enthusiasm is tempered by caution. The authors identify significant challenges, including data security, trust in AI-generated results, organizational resistance, and the need to preserve professional judgment. Auditors worry that overreliance on GenAI could lead to deskilling and loss of critical thinking, which are key qualities of the internal audit profession. Moreover, compliance concerns and lack of structured AI governance frameworks hinder broader adoption.
From a theoretical standpoint, the findings align with the Technology Acceptance Model (TAM), emphasizing that auditors’ adoption of AI depends on perceived usefulness, ease of use, and trust. Yet, Bonrath and colleagues extend this model by highlighting the moderating role of organizational barriers, showing that even when individuals recognize AI’s potential, institutional structures and data policies can prevent its practical implementation.
Overall, the study contributes to both academic methodology and audit practice. It illustrates how GenAI can make qualitative research more rigorous and replicable while helping internal auditors work more strategically and efficiently. By combining methodological innovation with practical insight, the paper offers valuable guidance for researchers and practitioners navigating the era of intelligent automation. The full study is available on SSRN here.