Exploring Generative Artificial Intelligence Adoption in Auditing: A Technology Acceptance Model Perspective - Rohman, Fatkhur; Widya, Rizky; Syaharani, Noer (ICSIGER- International Conference on Science and Emerging Technology, 2025)
Abstract: This article presents a comprehensive literature review on the adoption of Generative Artificial Intelligence (GenAI) in auditing, analyzed through the lens of the Technology Acceptance Model (TAM). Drawing upon 23 recent peer-reviewed studies published between 2020 and 2025, the review synthesizes key developments, opportunities, and challenges at the intersection of digital transformation and audit practice. Findings indicate that GenAI offers substantial benefits for audit effectiveness—most notably in enhanced fraud detection, process automation, data analysis, and audit quality—contributing to a high level of perceived usefulness among auditors. Nevertheless, perceived ease of use varies, with some practitioners citing intuitive GenAI tools, while others experience hurdles related to training needs, system complexity, and concerns regarding transparency and algorithmic bias. The behavioral intention to adopt GenAI is significantly influenced by organizational commitment, leadership support, and ongoing auditor training, though resistance to change and regulatory uncertainty remain persistent barriers. Key challenges include doubts over AI output reliability, data privacy risks, integration issues, and the digital maturity gap, especially in smaller firms. The review concludes that the successful integration of GenAI into auditing hinges on human resource readiness, supportive culture, regulatory clarity, and continuous professional development. It emphasizes the urgent need for further empirical and longitudinal research to objectively assess GenAI’s real impacts on audit outcomes, independence, and ethical considerations in diverse organizational settings.