From Basics to Intelligent Tools: Accounting Information and AI in Public‐Sector Internal Auditing

Abstract

This study examines the integration of artificial intelligence (AI) in public sector internal auditing, focusing on the extent of AI adoption, the types of AI tools used, and the challenges faced by auditors in implementation. The research employs both descriptive statistical analysis and inferential techniques, including Spearman’s correlation and ANOVA, to assess the relationships between AI adoption and institutional factors. Findings indicate that while data analytics is the most commonly used AI tool, a significant proportion of respondents do not utilize AI in their auditing practices. The primary barriers to AI adoption include a lack of training, high costs, and concerns regarding data privacy. The study further reveals that AI usage varies depending on the type of institution in which they work. These insights contribute to the ongoing discussion on digital transformation in auditing, emphasizing the need for enhanced training programs and strategic investments to facilitate AI integration.