The Impact of Artificial Intelligence on the Scope of Internal Audit: A Comparative Study Between Population-Based and Sample-Based Approaches

Abstract

This study explores the impact of artificial intelligence (AI) on internal auditing, particularly its potential to transition from providing reasonable assurance to absolute assurance. By leveraging AI's capability to analyze entire datasets instead of relying on traditional sampling methods, the research examines both the opportunities and challenges of this paradigm shift. Using a mixed-methods approach, the study combines quantitative analysis, including reliability (Cronbach’s Alpha: 0.85) and validity testing, with qualitative insights gathered from experts in the field, the research concludes that redefining internal auditing to imply absolute assurance is impractical and recommends a hybrid model combining AI's computational power with human expertise. These insights contribute to the growing discourse on AI’s transformative role in auditing and its implications for stakeholders and practitioners.