The Transformative Role of Artificial Intelligence in Internal Auditing: A Critical Review

Zulkiffly Baharom
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Zulkiffly Baharom: Tunku Puteri Intan Safinaz School of Accountancy (TISSA-UUM), College of Business, University Utara Malaysia, Malaysia

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 6, 2953-2966

Abstract: This systematic literature review investigates the transformative impact of artificial intelligence (AI) on internal auditing, addressing critical gaps in analyzing practical implementation challenges and theoretical frameworks for professional auditing contexts. A rigorous methodology examined 35 peer-reviewed studies published between 2018-2025, sourced through Google Scholar using predetermined inclusion criteria and systematically evaluated to identify implementation trends, performance disparities, and research gaps. Results demonstrate that AI technologies achieve measurable performance improvements, with machine learning attaining 85% fraud detection accuracy compared to 60% for traditional methods, yet implementation outcomes reveal significant challenges: only 23% of auditors successfully transitioned to strategic advisory roles following AI adoption, 35% reported decreased professional skepticism, and small organizations face implementation costs exceeding benefits by 40%. Critical research limitations emerged, including 78% of studies focusing on developed countries, 65% examining only financial services, merely 8 studies including samples exceeding 100 participants, and implementation failure rates of 40-60% remaining largely underreported. The analysis reveals that current technology adoption models prove inadequate for professional auditing environments, necessitating new theoretical frameworks incorporating professional skepticism and liability considerations, while organizations require hybrid workforce models, AI-focused quality assurance systems, and comprehensive risk management protocols. This review identifies seven priority research directions emphasizing longitudinal studies, failure case analysis, and cross-industry validation to advance effective AI implementation in internal auditing practice.

Date: 2025
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