<![CDATA[Artificial Intelligence (AI) has emerged as a transformative force in the auditing profession, offering innovative tools and applications that enhance audit efficiency, accuracy, and scope. This systematic literature review aims to comprehensively examine the current state of AI integration in auditing, focusing on the various AI tools utilized, practical applications within both internal and financial audits, and the challenges faced during implementation. Using a rigorous search and screening process across multiple academic databases, this study synthesizes findings from recent empirical and theoretical research published over the last decade. Results reveal a growing adoption of machine learning, natural language processing, and robotic process automation in audit processes, which contribute to improved fraud detection, risk assessment, and data analysis capabilities. However, challenges such as data privacy concerns, ethical considerations, lack of auditor competency in AI technologies, and regulatory uncertainties persist. This review highlights critical gaps in the literature, particularly the need for standardized frameworks to guide AI deployment and the development of auditor skills to effectively leverage AI tools. The study concludes with recommendations for future research and practical implications for auditors, firms, and policymakers aiming to harness AI’s full potential in auditing. This review contributes to advancing knowledge on AI’s role in modernizing audit practices and shaping the future of the auditing profession.]]>
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