The rapid evolution of digital technology has significantly transformed the landscape of the public accounting profession. One of the most disruptive innovations is the implementation of Artificial Intelligence (AI) within auditing processes. This study aims to explore how the integration of AI technology affects audit quality and to identify the opportunities and challenges arising from its implementation. The research methodology employed is a Systematic Literature Review (SLR) of reputable journal articles published over the last ten years. The literature selection process followed the PRISMA protocol to ensure transparency and objectivity of the findings. The analysis was conducted on various literatures discussing the use of machine learning, natural language processing, and robotic process automation in auditing procedures. The results of the literature review indicate that the application of AI contributes positively to the improvement of audit quality through more accurate anomaly detection, real-time processing of large data volumes, and the reduction of human error risks. AI enables auditors to shift from sample-based testing toward full population auditing, which directly enhances the level of audit assurance. However, this study also identifies crucial challenges such as algorithmic ethics, the need for new digital competencies for auditors, and the high cost of technological infrastructure investment.
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