<![CDATA[The rapid development of Artificial Intelligence (AI) has significantly transformed the audit profession by introducing advanced data analytics, automation, and intelligent decision support systems. These technologies offer considerable potential to enhance audit quality by improving efficiency, enabling comprehensive data analysis, and strengthening fraud and risk detection capabilities. However, the adoption of AI in auditing is accompanied by complex challenges that extend beyond technological implementation. This study examines the challenges associated with AI implementation in the audit profession and analyzes their implications for auditor roles, professional judgment, and audit quality. Using a systematic literature review approach, this study synthesizes existing academic research to identify the dominant technical, ethical, regulatory, and human-related issues influencing AI adoption in auditing practice. The findings indicate that although AI can improve audit accuracy and effectiveness, its success largely depends on factors such as data quality, system transparency, auditor competence, and ethical governance. Furthermore, AI is reshaping the role of auditors by shifting their focus from routine procedural tasks toward analytical evaluation and professional judgment. Ethical concerns such as data privacy, algorithmic bias, and accountability, along with regulatory limitations, remain key barriers. Overall, AI should be viewed as a complementary tool that strengthens audit quality when responsibly integrated with professional expertise and appropriate governance mechanisms.]]>
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