<![CDATA[The Industry 4.0 revolution has driven a fundamental transformation within the audit profession ecosystem, where the explosion in volume, velocity, and variety of data (Big Data) demands auditors abandon conventional methods in favor of more adaptive, technology-based approaches. Traditional sampling methods are increasingly viewed as inadequate for capturing risks in increasingly complex business transactions. Artificial Intelligence (AI) emerges as a strategic solution to revitalize audit capabilities, offering unprecedented predictive and cognitive analysis capabilities. However, the impact of AI integration on operational effectiveness and overall assurance quality remains a subject of dynamic debate. The objective of this study is to systematically identify, analyze, and critically synthesize current literature regarding the multidimensional implications of AI implementation in modern audit procedures, as well as to comprehensively map the transformative opportunities and structural challenges accompanying it. Using a Systematic Literature Review (SLR) method adhering to PRISMA protocols, this study examines academic articles published between 2015 and 2025 from various reputable databases such as Scopus, Web of Science, and Google Scholar. Synthesis results indicate a duality in AI's impact: on one hand, AI significantly boosts efficiency through the automation of routine tasks (such as document verification and reconciliation) and enhances audit quality via full population testing, which minimizes detection risk. On the other hand, adoption is hindered by serious challenges regarding the technical competence gap of auditors, data privacy ethical issues, and the "black box" problem where algorithmic logic is difficult to explain to regulators and clients, potentially threatening audit transparency]]>
