<![CDATA[Artificial Intelligence (AI) adoption in financial auditing presents disruptive potential, enhancing efficiency and insight while challenging foundational principles of evidence, professional skepticism, and financial reporting standards. This study synthesizes academic literature through a Systematic Literature Review (SLR) of Scopus and Web of Science databases, following PRISMA guidelines. Thematic analysis reveals three critical themes: (1) AI’s transformation of audit processes through improved risk assessment and substantive testing, alongside emerging concerns about evidence reliability and algorithmic “black boxes”; (2) ethical and epistemological challenges to auditors’ roles in maintaining professional judgment and skepticism within algorithmic environments; and (3) mounting pressure on the IFRS framework to accommodate AI-driven business models, data-intensive assets, and novel valuation techniques. The study concludes that while AI can enhance assurance quality, it necessitates concurrent development of new auditing standards and a future-oriented revision of the IFRS Conceptual Framework to ensure sustained relevance and reliability. This synthesis establishes a research agenda for standard setters, practitioners, and academics, highlighting gaps in understanding the interplay between technological innovation and accounting’s conceptual foundations.]]>
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