<![CDATA[Artificial Intelligence (AI) in public sector internal audit offers substantial promise for enhancing efficiency and accountability, yet its adoption remains limited due to organizational unpreparedness and institutional barriers. This study explores how organizational readiness and institutional challenges influence AI implementation in audit functions, using a Systematic Literature Review of 583 articles. The findings reveal that current literature is heavily centered on conceptual discussions of readiness and barriers, with minimal empirical evidence of real-world implementation. As its core contribution, this research proposes a relational conceptual framework in which readiness and barriers act as primary determinants of AI adoption, embedded within the broader context of digital transformation in the public sector. A phased implementation strategy is also recommended, beginning with readiness assessment, followed by pilot projects, internal reforms, and culminating in systemic integration and continuous evaluation. The study contributes not only to theoretical understanding of digital adoption but also offers a strategic foundation for regulators and oversight bodies to build a sustainable, data-driven public audit ecosystem.]]>
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