<![CDATA[The rapid integration of Artificial Intelligence (AI) into organizational strategy has intensified the need for robust data governance mechanisms that ensure data quality, accountability, and strategic alignment. While prior studies have examined data governance and AI strategy separately, a comprehensive synthesis explaining how Information Systems (IS) frameworks bridge technical data management and competitive advantage remains limited. This study addresses this gap by conducting a Systematic Literature Review (SLR) to synthesize key IS frameworks that govern data for AI-driven strategic outcomes. Following the PRISMA 2020 protocol, relevant peer-reviewed articles published between 2018 and 2026 were systematically collected from Scopus, Web of Science, and the AIS eLibrary. A total of 65 high-quality studies were selected for thematic and theoretical analysis. The findings reveal three dominant thematic clusters: algorithmic accountability and ethics, data pedigree and provenance, and the evolving strategic role of the Chief Data Officer (CDO). The synthesis further demonstrates a theoretical shift from static, compliance-oriented governance toward dynamic capabilities grounded in the Resource-Based View. This study contributes to IS and AI strategy literature by re-conceptualizing data governance as a second-order organizational capability that enables sustainable competitive advantage. Practical implications highlight the importance of governance agility, strategic alignment, and trust-building mechanisms in scaling AI initiatives.]]>
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