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The governance of artificial intelligence has emerged as a critical organizational capability as firms increasingly deploy AI systems that shape strategic outcomes, raise ethical concerns, and face expanding regulatory requirements. This article reviews the literature on organizational AI governance across management, information systems, and interdisciplinary scholarship. The analysis reveals that effective AI governance requires integration of three interconnected dimensions: ethical governance addressing bias, fairness, transparency, and accountability; strategic governance encompassing competitive positioning, oversight structures, and control mechanisms; and regulatory governance responding to evolving compliance mandates. The review identifies tensions between innovation imperatives and responsible governance, highlighting the need for organizations to balance rapid AI deployment with robust control systems. Key governance mechanisms include transparency frameworks, explainability tools, human oversight protocols, and risk management processes. The analysis concludes that AI governance must evolve from fragmented technical and compliance approaches toward integrated organizational systems that embed accountability across leadership, management, and operational levels.