The rapid development and increasing organizational deployment of AI, especially in high-risk application areas and under evolving regulation, create a growing need for effective AI governance. Hence, AI governance has attracted increasing attention in both academia and practice. While prior reviews have examined academic research on AI governance, practice-oriented grey literature has received less scholarly attention. This study examines how academic and grey literature differ in their treatment of AI governance. To this end, we review academic and grey literature on three focal objects of AI governance: AI data, AI algorithms, and AI models. Our findings suggest that academic literature tends to emphasize principles and pre-deployment governance, whereas grey literature more often foregrounds policies, implementation-oriented controls, and post-deployment oversight. Taken together, the two literatures appear to offer partly complementary perspectives on organizational AI governance. The study clarifies how AI governance is framed differently in scholarly and practiceoriented discourse and thereby contributes to Information Systems research and practice on organizational AI governance