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The rapid integration of artificial intelligence into higher education has produced a significant imbalance: a growing body of research addresses AI tools and their effects on student learning, while the institutional and leadership dimensions of AI governance remain systematically underexplored. This systematic review with thematic synthesis (PRISMA 2020) examines how indexed literature conceptualizes AI governance and leadership in higher education institutions (HEIs), what institutional and managerial factors operate as facilitators or barriers, what structural tensions characterize the field, and what the Latin American context reveals about the limits of prevailing frameworks. Drawing on a corpus of 30 verified sources — 20 peer-reviewed studies (2021–2026) plus 10 foundational theoretical works included by exception — the analysis identifies five analytical themes: fragmented governance architectures without systemic accountability; the underresearched role of academic leadership as the critical institutional link; the theoretical limits of individual acceptance models (TAM/UTAUT) when applied to organizational-level decisions; datafication as covert governance operating beneath formal policy layers; and Latin America’s near-absence from indexed production as a structural finding rather than a methodological limitation. The study proposes an integrative conceptual framework articulating organizational ambidexterity (O’Reilly & Tushman, 2004, 2013), dynamic capabilities (Teece, 2007), and the critique of datafication (Williamson, 2018; Selwyn & Gasevic, 2020) as a unified analytical device for AI governance in HEIs, anchored in the specific conditions of Latin American higher education.