The integration of artificial intelligence (AI) in higher education presents significant pedagogical opportunities and institutional risks that require a strategic and multifaceted response. In pedagogical terms, the literature and empirical findings agree that AI can enhance the personalization of learning through adaptive pathways and recommendations based on behavioral analysis, which enhances engagement and, in some contexts, academic performance. In parallel, there are significant ethical, technical, and organizational risks. Cited authors highlight concerns about data privacy, algorithmic bias, the validity of AI-generated outputs, and threats to academic integrity (eg, the potential for generating content that facilitates plagiarism). These risks are reflected in the need for ethical and governance frameworks: data protection policies, explainability of algorithmic decisions, bias audits, and traceability protocols. Therefore, the central recommendation is to adopt an integrated and phased institutional strategy that emphasizes people, processes, and policies rather than technology alone. This involves (1) creating governance structures and ethical frameworks that define technical requirements and periodic audits; (2) deploying competency-based training programs with impact indicators and ongoing support; and (3) implementing disciplinary pilots with monitoring, evaluation, and institutional learning mechanisms to adjust and scale. This will maximize the benefits—personalization, efficiency, and improved evaluation processes—while mitigating risks related to ethics, equity, and academic integrity, ensuring that AI acts as a tool to enhance, not replace, educational work