Artificial intelligence (AI) promises substantial gains in healthcare, yet Mexico’s adoption remains hindered by a mismatch between sophisticated algorithms and unresolved system fundamentals. This paper presents a clinically realistic, evidence-informed framework that distinguishes between use cases for screening, diagnosis/monitoring, and administrative optimization, arguing that the most significant near-term value lies in logistics and burden reduction. We inventory Mexico’s conditions—significant “data debt” due to fragmented, non-interoperable records alongside underused strategic assets such as national clinical repositories and highly motivated human capital—and translate them into three actionable principles: (1) prioritize digital infrastructure and interoperability before advanced analytics; (2) foster a collaborative, open-source ecosystem and regulatory sandboxes for safe, rapid validation; and (3) design for the “augmented doctor” via explainable AI integrated into clinical workflows. Success should be measured not by algorithmic novelty but by reduced administrative load, improved interoperability, and a resilient digital health ecosystem that enables subsequent AI gains at scale.