While generative AI adoption in healthcare is high, most pilots fail to reach production due to three core systemic gaps. First, poor data quality undermines trust—fragmented, unreliable patient data leads to inaccurate outputs and stalled deployments. Second, governance frameworks lag behind AI usage, creating security, compliance, and privacy risks that make leaders hesitant to scale. Third, workforce readiness is lacking, with significant gaps in data and AI literacy preventing clinicians from confidently validating AI outputs. These issues compound, increasing risks like misdiagnosis, compliance violations, and automation bias. To succeed, organizations must move from fragmented tools to unified data platforms that improve data quality, embed governance, and empower staff—enabling safe, scalable AI adoption that delivers real clinical and operational value.

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