This research paper explores how generative AI applications—particularly domain-specific small language models (SLMs)—can be effectively and responsibly integrated into healthcare enterprises to address persistent data, operational, and governance challenges. Rather than focusing on speculative potential, it grounds AI adoption in real-world healthcare constraints, emphasizing the critical role of data readiness, interoperability, regulatory compliance, and clinical safety.

The paper introduces a comprehensive governance-first framework spanning AI, model, and data governance, offering healthcare organizations a structured approach to designing, developing, deploying, and monitoring AI systems across their full lifecycle with an emphasis on human in the loop. Through detailed use cases and empirical case studies, it demonstrates how SLMs can deliver clinically aligned, explainable, and privacy-preserving outcomes—supporting decision-making in areas such as care triage, administrative optimization, and oncology treatment guidance. Intended for healthcare executives, technology leaders, governance professionals, and policymakers, the paper serves as both a research contribution and an applied playbook, providing actionable guidance, implementation checklists, and a phased roadmap for moving beyond AI experimentation toward scalable, ethical, and value-driven adoption.

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