10 Key Questions About Agentic AI for Healthcare and Life Sciences Executives

According to the article, by 2026 agentic AI will become a reality in enterprise systems, but for it to be applied safely and effectively, leaders of healthcare and pharmaceutical organizations need to answer 10 key questions. First, it is necessary to determine which workflows (for example, clinical trial analysis, prior authorization, revenue cycle management) will benefit from the introduction of agents, and to establish success metrics (reduced cycle time, improved productivity and accuracy). Second, a reliable and well-governed data foundation that unifies disparate sources (electronic health records, lab data, imaging, insurance and commercial systems) is critical — otherwise agents will produce incomplete or incorrect answers. Governance must cover not only data access but also agent actions: which systems they can call, what data they can retrieve, what decisions they can propose, and which steps require manual confirmation. It is essential to avoid creating new data silos — the architecture should allow agents to securely access enterprise data without duplicating it. To prevent hallucinations, verified sources, role-based access, and transparency into the provenance of conclusions are required. Security, privacy, and auditability (HIPAA — the Health Insurance Portability and Accountability Act; GxP — Good x Practice; FedRAMP — the Federal Risk and Authorization Management Program) are mandatory for production deployment. The strategy should avoid vendor lock-in, allowing organizations to choose models suited to specific tasks while retaining control over their own data. Cost transparency is also necessary — the ability to estimate how much each multi-step agent call will cost and to set budgets accordingly. For collaboration across different organizations (contract research organizations, pharmaceutical companies, hospitals, insurers), agents must support secure data sharing without losing control. Finally, infrastructure should not become a burden for every individual team — it is better to use ready-made platforms, such as the Snowflake AI Data Cloud, so that domain experts can focus on their area of expertise rather than on machine learning operations.