# Agentic AI in Healthcare: 10 Questions for Leaders > Source: > Published: 2026-06-17 16:00:38+00:00 Leaders in healthcare and life sciences organizations are entering a new phase of AI adoption. The technology is shifting from simply answering questions, taking dictations and coordinating tasks to orchestrating complex workflows using natural language prompts. This substantive shift matters. An AI agent can gather context, reason through steps, call tools, recommend actions and work in partnership with employees across a process. In the industry, that could help teams accelerate research, improve operational efficiency and support more timely decisions across clinical, commercial and regulatory work. It also raises the stakes. When AI moves from generating content to taking action, decision-makers need a higher level of confidence. They need to know which data an agent can access, which actions it can take, how outputs are governed and how teams can audit what happened. They also need a clear view of cost, infrastructure requirements and long-term flexibility. When AI moves from generating content to taking action, decision-makers need a higher level of confidence. The question is not whether agentic AI has potential. It does. The better question is whether the organization’s data, governance and collaboration strategy is ready for it. Before healthcare and life sciences organizations move agentic AI into production, executives should ask these 10 questions. ## 1. Which workflows are the best fit for agentic AI, and how will we measure impact? Not every process needs an AI agent. The strongest candidates are often workflows that are complex, repetitive, manual, data-intensive or spread across multiple systems or teams. In life sciences, agentic AI may support clinical trial feasibility, site selection, protocol analysis, regulatory content workflows, safety case triage, medical information responses, commercial field insights or supply chain planning. In healthcare, agentic AI may support prior authorization, care management, revenue cycle operations, quality reporting, provider network analysis, member engagement or patient access. Leaders should start with value, not novelty. Ask where agentic AI can improve measurable outcomes, such as: Reducing cycle time Improving productivity Increasing accuracy Reducing administrative burden Speeding research or commercialization Improving patient, member, provider or investigator experiences Clear metrics help keep AI efforts focused. They also help leaders decide which pilots should scale and which should stop. ## 2. Do we have the trusted data foundation agentic AI needs to work effectively at scale? The quality of agentic AI output depends heavily on context. If the data is fragmented, stale or difficult to govern, and lacks [semantic understanding](https://www.snowflake.com/en/blog/agent-context-layer-trustworthy-data-agents/), ontology and metadata, an AI agent may miss key information or produce an answer that appears useful but lacks the context needed for action. That is a real challenge in the industry. Critical data often spans a wide range of sources, including electronic health records, claims, labs, imaging, clinical trial systems, safety systems, commercial applications, supply chains and partner environments. Some data is structured. Some is semi-structured. Much of it is unstructured, including notes, documents, images, transcripts and scientific content. Executives must ask whether their data foundation can bring these sources together in a governed way. Agentic AI cannot efficiently scale on one-off extracts, duplicate pipelines or disconnected data marts. It needs secure access to reliable data across the organization. Executives must ask whether their data foundation can bring these sources together in a governed way. Agentic AI cannot efficiently scale on one-off extracts, duplicate pipelines or disconnected data marts. Organizations benefit from a platform that can unify, integrate, analyze and share data across teams, clouds and regions. For agentic AI, that foundation matters because agents usually need more than a prompt and a model. They perform best on governed access to and contextual meaning for the right data, at the right time, for the right user and workflow. ## 3. How will we govern what AI agents can access, generate, recommend and do? Governance changes when AI starts to act. With analytics, governance often focuses on who can access a report or data set. With generative AI, governance expands to prompts, model outputs and usage. With agentic AI, it should also cover actions. Important questions decision-makers need to ask: What systems can an agent access? What data can it retrieve? What recommendations can it make? Which steps require human review? Which actions are off-limits? How will teams monitor activity? How will the organization audit decisions later? These questions are not theoretical. They can determine whether teams can scale agentic AI with confidence. Healthcare and life sciences organizations manage vast stores of protected health information, research data, commercial data and intellectual property. Organizations should build governance into agentic AI workflows from the start, not add it after a pilot succeeds. The goal is not to slow innovation. Strong governance gives teams clear rules, reduces uncertainty and helps business users adopt AI more safely. ## 4. Can our AI agents securely use data across the organization without creating more silos? Many agentic pilots start fast but create new complexity. A team chooses a tool, copies data into a separate environment and proves a narrow workflow. That approach can work for experimentation. It can also create more data movement, more governance work and more places where sensitive data must be protected. Decision-makers should ask how agents will securely access the data they need without creating another disconnected layer. For example, a clinical operations agent may need protocol documents, site performance metrics, enrollment data and patient population insights. A commercial agent may need approved content, customer relationship management data, market access information and field insights. And a healthcare operations agent may need claims, provider, call center and care management data. These workflows cross systems and teams. They also involve sensitive data. The architecture matters. A strong agentic AI strategy should keep data connected, secure and governed. It should also help teams reduce duplication, avoid unnecessary data movement and give agents controlled access to the information each workflow requires. ## 5. How can we avoid hallucinations and misguided or unreliable agentic outputs? Hallucinations are a known risk with generative AI. With agentic AI, the risk becomes more serious because outputs may influence downstream actions. Leaders should ask how agents will stay grounded in approved, current and relevant information. The answer should include more than model selection. It should cover data quality, a robust semantic layer, retrieval, access controls, business rules, workflow context and human review. Leaders should ask how agents will stay grounded in approved, current and relevant information. The answer should include more than model selection. A regulatory team needs an agent to use current approved content. A clinical operations team needs recommendations based on the latest trial, site and enrollment data. A payer or provider team needs outputs that reflect policy, coverage, clinical and operational context. Better grounding starts with better data access, context and governance. AI agents should retrieve information from trusted sources, respect role-based access controls, and provide sufficient transparency so people can understand where their outputs came from. The practical question for executives is simple: Can the organization explain what data the agent used and why the output should be trusted? ## 6. Are we ready for privacy, security, compliance and auditability in production? A proof of concept can run in a controlled environment. Production AI has to meet a higher standard. When agentic AI supports real work, executives need clear answers about how sensitive data is accessed, where it is processed, which models are used, how outputs are retained and how activity is monitored. In healthcare and life sciences, that may involve requirements tied to HIPAA, HITRUST, good practice (GxP), HITECH, FedRAMP or internal policies for regulated data. Executives should ask whether the organization can audit agentic AI activity across the full workflow. That includes prompts, data sources, model responses, tool calls, user approvals and final actions. Auditability builds trust. It also helps teams improve AI workflows over time. When organizations can see what happened, it helps them validate performance, identify risk, refine controls and scale successful patterns across more use cases. ## 7. Are we building AI capabilities we control, or creating long-term dependency? Many organizations move quickly by sending proprietary data to third-party AI platforms or building workflows around a single model provider. That can speed early experimentation. It can also create strategic risk. For organizations proprietary data is often a competitive advantage. It may include novel compound research, clinical trial insights, patient population patterns, commercial strategy, manufacturing intelligence, or provider and member insights. Leaders must ask whether their AI architecture protects that advantage. The organization should be able to choose the right model for the right task, change models as needs evolve and avoid rearchitecting its data and AI strategy around a single vendor. It should also maintain control over how proprietary data is used. Agentic AI should increase strategic flexibility. It should not lock the organization into a model, platform or architecture that becomes difficult to change later. ## 8. How do we know what each AI workload will cost before we run it? AI costs can be hard to predict, especially when usage moves from small pilots to enterprise adoption. Agentic AI can add more variability because agents may make multiple model calls, retrieve data, call tools and run multi-step workflows. For executives, cost transparency is part of responsible scaling. Leaders need to ask whether teams can estimate, monitor and control AI workload costs. Can the organization see what a workload may cost before running it? Can teams set budgets by project or business unit? Can teams shut down or adjust workloads before costs rise beyond the expected range? Executives need to ask whether teams can estimate, monitor and control AI workload costs. Cost governance matters because successful adoption can accelerate usage quickly. Without the right controls, teams may struggle to connect AI investment to business value. A mature agentic AI strategy should include data governance, security governance and cost governance. ## 9. How can agentic AI improve collaboration while keeping sensitive data protected? Work in the industry depends on collaboration. Providers, payers, pharmaceutical companies, biotech firms, contract research organizations, academic institutions, public health agencies and technology partners all need to work across organizational boundaries. But collaboration can be difficult when data is sensitive, regulated and distributed. Traditional approaches often require data copies, file transfers, custom integrations or lengthy review cycles. Agentic AI can add complexity if agents need to support shared workflows across partners. Leadership must ask how agentic AI can improve collaboration without weakening control. Could different research teams analyze shared data without exposing sensitive intellectual property? Could medical and commercial teams work from content that is governed and approved? Could providers and payers coordinate more effectively around population health, quality measures or care gaps while protecting patient and member data? Agentic AI should not force a trade-off between collaboration and control. The right approach can help organizations work together with live, governed data while keeping sensitive information protected. ## 10. How much AI infrastructure do we want our teams to maintain? Healthcare and life sciences organizations need AI capabilities. They do not need every team to become an AI infrastructure team. If agentic AI requires teams to manage model hosting, orchestration layers, infrastructure scaling, monitoring pipelines and custom machine learning operations for every use case, adoption slows. Scarce technical talent gets pulled into maintenance instead of higher-value work. Executives should ask how much infrastructure complexity the organization is taking on. A low maintenance approach can help teams focus on the work that creates value: building trusted data products, designing domain-specific workflows, strengthening governance, supporting business users and measuring outcomes. This is especially important in the industry, where domain expertise matters as much as technical expertise. Scientists, clinicians, regulatory experts, commercial leaders and operations teams need secure, governed ways to use AI in their daily work. They should not have to understand every layer of AI infrastructure to benefit from agentic AI. ## What it takes to scale agentic AI responsibly Agentic AI fundamentally changes how organizations operate. It can help teams move from insight to action, reduce manual effort and coordinate complex workflows across the enterprise. But the organizations that scale agentic AI successfully will not start with the model alone. They will start with the foundation around it. That means trusted data. Robust governance. Secure collaboration. Cost visibility. Model flexibility. In a nutshell, infrastructure that does not add unnecessary maintenance or financial or governance burden. These questions help decision-makers separate a promising pilot from an AI strategy that can effectively scale. Agentic AI can reward organizations that already treat data as a strategic asset. When agents work inside a governed data environment, with the right controls and the right business context, teams can move faster without losing the trust their work demands. Ready to see agentic AI in action? [Watch Accelerate Healthcare & Life Sciences](https://www.snowflake.com/accelerate/healthcare-life-sciences/) to hear from industry leaders as they showcase AI use cases and share how they’re enabling better healthcare outcomes with Snowflake’s AI Data Cloud for Healthcare & Life Sciences.