Healthcare IT leaders are at the forefront of one of the most important transformations in modern care: the rise of agentic AI. Agentic AI promises to automate workflows, reduce clinician burden, and unlock powerful insights from vast amounts of patient data. But the success of agentic AI depends on more than just algorithms—it requires a secure, scalable, and resilient infrastructure to support the complex, high-volume workloads AI demands. Without the right foundation, even the most advanced AI tools risk underperforming or exposing health systems to compliance, cost, and security challenges¹. Throughout this article you’ll discover why investing in next-generation infrastructure is critical to achieving sustainable AI success in healthcare.
Unlocking Hidden Value in Healthcare Data
Healthcare generates more data than any other sector—yet less than 3% is effectively used.² Inefficient systems incapable of processing multi-modal data force clinicians to manually sift through charts and records, slowing care and straining resources. Agentic AI, powered by secure and scalable infrastructure, harmonizes disparate data streams into usable insights. This shift frees clinicians from paperwork and channels the hidden value of data directly into patient care.
Modernizing Core Infrastructure for Flexibility and Agility
Legacy IT systems cannot keep pace with agentic AI. By 2025, 94% of organizations will operate across multiple cloud environments, while 79% will revert some services back on-premises for control—underscoring the importance of hybrid, adaptable infrastructure³. Elastic computing, low-latency connectivity, and scalable storage are essential. Deloitte projects that AI-driven workloads will increase by at least 20% across every IT environment in the next year⁴. To support this demand, IT leaders must modernize infrastructure today to ensure reliability tomorrow.
Protecting Patient Trust with Enterprise-Grade Security
AI adoption in healthcare depends on trust. HIPAA compliance and data protection must be embedded into every system. A recent framework for HIPAA-compliant agentic AI highlights the role of attribute-based access control, PHI sanitization, and immutable audit trails as safeguards against breaches⁵.
Beyond compliance, enterprise-grade protections such as zero-trust architectures, advanced encryption (at rest and in transit), and continuous threat monitoring are essential to defend against increasingly sophisticated cyberattacks targeting healthcare organizations. Embedding these principles into infrastructure design not only reduces risk and prevents costly breaches but also ensures that AI systems can be scaled confidently across clinical and operational domains.
When security is treated as the foundation of innovation rather than a barrier, health systems gain a critical advantage: the confidence of patients, providers, and regulators. Trust is the currency that enables safe, scalable AI adoption, and it is earned through uncompromising commitment to security at every layer of infrastructure.
Scaling for Resilience
Healthcare demand fluctuates dramatically—from seasonal surges to pandemic spikes that can strain every corner of the system. For CIOs, CMIos, CTOs, and other IT and IT-adjacent leaders, the ability to scale is not just about handling more data or ensuring adoption by more users but rather ensuring that critical services remain available when patients and clinicians need them most.
Elastic infrastructure as a safety net: Scalable, cloud-based infrastructure allows health systems to expand or contract dynamically, minimizing downtime while maintaining continuity of care. For IT leaders, scalability is not optional; it is the foundation of resilience.
AI-specific scaling requirements: Agentic AI workloads often require rapid bursts of compute power for tasks such as analyzing multi-modal patient data, generating treatment insights, or orchestrating best next action and other multi-step workflows across systems. Without resilient scaling, these models can stall and create delays in clinical decision-making.
Resilience beyond volume: Scalability underpins disaster recovery and business continuity planning. Health systems with elastic, distributed infrastructure can reroute workloads and maintain performance even when local systems are compromised by cyberattacks, natural disasters, or hardware failures.
Strategic imperative: As IDC emphasizes without scalable infrastructure, even the best AI use cases fail to achieve mission-critical impact⁶. Resilience is not an “add-on”; it’s the foundation that allows Agentic AI to operate reliably under pressure, delivering consistent value across clinical and operational settings.
In short, scalability ensures resilience, and resilience is what transforms agentic AI from an experimental tool into a trusted partner in healthcare delivery.
Demonstrating ROI Through High-Impact Use Cases
AI succeeds when it solves real problems quickly. By starting with targeted high-value workflows such as revenue cycle optimization, patient scheduling, or clinical documentation, IT leaders can demonstrate immediate impact. Early wins generate financial savings while also building organizational confidence to expand AI adoption. Research underscores this “start small, scale fast” approach. Modular, platform-based AI allows organizations to deploy discrete agents in focused areas, then expand iteratively. This modularity creates scalability, resilience, and adaptability, since individual agents can be updated without disrupting the entire system¹.
By choosing practical, high-ROI use cases first, health systems prove value, reduce resistance, and establish the foundation for broader AI success.
Driving Adoption with Workflow Integration and Observability
Clinician adoption determines AI success. Even the most advanced models will fail if they disrupt established care processes or require clinicians to leave their core tools. AI solutions must be embedded seamlessly into EHRs and operational systems, enhancing workflows rather than disrupting them. Modular standards-based integration increases adoption likelihood and long-term improvement¹. Yet adoption alone is insufficient.
However, adoption without reliability is fragile. With 78% of global organizations already relying on AI, infrastructure observability, continuous monitoring of GPUs, latency, and throughput, is critical to maintainperformance¹.
Equally important is the fusion of observability with governance. Monitoring by itself highlights problems, but governance frameworks ensure accountability, auditability, and alignment with clinical and ethical standards. Together, workflow integration, observability, and governance create a feedback loop: clinicians adopt and use AI confidently, IT teams maintain high performance, and executives see measurable return on investment.
Combining workflow integration with observability and governance ensures resilience, adaptability, and sustained competitive advantage for health systems embracing Agentic AI.
Conclusion
By 2028, 33% of enterprise software applications will incorporate agentic AI, up from less than 1% in 2024, allowing 15% of daily work decisions to be made autonomously.7 This can transform healthcare operations and patient care, but only if it is supported by infrastructure that is modern, secure, scalable, and observable. For health system IT leaders, infrastructure is no longer an afterthought; it is the strategic enabler of success. The question is no longer whether to modernize, but whether your organization is ready now. The future of AI leadership in healthcare will be defined not by those who experiment first, but by those who build the strongest foundations.
References
- TechRadar. (2025). The need for robust AI infrastructure. TechRadar Pro. https://www.techradar.com/pro/the-need-for-robust-ai-infrastructure
- GE HealthCare. (2024, June). How agentic AI systems can solve the three most pressing problems in healthcare today. https://www.gehealthcare.com/insights/article/how-agentic-ai-systems-can-solve-the-three-most-pressing-problems-in-healthcare-today
- PwC. (2025). Google Cloud and PwC: Healthcare AI insights. PwC. https://www.pwc.com/us/en/technology/alliances/google-cloud/healthcare-ai-agents-solutions.html
- Deloitte. (2024). Growing demand for AI computing: The impact of AI-driven workloads on infrastructure. Deloitte Insights. https://www.deloitte.com/us/en/insights/topics/emerging-technologies/growing-demand-ai-computing.html
- Neupane, A., et al. (2025). A framework for HIPAA-compliant agentic AI systems. arXiv preprint. https://arxiv.org/abs/2504.17669
- IDC & AMD. (2025). Infrastructure challenges for scaling AI: IDC Spotlight Report. AMD. https://www.amd.com/content/dam/amd/en/documents/solutions/data-center/insights/enterprise-ai-insights-infrastructure-challenges-scale-ai-idc-spotlight.pdf
- Lovejoy, K. (2025, September 11). Agentic AI: The reality behind the hype. Kyndryl. https://www.kyndryl.com/us/en/about-us/news/2025/09/agentic-ai-fact-vs-fiction
