Artificial intelligence is quickly moving from experimentation to expectation in healthcare. Whether the goal is improving clinical outcomes, driving operational efficiency, or enabling value-based care, AI is increasingly embedded in both clinical and administrative workflows. But for many health systems, the biggest barrier to success isn’t the algorithm; it’s the underlying infrastructure, data, and analytics foundation.
Underpinning both clinical (EHR) and administrative (ERP) applications is the core IT infrastructure and data architecture of the healthcare organization. AI integration often pushes the limits of traditional IT, requiring enhancements in computing power, data management, and analytics tooling. Leaders need to ensure their infrastructure and data practices are ready to support AI at scale. Those that succeed are investing deliberately in five foundational capability areas.
1. Scalable Infrastructure Enables AI at Enterprise Scale
Traditional infrastructure designed for EHR and ERP systems often lacks the computing elasticity required for AI workloads. Cloud-enabled platforms provide the scalable compute, storage, and GPU acceleration necessary to train models, run predictive analytics, and operationalize AI solutions. Many health systems are adopting hybrid environments, maintaining resilient core systems while leveraging cloud platforms for analytics, model development, and integration. This approach ensures performance, scalability, and long-term flexibility without disrupting mission-critical operations.
2. Data Governance and Quality Are Foundational to Trustworthy AI
AI is only as effective as the data behind it. Strengthening enterprise data governance, including defined ownership, stewardship, data quality processes, and metadata transparency, is essential. Mature governance programs ensure data accuracy, privacy, and lineage, while enabling safe and compliant use of information. Beyond AI readiness, these investments also improve reporting, regulatory compliance, and operational decision-making across the enterprise.
3. Unified Data Platforms Unlock Enterprise Value
AI depends on the ability to integrate and analyze data across clinical, operational, and financial systems. Enterprise data platforms – supported by interoperability standards such as FHIR and modern integration engines – eliminate silos and create a unified environment for analytics. This consolidated data foundation enables advanced use cases, from predictive clinical decision support to operational optimization and population health management.
4. People and Skills Accelerate Adoption
Technology alone does not deliver value, and AI readiness is as much about people as platforms.
Organizations must develop or augment analytics and data science capabilities to build, validate, and operationalize AI solutions. Many health systems are establishing AI governance structures, analytics centers of excellence, or partnering with experienced advisory firms like HSi to accelerate adoption while ensuring safe and sustainable implementation.
5. Cybersecurity and Resilience Safeguard Clinical Operations
As data volume and complexity grow, cybersecurity and operational resilience become even more critical. AI systems must be protected with strong identity management, encryption, and monitoring, while ensuring fallback processes are in place to maintain continuity of care if systems fail or require intervention.
Building the Foundation for Sustainable Innovation
AI readiness is ultimately a leadership and infrastructure strategy. Health systems that invest in scalable cloud infrastructure, interoperable data platforms, and robust data governance now will be far better positioned to deploy cutting-edge AI solutions quickly and safely. Just as importantly, they will likely spend less time wrangling data and more time extracting actionable insights, giving them a competitive edge in improving outcomes and efficiency.
At HSi, we see the most successful organizations treating infrastructure, data, and governance not only as prerequisites to AI but as strategic enablers of transformation. Preparing infrastructure and data for AI is about building a strong, flexible foundation. Trends like sophisticated AI, value-based analytics, and M&A-driven integration are best addressed with a consolidated effort of infrastructure, applications, data management, change readiness, governance, and leadership. In other words, technology upgrades must go hand-in-hand with governance and teamwork.
