Nutanix today unveiled new regulated industry data from its eighth annual Enterprise Cloud Index (ECI) survey and research report shared earlier this year. Serving as a deep-dive companion to the global report, the Healthcare, Financial Services, and Public Sector industry reports reveal how organizations are adapting infrastructure strategies to support growing AI adoption.
Nutanix’s ECI survey indicates that enterprise AI adoption is accelerating across healthcare, financial services, and public sector organizations, but many reported that their infrastructure is not ready to run these workloads at scale. The report identified concerns around shadow AI risks, data sovereignty, compliance, and organizational silos as organizations work to modernize hybrid multicloud environments for AI applications.
While Nutanix’s global ECI survey illustrated a baseline of challenges with AI infrastructure readiness among IT decision-makers, these new analyses reveal that the pressure is significantly magnified within these highly regulated sectors. Enterprise AI adoption is accelerating rapidly across healthcare, financial services, and public sector organizations, yet IT leaders in these fields report a much sharper infrastructure deficit. Because of strict regulatory mandates, these specific industries face heightened exposure to shadow AI risks and data sovereignty violations compared to the global average, transforming generic organizational silos into critical compliance vulnerabilities as they attempt to modernize hybrid multicloud environments.
“Organizations across every industry are working to move their AI projects from experimentation to delivering real business value, but the infrastructure requirements vary significantly depending on sector and workload,” said Thomas Cornely, EVP of Product Management at Nutanix. “The one consistent factor is a need for infrastructure and operating models that deliver flexibility, resiliency, and security to run both traditional and AI-powered applications at scale.”
The rise of shadow AI, when employees or business units use AI tools outside approved IT governance or security oversight, is an ongoing concern that highlights governance gaps in these highly regulated industries. Additionally, organizational silos between business units and IT teams add to the potential complexity of AI use and governance.
“Across the Middle East and Africa, organizations in highly regulated sectors are embracing AI to improve services, accelerate innovation, and strengthen resilience. But as AI moves from experimentation into everyday operations, the conversation is shifting from what AI can do to how it can be deployed responsibly. Data sovereignty, security, and governance are becoming essential foundations for AI adoption, particularly in healthcare, financial services, and the public sector. Organizations that modernize their infrastructure with these requirements in mind will be better positioned to capture the benefits of AI while maintaining the trust of customers, citizens, and communities,” said Mohammad Abulhouf, Vice President & GM, Middle East & Africa, Nutanix.
Key findings from this year’s report by industry, based on survey responses, include:
Healthcare: AI Innovation Must Balance Security and Compliance
As healthcare organizations transition AI workloads directly to the bedside, application containerization has become foundational to supporting this innovation by providing secure, portable environments that eliminate cloud latency and protect data sovereignty. However, balancing these modern deployments with general on-premises infrastructure readiness remains the industry’s primary operational hurdle.
- According to the Nutanix ECI Healthcare Report, 72% of healthcare IT leaders cite data sovereignty as a top infrastructure priority.
- Data from the Nutanix study reveals that 83% of healthcare organizations view unauthorized “shadow AI” tools as a critical business and data risk.
- The Nutanix ECI study revealed the top AI applications or capabilities healthcare organizations expect to use within the next three years include generative AI (62%), agentic AI or autonomous agents (57%), and predictive analytics or machine learning models (55%).
AI adoption in healthcare is accelerating, driven by the need to improve operational efficiency, enhance patient outcomes, and support innovation in both clinical and administrative workflows. However, challenges such as infrastructure readiness, shadow AI, and operational barriers remain significant. Containers are playing a key role as healthcare organizations seek to support innovation while protecting sensitive patient data and meeting regulatory requirements.
Financial Services: Resilience and Governance Drive AI Strategies
Financial institutions are aggressively deploying AI to optimize everything from core systems to edge operations, including in-branch personalization, customer service, anomaly detection, and more. However, strict data sovereignty requirements are forcing a shift toward secure, hybrid cloud architectures.
- The Nutanix Financial Services ECI Report found that 86% of financial sector executives believe unmanaged shadow AI tools introduce severe business risk.
- Research from the Nutanix study indicates that 62% of financial services IT leaders expect conversational and agentic AI to materially improve customer or employee experiences.
- According to Nutanix’s report, 90% of financial services IT leaders report AI is meaningfully accelerating container adoption.
While financial services organizations continue to improve infrastructure to support AI adoption, data protection and other concerns limit public cloud use to just 62% despite 79% citing data sovereignty as a high priority or must-have factor. Containers and hybrid infrastructure are emerging as critical components needed for scaling AI responsibly to support high-performance workloads from core systems to the edge, including in-branch personalization, point-of-sale anomaly detection, and predictive ATM maintenance.
Public Sector: Modernization Efforts Accelerate Amid Infrastructure Challenges
Faced with a dual mandate to advance mission outcomes and safeguard public data, government agencies are turning to application containerization to improve the speed, scalability, and security of their AI workloads, even as organizational silos increase the risk of unmanaged shadow AI.
- Government and education IT leaders surveyed in the Nutanix Public Sector Report overwhelmingly agree (91%) that unvetted AI usage creates severe mission and security risks.
- A key takeaway from the Nutanix ECI research is that 73% of public sector infrastructure is currently unready to run complex AI workloads on-premises.
- Findings from the Nutanix industry study indicate that 87% of public sector technology leaders expect their reliance on application containerization to scale up over the next three years.
The findings show that public sector organizations, including federal, state, and local governments, K-12, and higher education, are incorporating AI into business operations ranging from benefits eligibility to fraud detection. Yet, they are facing barriers in infrastructure readiness, workforce capability, and governance. This drives the urgent need for public sector IT leaders to build modernized hybrid infrastructure that can support the growing needs of this diverse industry.
