Nutanix Healthcare ECI Report Finds AI Urgency Outpacing Infrastructure Readiness as Shadow AI Risks Mount

Bengaluru, June 25: Nutanix  a leader in hybrid multicloud computing, today published findings from the healthcare vertical edition of its eighth annual Enterprise Cloud Index (ECI) survey. The report, which examines infrastructure readiness, AI adoption, and containerisation trends across healthcare organisations globally, reveals a sector under mounting pressure. AI deployment is being driven from the top, shadow AI is proliferating across clinical and administrative functions, and the infrastructure required to support secure, compliant AI workloads at the point of care is not yet in place.

As AI moves from the data centre to the bedside, where up to 75% of healthcare data is expected to be generated, the stakes around infrastructure readiness, data sovereignty, and clinical governance have never been higher. The findings show that while healthcare IT leaders recognise the transformative potential of AI, including through autonomous agents and real-time clinical decision support, the organisational and infrastructure gaps required to realise that potential remain significant.

“Healthcare organisations across APJ are under growing pressure to adopt AI, but clinician demand is colliding with the readiness of the infrastructure underneath it. The impact extends beyond IT; it can affect the availability of critical systems, access to data, and ultimately, the continuity of patient care. For healthcare leaders, the priority is shift from reactive management and build a unified, hybrid approach that bridges the gap between data sovereignty compliance and the real-time, low-latency insights required at the patient’s bedside,” said Daryush Ashjari, Chief Technology Officer and VP of Solution Engineering, APJ at Nutanix.

Key findings from the 2026 Nutanix Healthcare ECI report include:

• Shadow AI is widespread and largely unmanaged: Seventy-nine percent of healthcare organisations encounter AI applications or agents being implemented by employees in non-IT functions, and 83% believe that AI tools and agents operating outside official oversight create business risk. The same proportion, 83%, say silos between business units and IT make it difficult to effectively execute technology initiatives, deepening the governance challenge as AI adoption scales.

• Infrastructure is not ready for AI at the point of care: Eighty-eight percent of healthcare IT leaders view their current infrastructure as not fully ready to support deploying AI workloads on-premises. This is a significant gap given that AI inference at the point of care, rather than via cloud-only processing, is increasingly seen as essential to eliminating latency risks in clinical settings. Single patient rooms can generate up to 7TB of data annually, and high-density device environments such as ICU beds can include 15 to 20 connected devices, requiring local, low-latency AI processing to maintain clinical continuity.

• AI is accelerating container adoption as healthcare modernises its application strategy: Eighty-six percent of healthcare organisations say AI is meaningfully accelerating their adoption of containers, which enable AI models to be deployed locally at the bedside in secure, portable environments. Eighty-one percent expect the level of application containerisation to increase at their organisation, and 80% are already building new applications in containers. Containers allow hospitals to keep data where it is generated, within their own walls, while enabling real-time AI-driven insights without compromising network performance.

• AI agents are seen as transformative for healthcare operations: Fifty-eight percent of healthcare IT leaders expect AI agents to improve productivity and efficiency, 57% anticipate agents will transform business processes and operations, and more than half (55%) see potential for AI agents to create new products, services, or revenue streams. Looking three years ahead, 57% of organisations anticipate using agentic AI or autonomous agents, alongside generative AI (62%) and predictive analytics or machine learning models (55%).

• Data sovereignty is a must-have, not a nice-to-have: Seventy-two percent of healthcare organisations say data sovereignty is a high priority or a must-include when making infrastructure decisions. Fifty-four percent run containerised applications on-premises or on private clouds today, and 54% feel the need to run infrastructure within a single country due to customer or stakeholder expectations. This reflects the sensitivity of protected health information (PHI) and the compliance requirements governing where this data can be stored and processed.

• AI adoption is being driven from the top, with scale coming fast: Fifty-five percent of healthcare organisations anticipate having more than five AI-enabled applications within three years, including 12% who expect to be running more than 10. Sixty-three percent currently run AI applications on managed service providers, with hybrid deployment models expected to remain the norm as organisations look to support AI centrally and at the point of care.

Together, these findings paint a clear picture: healthcare organisations are accelerating into AI without the infrastructure beneath them to support it safely. Closing the gap from the data centre to the bedside requires a fundamental rethink of how healthcare IT is architected, governed, and scaled.

Where the data meets the bedside

The findings point to a sector at critical stage in its innovation journey. AI is more than just a consideration for healthcare and is already running in containerised environments, across hybrid infrastructures, and increasingly at the point of care. But the picture underneath is messy as organisations are juggling workloads across on-premises systems, private clouds, and managed services simultaneously, often without the unified infrastructure strategy to support them consistently or safely.

For healthcare IT leaders, this creates a clear mandate. AI workloads at the bedside demand infrastructure that can deliver performance, maintain regulatory compliance, and support clinical governance. Not just centrally, but locally, where latency and continuity directly affect patient outcomes. A cloud-only approach is no longer sufficient.

Clinical continuity and patient outcomes depend on healthcare organisations fundamentally rethinking how they architect for AI by building secure data environments, scalable compute, and reliable application delivery across the entire business.