Regulated Industries Face Growing AI Infrastructure and Governance Challenges, Nutanix Finds

Organisations across healthcare, financial services and the public sector are accelerating AI adoption, but infrastructure readiness, data sovereignty, security and governance remain significant challenges, according to new findings from Nutanix’s eighth annual Enterprise Cloud Index (ECI) research.

The industry-specific reports examine how organisations in these highly regulated sectors are adapting their infrastructure strategies as AI workloads become more widespread. The findings indicate that while AI adoption is increasing, organisations continue to face challenges in preparing infrastructure to support these workloads at scale.

The research also highlights concerns around so-called shadow AI, where employees or business units use AI applications without formal approval or oversight from IT and security teams. Organisational silos between business and technology teams can further complicate governance and increase the potential for security and compliance issues.

“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 challenges are particularly relevant in the Middle East and Africa, where organisations in regulated industries are increasingly deploying AI across operational and customer-facing environments.

“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.

Healthcare organisations are increasingly exploring AI for clinical and administrative applications, but the need to protect sensitive patient information is shaping infrastructure decisions. According to the Nutanix Healthcare ECI Report, 72% of healthcare IT leaders identify data sovereignty as a top infrastructure priority. Meanwhile, 83% consider unauthorised shadow AI tools a critical business and data risk.

Generative AI is expected to be the most widely adopted AI capability among healthcare organisations over the next three years, with 62% of respondents identifying it as an expected use case. Agentic AI or autonomous agents follows at 57%, while predictive analytics and machine learning models account for 55%.

The findings suggest that healthcare organisations are increasingly turning to containerised applications and hybrid infrastructure to support AI workloads while maintaining control over sensitive data and meeting regulatory requirements.

Financial institutions are also increasing AI adoption across areas including customer service, anomaly detection, personalisation and core technology operations. The Nutanix Financial Services ECI Report found that 86% of financial-sector executives consider unmanaged shadow AI tools a severe business risk. Meanwhile, 62% of financial services IT leaders expect conversational and agentic AI to have a material impact on customer or employee experiences.

AI adoption is also contributing to increased use of application containers. According to the research, 90% of financial services IT leaders said AI is meaningfully accelerating container adoption. Data sovereignty remains a major consideration in infrastructure decisions. While 79% of financial services respondents identify data sovereignty as a high-priority or essential requirement, public cloud usage stands at 62%.

The findings point towards greater use of hybrid cloud infrastructure and containerisation as financial institutions seek to support AI workloads while addressing data protection and regulatory requirements. Government and education organisations are similarly increasing their use of AI while facing infrastructure and governance challenges.

The Nutanix Public Sector Report found that 91% of government and education IT leaders surveyed believe unvetted AI use creates severe mission and security risks. Infrastructure readiness is another concern. Some 73% of public sector infrastructure is currently considered unready to run complex AI workloads on-premises, according to the research.

At the same time, 87% of public sector technology leaders expect their reliance on application containerisation to increase over the next three years. Public sector organisations are using AI for applications ranging from benefits eligibility and fraud detection to other operational and administrative processes. However, infrastructure readiness, workforce capabilities and governance remain barriers to wider adoption.

The findings indicate that modern hybrid infrastructure will play an increasing role as public sector organisations seek to accommodate AI workloads while maintaining control over sensitive government and citizen data. Across the three sectors, the research points to a common challenge: organisations need to modernise infrastructure while ensuring that AI deployments meet security, regulatory and data sovereignty requirements.

The Nutanix ECI research was commissioned for the eighth consecutive year and conducted by Wakefield Research in November 2025. It surveyed 1,600 cloud, IT and engineering executives at organisations with at least 500 employees across 14 countries, including India and Saudi Arabia.

The findings suggest that as AI moves from experimental deployments towards broader production use, regulated industries will need to address not only the technology required to run AI workloads but also the governance frameworks and infrastructure controls needed to deploy them responsibly.