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Publication Date: 2026-02-10 07:07:00
According to Nutanix’s Enterprise Cloud Index study, more than half of global organisations say their IT systems require substantial upgrades before generative AI can be deployed at scale, while a similar proportion cite gaps in internal skills.
In a conversation with Frontier Enterprise, Nutanix executives Manosiz Bhattacharyya and Daryush Ashjari discussed where AI initiatives break down in practice, and what prevents organisations from scaling beyond early experimentation.
Infrastructure setbacks
According to Bhattacharyya, Chief Technology Officer at Nutanix, legacy three-tier architectures are a common constraint because they lack the agility, scalability, and data mobility that AI workloads require.
“Without a flexible foundation, it becomes challenging to move data and models securely across data centres, the edge, and the cloud,” he said.
A lack of readiness for containerisation further complicates the situation. Even as nearly 90% of global enterprises begin containerising applications, many still lack the tools and operational expertise needed to manage Kubernetes environments effectively. This limits the portability and elasticity that AI workloads depend on, Bhattacharyya added.
Security and compliance remain another pressure point. Around 95% of global organisations believe they could be doing more to secure generative AI models and…
