Understand the infrastructure challenges slowing AI adoption and how to overcome them
AI adoption is accelerating, but most enterprises are running into four infrastructure barriers: container complexity, distributed AI deployment, shadow AI, and rising sovereignty demands. This guide breaks down each challenge and shows the practical capabilities needed to overcome them, helping IT leaders modernize faster and support GenAI and agentic AI.
Artificial intelligence is transitioning from an experimental capability to a mission-critical necessity. As organizations race to harness the power of Generative AI (GenAI) and prepare for the next wave of agentic AI, legacy infrastructure has become an inhibitor to innovation.
According to the 8th Annual Nutanix Enterprise Cloud Index, almost two-thirds (59%) of executives reported that they anticipate their organization will have more than five AI-enabled applications within three years. Nearly a quarter (23%) expect to have more than ten.
But the path from insight to action is fraught with hurdles. This guide examines four critical infrastructure challenges enterprises face today: accelerating container adoption, deploying AI everywhere, preventing shadow AI, and protecting data sovereignty. It explains why these challenges are emerging, how they impact operational efficiency and risk, and what IT leaders can do to address them.
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Many organizations struggle to move from early pilots to production because of fragmented data, limited GPU access, siloed teams, and complex infrastructure decisions. These challenges can make it difficult to operationalize AI reliably across cloud, edge and on‑premises environments.
This guide is designed for IT leaders, cloud architects, platform engineering teams, and AI project owners who need practical insight into how to modernize infrastructure for GenAI and emerging agentic AI workloads.
Nutanix Enterprise AI (NAI) provides a unified platform designed to support AI workloads with high performance, strong data governance and hybrid cloud flexibility. It supports GPU‑enabled compute, Kubernetes®, scalable storage, and enterprise‑grade data services so organizations can deploy and manage AI like any other workload.
You’ll gain a clear understanding of the four infrastructure challenges slowing AI adoption and learn what capabilities are needed to overcome them. The guide provides practical guidance to help reduce complexity, improve consistency, and support AI deployment across diverse environments.
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