What are the main storage challenges enterprises face when implementing AI?

Enterprises struggle with data siloed across different storage types (SAN, NAS, object storage) and locations (cloud, edge, multiple data centers), making it difficult to identify and access the right data for AI models. Additionally, high-performance AI workloads require specialized storage with highly parallelized capabilities to keep GPU clusters saturated with data.

Should enterprises focus on large language models or smaller specialized models?

While large language models with billions of parameters will likely remain cloud-based due to their scale requirements, most enterprises should focus on developing many small language models tailored to their specific needs. The key is building infrastructure that can ingest learnings from public cloud models and apply them to proprietary on-premises data.

Managing Data Storage for Enterprise AI Initiatives

Name: Managing Data Storage for Enterprise AI Initiatives
Uploaded: 2026-04-02T20:40:44-04:00
Duration: 4 min 53 s
Description: TL;DR Managing data across siloed storage systems (SAN, NAS, object, cloud, edge) has become critical for enterprise AI success, as organizations struggle with visibility into what data they have and where it resides. High-performance AI workloads requ...

Nutanix

04/02/2026

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TL;DR

Managing data across siloed storage systems (SAN, NAS, object, cloud, edge) has become critical for enterprise AI success, as organizations struggle with visibility into what data they have and where it resides.
High-performance AI workloads require highly parallelized storage capabilities to keep GPU clusters saturated, while typical enterprises need strategies for feeding the right data into models running at various locations.
AI initiatives may finally drive organizations to solve long-standing data visibility problems, as optimizing AI environments is impossible without comprehensive understanding of available data assets across the infrastructure.

Summary

Simon Robinson, principal analyst at Omdia, examines the critical challenges enterprises face in managing data across cloud and hybrid multicloud environments as they pursue AI initiatives. The discussion highlights how data visibility and accessibility have become paramount concerns, particularly as organizations build AI models that require specific data sets distributed across siloed storage systems including SAN, NAS, object storage, and edge locations. Robinson emphasizes that while massive GPU clusters demand highly parallelized storage capabilities to maintain performance, the broader enterprise challenge centers on understanding what data exists, where it resides, and how to effectively mobilize it for AI applications. He argues that AI could finally provide the business justification organizations need to solve long-standing data visibility problems, as optimizing AI environments becomes impossible without comprehensive data insight. The conversation also addresses the practical reality that while large language models will likely remain cloud-based due to their scale requirements, enterprises should focus on developing smaller, specialized models that can leverage cloud learnings while operating on-premises with proprietary data.

Chapters

0:00 - Data Management Complexity in Multicloud
0:52 - Storage Performance for GPU Clusters
1:28 - Data Visibility and Silos
3:00 - Cloud vs On-Premises AI Models

Key Quotes

0:00 "We talk a lot about storage, but the important part is the data. That's the stuff we care about."
2:24 "Organizations struggle with visibility into their data. They always have. And there's always been kind of, well, what is the benefit of solving that problem? I think AI could be a major enabler in persuading organizations to finally solve that problem."
4:07 "The important point is just start, just do something, right? Because the sooner you get going, the faster you're going to learn, the faster you're going to perhaps fail or make some mistakes, and then learn again and do it again."

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