Old Enterprise Storage No Longer Works for AI: What’s Next?

AI-driven workloads are pushing enterprise data storage to its limits and forcing businesses to rethink how they manage, move, and access their growing volumes of unstructured data.

Enterprises also deploy AI models, hybrid cloud solutions, and high-performance computing. But predictably, they are encountering bottlenecks that traditional storage architectures weren’t built to handle.

Then there are the infamous data silos that continue to hold businesses back. IT teams are also challenged to gain greater flexibility while avoiding vendor lock-in and unexpected budget increases.

So, what should they do as traditional enterprise cloud storage systems can’t cope with the growing AI needs?

Table of Contents Table of Contents

1. Key Takeaways
2. Why Traditional Storage Models No Longer Work for AI
3. Why AI Success Depends on Smarter Data Orchestration
4. Enterprises Need Storage Solutions That Outlive Their Infrastructure
5. Why Open Source Is Changing the Enterprise Storage Game
6. Why Rising Storage Costs Are Becoming an IT Survival Issue
7. The Bottom Line
8. FAQs
9. References

Show Full Guide

Table of Contents

1. Key Takeaways
2. Why Traditional Storage Models No Longer Work for AI
3. Why AI Success Depends on Smarter Data Orchestration
Show Full Guide
4. Enterprises Need Storage Solutions That Outlive Their Infrastructure
5. Why Open Source Is Changing the Enterprise Storage Game
6. Why Rising Storage Costs Are Becoming an IT Survival Issue
7. The Bottom Line
8. FAQs
9. References

Why Traditional Storage Models No Longer Work for AI

The shift toward data orchestration, intelligent tiering, and open enterprise storage is becoming a necessity rather than a luxury. One company at the forefront of this shift is Hammerspace, which has reported an extraordinary 10x revenue growth in response to the rising demand for AI storage and hybrid cloud solutions.

Hammerspace’s Global Data Platform was created to eliminate silos and ensure seamless access to unstructured data regardless of its location. Hammerspace’s approach is anchored in parallel network file systems (pNFS), a technology that separates metadata from data, allowing for extreme parallel performance without the usual bottlenecks.

David Flynn, CEO of Hammerspace, explained why storage performance alone isn’t enough to meet today’s AI-driven demands.

“The whole notion of data storage products as a place where you put data to be managed has to be replaced with the idea that data exists in an overarching, global namespace that enables applications to get to it, no matter where it resides.”

Why AI Success Depends on Smarter Data Orchestration

The key challenge isn’t just where data is stored but how it’s orchestrated across distributed environments. This need for orchestration is echoed by Krishna Subramanian, co-founder and COO of Komprise, a company specializing in unstructured data management.

“90 percent of data is unstructured. So, tying unstructured data to AI with proper data governance and security is key for a business. And that’s the problem we are focused on,” she said.

The surge in AI adoption is amplifying the urgency of tackling unstructured data. Subramanian also offered a timely reminder of the importance of recent events.

“DeepSeek has shown us that model training is only about 5 percent of the market. Ninety-five percent of the market uses a trained model, and the cost of model training will keep dropping over time. So yes, performance is important, but even more important is how your corporate data connects to AI because AI has already been trained on all the data in the public domain.”

Enterprises Need Storage Solutions That Outlive Their Infrastructure

Many enterprises operate in multi-cloud and hybrid environments, where data is scattered across multiple storage platforms. This fragmentation creates inefficiencies and leads to vendor lock-in, where companies must buy more storage to migrate their data.

Subramanian highlights the challenge:

“97 percent of businesses use more than one storage architecture and more than one storage vendor. The average data lifespan in an enterprise is 20 years, while the average lifespan of a storage solution is 3 to 5 years. So, your data will outlive your storage infrastructure. That’s why having a storage-independent solution is critical.”

Why Open Source Is Changing the Enterprise Storage Game

TrueNAS is another company that is taking a different approach to enterprise data storage solutions. Brett Davis, Executive Vice President at TrueNAS, believes that openness and collaboration are just as important as technology. He told Techopedia:

“All roads lead to open source. Open source eventually commoditizes the market, driving costs down and lowering barriers to entry. The traditional enterprise storage tax is too high. And that’s why we exist.”

This philosophy of open enterprise storage is reflected in TrueNAS’ widespread adoption. “60 percent of the Fortune 500 is using TrueNAS in some shape or form,” Davis stated.

Why Rising Storage Costs Are Becoming an IT Survival Issue

Storage costs aren’t just an affordability issue – they’re a matter of survival for many IT departments struggling with constrained budgets. At a recent Gartner conference, the number one challenge IT leaders raised was the growing storage cost. Davis reflects on this:

“IT organizations are asked yearly to do more with less. Data is growing, but budgets aren’t growing in lockstep. And now AI initiatives are blowing up storage budgets even further.”

The push toward AI-ready storage solutions is also raising concerns about data security. Enterprises are increasingly worried about how AI models interact with sensitive corporate data.

From her experiences dealing with customers at Komprise, Subramanian said, “A lot of times, you want to know more than just where your data is – you need to know if any of those files have sensitive data.” She also added:

“Is sensitive data sitting in places where it shouldn’t be? Because it’s a liability, especially when AI workflows are involved.”

Komprise has developed sensitive data detection capabilities to ensure businesses can manage their AI data workflows without introducing compliance risks. But will it be enough?

The Bottom Line

The need for intelligent, flexible, and cost-effective storage solutions will only grow as AI-driven workloads expand.

Whether through global namespaces, open enterprise storage, or independent enterprise data management, companies are moving toward models that reduce reliance on traditional vendor lock-in while improving efficiency and accessibility.

The future of AI storage isn’t just about capacity or performance – it’s about how well businesses can orchestrate, manage, and secure their data.

FAQs