Cloud

From Storage to Data Cloud: How AI Is Forcing a Rethink of Enterprise Infrastructure

Artificial intelligence is pushing traditional storage to its limits, driving a shift to unified data platforms.

Bob is the managing editor of BizTech magazine.

Artificial intelligence (AI) is getting all of the headlines, but IT infrastructure is where the real pressure is building. Across industries, organizations are discovering that traditional storage architectures — built around silos, manual provisioning and rigid scaling — simply can’t keep up with modern data demands. In response, Everpure is advancing a new model: the Enterprise Data Cloud.

At a high level, the idea is straightforward. Instead of managing fragmented storage systems across on-premises and cloud environments, IT teams operate a unified data platform that abstracts complexity and delivers cloudlike agility everywhere.

But according to Shawn Rosemarin, Everpure’s vice president of research and development for customer engineering, the shift is long overdue. “Storage is the last piece of the data center that really never grew up,” he says. “If you look at compute, networking, applications — they’ve all evolved dramatically. Storage has largely stayed the same.”

Why AI Is Breaking Traditional Storage Methods

The rise of AI has exposed just how outdated many storage environments have become. Unlike traditional enterprise applications, AI workloads demand extreme performance, massive scale and seamless access to distributed data. That combination is pushing legacy architectures beyond their limits.

“The biggest issue is getting data to GPUs fast enough,” Rosemarin explains. “Traditional storage was never designed to deliver tens of terabytes per second of throughput.”

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At the same time, organizations are struggling to unify data spread across silos — databases, file systems, object storage and cloud platforms — into something usable for AI models. “Companies are swimming in data,” he says. “But they either don’t have the data they need, or it’s not in the format they need to use it effectively.”

Complicating matters further, AI introduces conflicting infrastructure demands. Training workloads require massive, sustained throughput, while inference workloads depend on low, predictable latency.

“Most architectures force you to choose,” Rosemarin says. “But organizations need to support both.”

What the Enterprise Data Cloud Looks Like in Practice

The Enterprise Data Cloud isn’t about buying a specific product. It’s about changing how data is managed across its lifecycle. Rosemarin describes a model built on three layers: storage management, data set management and data intelligence, all unified under a single control plane.

“It’s about having one unified way to operate and manage data, regardless of where it lives,” he says. “The boxes are not the interesting part anymore. What matters is the operating model.”

For midmarket IT teams, that shift is less about adding complexity and more about removing it. That includes eliminating technical debt through nondisruptive upgrades; standardizing operations across block, file and object storage; and gaining centralized visibility into the entire environment.

“I don’t want separate tools for on-prem and cloud,” he adds. “I want one control plane, one set of policies and one way to manage everything.”

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How IT Teams Gain Efficiency, Resilience and Control

One of the clearest advantages of the data cloud model is operational simplification. Rather than relying on manual processes and fragmented tools, organizations can automate provisioning and management through policy-driven controls. In practice, that means storage resources can be deployed in seconds with consistent configurations, reducing the risk of human error and eliminating the inconsistencies that often arise in large environments.

“It’s about having storage provisioned on demand in seconds, with the right policies applied automatically and continuously validated,” Rosemarin says.

This shift also strengthens cyber resilience. Instead of treating data protection as a downstream function, modern platforms build safeguards directly into the storage layer. Capabilities such as immutable snapshots and rapid recovery options enable organizations to return to a known-good state much faster than traditional, backup-based approaches, which can be slow and disruptive.

At the same time, efficiency is becoming a top priority as data volumes surge and budgets tighten. By standardizing on a unified platform and continuously optimizing performance through software, IT teams can extract more value from existing infrastructure without constantly adding capacity or head count.

For organizations looking to modernize, the path forward starts with reducing technical debt, particularly by moving away from disruptive upgrade cycle, and adopting a consistent operating model across environments. Just as important is a shift in how infrastructure is consumed: Increasingly, organizations are prioritizing predictable service levels and outcomes over fixed hardware specifications.

As AI adoption accelerates, the demands on infrastructure will only grow. But the bigger shift is already underway: treating data not as something to store, but as something to activate.

“The paradigm has changed,” Rosemarin says. “Data is now central to how organizations create value.”

Ready to modernize your data infrastructure for artificial intelligence? Connect with CDW and Everpure to learn how a unified enterprise data cloud strategy can simplify operations, strengthen resilience and accelerate business outcomes: