Three insights you may have missed from theCUBE’s coverage of Pure Accelerate

As enterprises advance their artificial intelligence initiatives, they’re discovering that the real constraint isn’t model sophistication — It’s data. AI outcomes now depend on whether organizations can access, mobilize and operationalize data as an active system rather than a passive repository.

This shift was a defining theme at Pure Accelerate 2026. The challenge is not simply whether organizations can store data, but whether they can mobilize and operationalize it to achieve meaningful AI outcomes, according to Christophe Bertrand, principal analyst for cyber resiliency and data management at theCUBE Research.

“This is not about storage anymore. It’s about data,” Bertrand said in a keynote analysis during the event. “And that was very clear in the [chief executive officer’s] address. There’s a new data dynamic — which makes data sort of primary — and they call it data primacy. I think it’s a very good term, where data is really central to everything.”

During the event, Bertrand and co-host Alison Kosik talked with Everpure Inc. executives, partners and customers across industries about the governance, ecosystems and infrastructure capabilities required to turn AI ambitions into business outcomes.

Here’s the complete video discussion with Christophe Bertrand and Alison Kosik:

Here are three insights you may have missed from theCUBE’s coverage of Pure Accelerate 2026:

Insight #1: Governance and data strategy are becoming prerequisites for successful AI outcomes.

Organizations pursuing AI outcomes are discovering that technology is rarely the primary obstacle. Enterprises that treat governance as a foundational control layer rather than an afterthought are better positioned to manage data access, security and compliance as AI deployments scale, explained Lynn Lucas, chief marketing officer of Everpure, and Phil Goodwin, research vice president of multicloud data management and protection at IDC Corp.

“This pivot to more of a data-centric model rather than hardware-centric … is very well timed,” Goodwin said. “The research that we’ve done has shown that the real barriers to AI success start with governance. In fact, that’s the number one reason that AI projects fail. The second one is data access — the silos, the inability to bring in the data.”

Addressing those challenges requires more than new infrastructure. Everpure’s Enterprise Data Cloud Success Blueprint is a framework designed to help organizations assess data maturity, noted Stephanie Richardson, vice president of product marketing at Everpure. The approach reflects a broader shift toward data-centric operating models that align technology, processes and business objectives.

“Implementing technology is part of the solution, but it’s not the only thing,” Richardson said. “It really requires you to refactor your environment and start with a fundamentally different data strategy and a fundamentally different approach to your technology.”

Here’s the full discussion with Lynn Lucas and Phil Goodwin:

Insight #2: Organizations are turning to partner ecosystems to bridge the gap between AI investments and business value.

As AI initiatives become more complex, organizations are discovering that no single vendor can address every requirement, from data preparation and governance to infrastructure optimization and deployment. That reality is driving greater collaboration across the technology ecosystem to help customers transform raw enterprise data into AI-ready data and measurable AI outcomes, noted Shawn Rosemarin, vice president of R&D and customer engineering at Everpure, and Jason Hardy, vice president of storage technology at Nvidia Corp.

“I bought GPUs; now I need to reinforce it with the rest of the IT infrastructure to be able to drive that forward,” Hardy said. “The investment by itself isn’t enough. It’s the ecosystem that gets wrapped around it that is needed to drive forward and get … that outcome.”

Partners are also helping organizations address the operational challenges that impede AI investments from reaching production deployment. As customer conversations shift away from infrastructure specifications and toward business outcomes, organizations are placing greater emphasis on data preparation, governance and validation before making large-scale AI commitments, emphasized Justin Field (pictured, left), technical solutions architect at World Wide Technology Inc., and Hope Galley (right), vice president of Americas partner sales at Everpure.

“A lot of those talks have switched over to just the data preparation, and is the data even clean,” Field said. “No matter what you buy, it won’t give you good value if your data isn’t curated and contextualized and ready.”

Here’s the complete video interview with Shawn Rosemarin and Jason Hardy:

Insight #3: AI is forcing an infrastructure rethink — from data and virtualization to energy and cyber resilience.

As AI agents become more dependent on real-time information, organizations are rethinking infrastructure designed around isolated applications and duplicated data. Autonomous infrastructure is emerging as a way to provide greater visibility into enterprise data while reducing the operational burden of managing increasingly complex environments, according to Chadd Kenney, vice president of product management at Everpure.

“If you were able to break down those silos, take the context and share it across each one of these applications and then later build a system of record with all of that data consolidated, AI agents now could actually be running on top of real-time data versus this latent copy,” he said. “If they only have access to Salesforce data, they would have to infer what the costs are and maybe just make up what would be profitable or not. If they understood what suppliers were, what the costs were and also what the total product cost was, they could actually infer what a profitable order is and make that workflow work.”

As organizations pursue AI outcomes, they are increasingly reevaluating legacy virtualization platforms and moving Kubernetes-based virtualization from proof-of-concept projects into production environments. Customer deployments such as CSX Corp.’s are helping validate whether a unified platform can support both virtual machines and containerized workloads at enterprise scale, pointed out Greg Muscarella, general manager at Portworx Inc., a subsidiary of Everpure, and Eric Grabill, lead senior product manager of IT at CSX.

“The adoption by critical infrastructure like CSX — running this on tier zero-type applications and keeping the trains running on time — proves that we’ve gone beyond those questions,” Muscarella said. “If we can turn virtual machines just into another containerized workload, that seems to get us all down that path: You have one platform that can run your container applications already, and we can also bring along those virtual machines for the ride as well.”

Energy availability is becoming a strategic consideration as enterprises scale the infrastructure required to support AI outcomes. Crusoe’s energy-first approach reflects growing recognition that AI infrastructure requires new thinking about power generation, reliability and operational efficiency as organizations scale GPU-intensive workloads, according to Omar Lari, senior director of product management at Crusoe Energy Systems LLC.

“Crusoe’s mission is to accelerate the abundance of energy and intelligence,” he said. “Energy is going to drive the next breakthroughs in AI. AI will eventually help us make the next breakthroughs in energy.”

At the same time, cyber resilience is shifting from perimeter defense and immutable backups toward active protection of the data layer. As threat actors increasingly target data directly, organizations are looking for infrastructure that can help detect, defend and recover from attacks more quickly, explained Brandon Willitts, director of product management at Everpure, and Leerun Laizerovich, associate vice president of partner technical solutions and design at Commvault Systems Inc.

“It used to be that perimeter defense was where we invested heavily,” Willitts said. “We didn’t have access to the data layer, but we were seeing the threat actors really moving laterally through our environment, going after the data. You want to take storage out of this passive witness role and turn it into an active defender and connect it from all the way end to end, from your network down to your storage layer.”

Here’s the full interview with Brandon Willitts and Leerun Laizerovich:

To watch more of theCUBE’s coverage of Pure Accelerate 2026, here’s our complete video playlist:

(* Disclosure: TheCUBE is a paid media partner for the Pure Accelerate event. Neither Everpure, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

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