Data intelligence is emerging as a key differentiator for enterprise AI and autonomous infrastructure as companies discover that copying information into dashboards and data lakes is too slow for agentic workloads. The shift is pushing IT teams to rethink architectures built for applications first and data second.

That debate is also reshaping how infrastructure companies frame their AI strategies. Breaking application silos and turning scattered repositories into live context for AI is now a core goal, according to Chadd Kenney (pictured), vice president of product management at Everpure Inc.

“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.”

Kenney spoke with theCUBE’s Christophe Bertrand and Alison Kosik at Pure Accelerate 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how governance and real-time data access could make enterprise AI more practical in production. (* Disclosure below.)

Autonomous infrastructure depends on live data intelligence

The next step requires a broader infrastructure reset. Rather than ask customers to centralize everything first, Everpure is using discovery and classification across on-prem, cloud and software-as-a-service repositories, often tied to a configuration management database, to map where data lives. That approach extends the reach of its FlashArray and FlashBlade platforms from storage into data intelligence.

“We typically integrate with a [configuration management database] like in ServiceNow, and it shows you each of your data endpoints,” Kenney said. “It spins up containers, interrogates the data and then brings it back to contextualize it and classify it. This knowledge map is what AI agents actually need to infer data across a wide swath of data.”

Autonomous infrastructure is becoming a key differentiator between organizations that achieve that goal and those that do not, according to Kenney. Organizations that successfully scale AI will need to move beyond simply managing infrastructure and develop a deeper understanding of the data that drives their operations.

“From the bottom up, they [will have] built an autonomous infrastructure,” he said. “Beyond that, there’s an understanding of the data, and they’re getting full use of it. If three years from now people don’t take advantage of this innovation, they’re going to still be stuck managing infrastructure and not actually understanding their data yet.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of Pure Accelerate 2026:

(* 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.)

Photo: SiliconANGLE