At Pure Accelerate 2026 in Las Vegas, Everpure Vice President of Product Management Chad Kenney outlined the company’s evolution from a storage provider into a broader data management platform designed for the AI era. He discussed Everpure’s rebranding from Pure Storage, the company’s focus on data intelligence and autonomous infrastructure, and why he believes the next major enterprise technology shift will be a move toward “data primacy,” where data becomes the central organizing principle for AI-powered business operations.
Kenney explained that while enterprises have invested heavily in AI models and agents, many organizations remain unprepared to leverage AI effectively because their data remains fragmented across applications, clouds, SaaS platforms, and infrastructure silos.
Core Takeaways
Beyond Storage: Everpure’s rebrand reflects a multi-year evolution from storage systems to autonomous infrastructure and now enterprise-wide data management designed for the AI era.
Shared Context for AI Agents: AI agents cannot make effective business decisions when limited to data inside a single application; enterprises need a shared context layer spanning CRM, ERP, ITSM, SaaS, and other systems.
Governance Through Data Intelligence: Building governance and access controls at the data layer enables organizations to provide AI agents with broader context while maintaining security, compliance, and role-based access control.
Data Primacy Is the Next Shift: Kenney believes enterprises are moving from an application-centric world toward data primacy, where understanding, contextualizing, and connecting data becomes more important than the applications themselves.
Key Quotes
The Rebrand Reflects a Much Larger Vision
“We started off building really great storage solutions, but over time we evolved into building autonomy around infrastructure. We began abstracting away the storage stack and helping customers manage infrastructure at a higher level. As we continued down that path, we realized customers needed help understanding and rationalizing their data, especially as AI introduced new challenges around silos and fragmented information.”
“The rebrand to Everpure reflects that evolution. We’re not moving away from storage, but we are expanding beyond it. We’ve gone from storage management to autonomous infrastructure to data management. We wanted our identity to reflect those broader ambitions and the larger role we now play in helping customers get value from their data.”
AI Agents Need Shared Context Across the Enterprise
“Many organizations are building agents inside individual applications, but those agents only understand the context available inside that application. If you build an agent inside a CRM system and ask it to approve profitable orders, it may know customer pricing but have no understanding of manufacturing costs, supplier expenses, or margin structures.”
“To answer those questions effectively, agents need shared context across the enterprise. They need to understand information from ERP systems, supply chain systems, CRM platforms, and other business applications simultaneously. That shared context becomes incredibly important because it allows agents to make decisions based on the full business picture rather than a narrow slice of information.”
Better Context Can Improve Security and Governance
“There’s a common concern that giving agents broader access to information will increase risk, but in many ways the opposite is true. Today, organizations often connect agents directly to multiple applications, creating fragmented access paths and limited governance.”
“By creating a shared context layer, you can introduce governance at a higher level. You can implement role-based access controls and define exactly what information agents can access. That creates stronger oversight and helps ensure agents only see the data they should have access to while still benefiting from broader enterprise context.”
Data Primacy Will Define the Next Era of Enterprise IT
“The data problem is larger than many organizations realize. Large language models and agents continue to improve rapidly, but enterprises still struggle to use their own data effectively because that data remains fragmented, siloed, and difficult to understand.”
“We believe we’re entering a period of data primacy, where data becomes the primary focus rather than the application stack. As organizations better understand, connect, and contextualize their data, they’ll be able to take much greater advantage of AI innovations and unlock entirely new business capabilities.”
