For decades, enterprise IT built everything around applications. SAP owned finance. Salesforce owned customer relationships. ServiceNow owned IT operations. Each application came with its own data, its own definitions, and its own version of what words like "customer" or "asset" actually meant.
That model worked well enough — until AI showed up.
AI doesn't work the way applications do. It doesn't care which system owns which data. It needs to reason across everything at once, in real time, with consistent context. And when it tries to do that inside a fragmented, application-centric architecture, it fails. According to a June 2026 IDC survey commissioned by Everpure, 54% of AI proof-of-concept projects never reach production. That's a majority of AI budgets generating zero return on investment.
The root cause, according to IDC's research of more than 1,300 organizations, isn't compute. It isn't talent. It's data. Ninety-four percent of respondents said data quality is important or very important to AI project success. The top contributors to poor data quality are redundant data, multiple storage silos, and obsolete data.
At Pure Accelerate 2026 in Las Vegas, Everpure is making a direct argument that this is an architectural problem — and that fixing it requires inverting 50 years of enterprise IT assumptions.
The Problem with App-Centric Architecture
Charles Giancarlo, Chairman and CEO of Everpure, laid out the historical case in a keynote essay released alongside this week's announcements.
The original vision for large-scale ERP systems was a single semantic framework — one shared definition of "customer," "order," and "asset" across the entire enterprise. That vision never survived contact with real-world IT. A best-of-breed era arrived instead, with specialized applications for every business domain. Each brought its own definitions, its own data copies, and its own ETL pipelines to communicate with everything else.
SaaS accelerated the problem. Every new application brought more fragmentation, more copies, more integration work. The typical enterprise today has hundreds or even thousands of applications, each with its own data set, requiring costly transformation before the data can be used elsewhere.
In the AI era, this isn't just inefficient. As Giancarlo puts it, it's unsustainable.
"Customer" in a CRM means something different than "customer" in a billing system. "Asset" in an ERP is not the same object as "asset" in a supply chain platform. Multiply that across hundreds of applications, and you have a system in which cross-domain meaning must be reconstructed every time it's needed. AI fed on that fragmented, inconsistent data produces inconsistent results. In business, accounting, and R&D, 95% accuracy isn't good enough.
The Data Primacy Model
Everpure's response is what Giancarlo calls data primacy — a fundamental inversion of the hierarchy between applications and data.
In the data primacy model, data becomes the system of record. Applications read from and write to shared data, but they don't own it. Context and semantics travel with the data itself, not embedded inside the application that created it. Governance is enforced at the data layer rather than policed by external software.
This rests on three principles Giancarlo articulated this week:
Semantics must travel with the data. Operational data must carry meaning that can be understood outside the system that created it. This requires building semantic descriptions alongside data relationships that define how a piece of data connects to everything else in the enterprise. Raw data becomes self-describing, contextually rich, and ready for AI to reason over accurately.
Data must be conserved, not copied. Enterprises have been conditioned to duplicate data for every new application, analytics environment, and AI use case. Data primacy inverts that. A single authoritative source of real-time data, governed and semantically described, serves all applications and all agents. One version. One truth.
Governance must be embedded at the data layer. In an app-centric world, access is managed through role-based controls tied to individual applications. In a data primacy world, AI agents operate across multiple systems simultaneously. Access must be contextual and dynamic — driven by the attributes of the data itself, the identity of the requesting agent, and the purpose of the action. Conventional role-based access control simply wasn't designed for that.
What Everpure Announced
To deliver on data primacy, Everpure announced three platform innovations at Pure Accelerate 2026, organized around what the company calls the Enterprise Data Cloud.
Universal Data Intelligence
The centerpiece announcement is Everpure Data Intelligence, formerly known as 1touch.io. It's a data intelligence layer that discovers, classifies, and contextualizes enterprise data at its source — across Everpure storage, public clouds, SaaS applications, third-party storage, and legacy environments, including mainframes.
Data Intelligence delivers three core capabilities. Universal Discovery provides visibility into both structured and unstructured data, regardless of storage format, covering major databases, including SQL Server and Oracle. Automated Governance automatically scans the environment to identify sensitive data — PII, PHI — and tracks lineage to maintain a complete map of the data landscape. AI-Ready Context maps raw data to its real-world business definitions, building a semantics knowledge graph that enables AI agents to understand, query, and act on information across the entire enterprise.
For enterprises deploying AI agents, the knowledge graph matters for a specific reason: accurate, contextually relevant data maximizes response accuracy while dramatically reducing context window sizes and token costs. Small language models fed focused, relevant data are outperforming large language models given raw, fragmented data — a practical reality that flips the assumption that bigger models always win.
Ashish Gupta, GM of Data Management and former CEO of 1touch, shared a concrete customer example during the press conference. A large credit card company was spending 21 person-hours per data subject access request (DSAR), with more than 7,000 requests per day. After implementing Data Intelligence, response time dropped to 30 seconds. Implementation for a mid-sized customer takes roughly a month — setup in week one, ML-based classification in weeks two and three, and knowledge graph development from week four onward.
Data Intelligence is also explicitly designed to work on data you don't store on Everpure. Gupta was direct about the competitive differentiation: most vendors in this space ask you to copy your data into their system. Everpure's approach works with data where it already lives — with more than 175 connectors spanning structured and unstructured data, SaaS, on-premises, cloud, and mainframe environments.
Unified Data Plane
The Unified Data Plane is Everpure's shared storage foundation spanning cloud, core, and edge across every workload type — from data archives and enterprise file to real-time applications, containerized workloads, and large AI factories.
Three new developments expand the Data Plane this week.
FlashArray//XL 190 with Purity Turbo Mode consolidates mission-critical workloads onto a single array with more performance and SLA headroom. Everpure Cloud Azure Native Virtual Machines — generally available in July — decouples storage from compute for storage-heavy workloads in Azure, enabling lift-and-shift migration without refactoring and management through the Azure portal as a first-party service.
Evergreen//One Overdrive, available in Q3 2026, lets organizations burst 25% above their SLA for both performance and capacity, paying only for what they actually use. It addresses a practical operational problem: AI workloads are unpredictable. Permanent capacity upgrades are expensive. Overdrive lets organizations absorb traffic spikes without permanent subscription increases.
Intelligent Control Plane
The Intelligent Control Plane is Everpure's fleet-wide operating model for governing data and automating actions across the entire environment. It turns manual, reactive storage administration into a self-optimizing system.
New and enhanced capabilities announced this week include Open Telemetry support, Fusion Fleet Topology, and two MCP servers — Pure1 MCP Server for intelligent queries and Fusion MCP Server for taking action across the global infrastructure estate.
The most compelling new addition is an agentic performance triage workflow. When high latency is detected, the system automatically creates a ServiceNow ticket, notifies via Slack, assigns a triage agent, validates the root cause against vSphere, and hands off to a human when remediation requires authorization. It's a clean, real-world example of human-in-the-loop agentic operations — automation handles detection and diagnosis, while humans retain control over action.
Also notable: Compliance Monitoring and Remediation, available in preview, automatically detects hardware and software configuration drift and enforces global corporate governance policies. Fusion Compliance and Agentic Triage (available Q4 2026) extends this by using agentic AI to suggest root causes for immediate technical remediation.
EDC Success Blueprint
To help organizations move from vision to practice, Everpure introduced the EDC Success Blueprint — a step-by-step methodology across 10 operational pillars for building and scaling an Enterprise Data Cloud. It starts with a readiness assessment to identify immediate infrastructure and security risks, then maps a clear path toward highly automated, efficient architecture.
Options Technology, a global capital markets infrastructure provider, offers an early proof point. After standardizing on Everpure FlashArray, FlashBlade, Evergreen//One, Pure1, Fusion, and Portworx Enterprise, Options achieved roughly an 80% reduction in storage management workload through Fusion and Pure1 automations, along with up to an 80% performance boost for key workloads.
Where Most Enterprises Actually Are
IDC's research is honest about the gap between where organizations need to be and where they are. Sixty percent of survey respondents said their storage infrastructure requires significant improvements or a total refresh to support AI workloads. Thirty-four percent report that their data scientists spend more than half their time waiting on IT — waiting for datasets to be provisioned, GPU time to be allocated, requests to be fulfilled — rather than building models.
IDC identifies four maturity tiers in its AI Readiness Index: Experimenting, Practitioners, Competent, and Masters. Most enterprises are currently sitting somewhere between Experimenting and Practitioner — AI projects are beginning to reach production, but storage and data access bottlenecks are still hampering GPU utilization and slowing project timelines.
The path to Competent and eventually Master status runs through exactly what Everpure announced this week: structured data governance, purpose-built AI storage infrastructure, and the elimination of data silos through intelligent platforms.
Everpure is making a bet that the storage layer is where this transformation has to happen — not inside any individual application, not inside a data lake that's already out of date, but at the data layer itself, where context and governance can travel with the data wherever it goes.
For enterprise IT teams trying to figure out why their AI pilots keep failing, that argument is worth paying attention to.
Everpure (NYSE: P) provided press access to Pure Accelerate 2026. The IDC research cited in this article was commissioned by Everpure.
