Every customer has AI somewhere in their business plan. So does every partner. Yet in conversation after conversation, the same pattern keeps showing up: The strategic AI projects that should be moving fastest are often ending up nowhere.

That tension was the starting point for our latest Everpure Partner Fireside Chat. Seb Darrington, VP of Global Partner Systems Engineering at Everpure, sat down with Mahesh Iyer, Senior Manager of Partner Sales at Everpure, for a genuine look at why AI projects stall and the sizable opportunity waiting for partners who can help fix it.

The problem isn’t the model. It’s the data underneath it.

For roughly two decades, enterprise IT was built around an application-centric world, Darrington explained. Every workload spawned its own copy of the data, and teams bolted on ETL, master data management, and business intelligence to manage the fragmentation that was created. In effect, organizations spent years fighting data gravity, the idea that as data accumulates mass, it becomes harder and more expensive to move.

AI flips that model. Instead of moving data toward applications, data now has to sit at the center, with applications and agents pulling from it. That shift is the foundation of what Everpure calls data primacy, and it’s why so many AI initiatives are gated not by the sophistication of the model, but by the readiness of the data feeding it.

Iyer put it plainly: Customers are increasingly being asked to build agents to complete tasks, which sounds simple until you consider what it actually requires. Before any data reaches an AI agent, an organization has to know where all of that data lives, catalog and classify it, and govern what can and can’t be shared securely and compliantly. Today, that’s still largely a manual, error-prone process. Without the right visibility, many organizations respond by sending agents only a small, “safe” slice of their data, which limits how much value the agent can ever deliver.

“An agent is only as good as the data it stands on,” Iyer said, “and if you feed incomplete data, you’re not really tapping into the full power of what that agent could do.”

‘We already have a data catalog’ isn’t the objection it sounds like

One of the most useful moments in the conversation was when Darrington pressed Iyer on a scenario many partners have faced: A customer says they already have a data catalog, DLP, and hyperscaler tooling in place. How can partners respond?

Iyer’s take: That’s a good start, but it’s not a finish line. A catalog tells you where data is. DLP can flag what’s sensitive. Hyperscaler tooling works well inside its own cloud. But none of that answers the harder questions: what the data actually means, how it relates across the entire estate, whether it’s current or duplicated, and whether an AI agent should even be allowed to use it.

Everpure Data Intelligence isn’t positioned to replace those tools. It’s built to make them more useful by continuously discovering, classifying, and contextualizing data across on-prem, multi-cloud, SaaS, and even mainframe environments. And critically, none of that data needs to sit on Everpure hardware.

“It is a door into the accounts our partners can’t open with us today from an infrastructure perspective,” Darrington said. For partners with trusted relationships in accounts where Everpure isn’t the storage vendor of record, this creates a reason to engage that never required a rip-and-replace conversation to begin with.

Beyond resale: Where the real partner margin lives

The question on everyone’s mind, as Darrington put it: Beyond the resale line, where’s the partner margin?

Iyer described two complementary profit pools. The first is straightforward: margin on Data Intelligence license resale, subject to each partner’s standard commercial terms. The second, and the one Iyer emphasized as more meaningful, is the services motion built around the software: paid readiness assessments, data discovery and classification workshops, governance and policy design, and the integration work required to connect findings into a customer’s existing security and data stack.

As a planning benchmark, Iyer suggested partners target $4 to $5 of services revenue for every $1 of Data Intelligence license revenue. The license is what opens the door; the surrounding services practice is what turns that into recurring revenue. An initial paid assessment can expand into implementation, then into ongoing managed governance or AI-readiness services as a customer’s data sources, regulations, and use cases evolve.

“Everpure Data Intelligence is designed to create this high-value partner work, rather than compete for it,” Iyer said. That’s a meaningful distinction for partners deciding where to invest their own expertise.

What this looks like in a real environment

To make the opportunity concrete, the session included a walkthrough of how Everpure Data Intelligence surfaces risk that traditional tools miss, using an anonymized example from a sports medicine network. A scan across disparate sources flagged dozens of files containing highly sensitive athlete data that had slipped through existing controls, including one document that contained nine different types of exposed data, from Social Security numbers to clinical diagnoses.

The platform didn’t stop at discovery. By mapping relationship context across separate, siloed sources, it connected a hereditary health condition on file for one patient to his dependent, whose records lived in a completely different database, surfacing a screening gap that led to an early, treatable diagnosis. It’s a vivid example of what “shadow data” actually costs an organization when it goes unmanaged, and the kind of risk conversation that gives partners a genuine entry point beyond infrastructure.

From the partner Q&A

A few highlights from the live questions:

“My team [consists of] infrastructure SEs. Is delivering a governance assessment a different skill set? Do I hire, retrain, or lean on you for the first few?” Darrington was direct: Implementation of Data Intelligence is currently delivered by Everpure by default, either Everpure-branded or white-labeled, as a deliberately conservative starting point while the team ensures early deployments are flawless (the product joined Everpure in February). What’s fully open to partners today is the advisory and consulting layer, scoping, governance and policy design, remediation, and integration into a customer’s existing stack, which represents the majority of billable work in these engagements. Partner-branded delivery is coming but isn’t available yet.
“If a customer’s board asks us to guarantee an agent won’t leak over-permissioned data, what can we honestly say?” Iyer didn’t dodge the question: There’s no way to fully guarantee that. But he explained how Everpure Data Intelligence can help organizations get as close as possible to that goal: It delivers near-complete visibility across structured and unstructured data, both at rest and in motion. Classification accuracy is validated by third parties. And layered context, spanning file, document, entity, and semantic relationships, lets organizations send more data to AI projects with more confidence than they could before.

Where to start

Iyer’s advice for anyone walking away from this session: Start by getting familiar with what’s already available on the Partner Portal. More content and deeper-dive workshops are coming in the following weeks and months. From there, look at your existing install base and identify low-hanging fruit: accounts where the conversation can expand from infrastructure into data readiness and governance.

As Darrington closed the session: Don’t wait for someone else to lead this conversation. Your customers are already asking these questions. With Everpure Data Intelligence, partners aren’t just making customers AI-ready; they’re proving they saw it coming.

If you missed the live session, Everpure partners can view the recording within Partner Central (login required).

— Tom Ayres, Global Partner Content and Communications Manager, Everpure