Dell AI Data Platform Event to Focus on Data Readiness as AI Moves Into Production

The event will feature Dell Technologies’ Arthur Lewis, David Noy, Vrashank Jain and Gaurav Chawla, along with Nvidia’s Jason Hardy, Elastic’s Sri Desikan, IREN’s Kambiz Aghili, CTBC Bank’s Peter Chu, and Orbital Studios executives, among others. theCUBE Research analysts Dave Vellante and John Furrier will speak with practitioners and industry executives about moving enterprise AI from experimentation into operational systems.

Paul Nashawaty, practice lead and principal analyst for application development, modernization and cloud-native at theCUBE Research, said AI momentum is accelerating, but enterprises are finding that the shift from experimentation to production depends less on model selection and more on data readiness. He cited AppDev data showing that 86% of enterprises prioritize data unification over compute, while 64% of enterprise AI teams identify insufficient storage throughput as a leading training bottleneck.

The discussion reflects a broader change in enterprise AI. Companies have spent heavily on models and accelerated computing, but the success of AI applications increasingly depends on whether those systems can reach the right data and use it effectively. Dell’s AI Data Platform addresses that challenge by bringing storage, data management and security together across environments where enterprise and neocloud AI workloads operate.

Nashawaty said the competitive advantage from an application development perspective is enabling developers to access trusted, governed data consistently across the AI lifecycle. As enterprises move beyond pilots, he said, organizations that connect their data infrastructure to production-grade application delivery will be better positioned to turn AI investment into measurable business outcomes.

The problem becomes more complicated as agents reach beyond carefully maintained databases and warehouses. In a pre-event interview with theCUBE, Dell Technologies’ Vrashank Jain described agents as unpredictable in the data they may need, forcing enterprises to prepare far more information than they did for traditional applications. “We’re shifting from a really predictable way to search things to a really unpredictable way of reasoning over loops,” Jain said. As agents reach further into enterprise repositories, companies must prepare “a lot more data that they can cycle through,” he added.

That includes information stored across SharePoint, OneDrive, SaaS applications, legacy systems and other sources where data may lack structure, tagging or labeling. Jain argued that the longstanding problem of data preparation becomes much larger when agents require useful context from those previously difficult-to-use sources.

Getting data ready is only part of the equation. Enterprises also have to determine which information an AI system needs at a particular moment without overwhelming models with unnecessary context or driving token costs higher. That is pushing context engineering into the enterprise data architecture.

Elastic’s Sri Desikan described an emerging approach in which context is prepared before an agent needs it, rather than requiring models to repeatedly search across multiple back-end systems. “How can you pre-build this context in a way that minimizes token costs and maximizes accuracy?” Desikan said. “Any answer to a question should be accurate, factual and, to the extent possible, be consistent, from person to person.”

The implications extend beyond retrieval. Structured and unstructured information may need to be combined, ranked and verified as agents reason through multiple steps. Desikan pointed to vector search, hybrid search and re-ranking as technologies increasingly suited to agent workflows, where software rather than a human must determine which results have the appropriate context. Furrier framed the emerging context layer as the “connective tissue” between models and enterprise data.