AI Data Foundations Move to Center of Enterprise AI Production Push

The shift is changing enterprise architecture. SiliconANGLE reported that AI stacks are rewriting the rules of business and transforming core mechanics, because infrastructure determines how decisions are made, how work is executed and how risk is governed.

“The winners will be the organizations that build a complete system around those models – one that connects to existing deterministic applications, creates a shared truth layer, controls agents as they take action, and uses human feedback to continuously improve,” theCUBE Research analysts Dave Vellante and George Gilbert said, according to SiliconANGLE. They said enterprises that get this right will run cheaper and differently, scaling with less proportional labor growth, compressing cycle times from insight to action and behaving more like platform companies with compounding advantage that is difficult for competitors to copy.

The report is part of SiliconANGLE Media’s exploration of the architectural shifts powering continuous, production-grade AI, and it points to theCUBE’s “Dell AI Data Platform Event: From Ambition to AI at Scale.”

Moving AI from pilots to production requires a stronger yet adaptable data foundation, according to the report. Large enterprises are using different infrastructure and deployment models for a wide range of workloads, shaping how compute providers such as Dell Technologies Inc. deliver infrastructure solutions.

Composability has become more popular. Instead of adopting a single stack, enterprises are seeking architectures that support plug-and-play data engines and frameworks. Varun Chhabra, senior vice president of product marketing and infrastructure solutions group at Dell, told theCUBE that customers want modular solutions and want to think about AI across the whole platform, including compute, storage, networking and GPUs, as well as the software framework and models, with testing and validation across the stack.

Model validation and modular solutions are becoming more essential with agentic AI, the report said. Many enterprises now aim to connect information with desired behaviors for agents and other AI tools, which requires an architecture that can feed agents valuable proprietary data for measurable results. Dell and other companies are building infrastructure for agents in the data layer.

“The agent itself is just a software system, and it has a number of components … it has LLMs, it has knowledge graphs, it has protocols,” Dell global chief technology officer and chief AI officer John Roese told theCUBE. “That data layer is not magic. It’s a real thing. You have to actually build it, you have to build an infrastructure that supports knowledge graphs and maintains them and can feed them, that also can do things like agentic.”

Storage and security are also shifting. AI’s expanding influence in the enterprise stack and demands of scalable data management are changing requirements for storage, security, sovereignty and governance, according to SiliconANGLE. In May, Dell announced enhancements for its PowerStore platform that Chhabra described as “the biggest leap forward in the platform’s history.” The changes included hardware and software enhancements that could deliver up to three times more input/output operations, throughput and density than previous generations.

Chhabra said the platform is a new class of modern data platform built to help customers lead through change rather than only react to it. Security has become a front-burner issue in the AI age. New context-aware, autonomous protection tools have highlighted for many security practitioners the potential to secure the enterprise data platform in more advanced ways.

“Understanding an event may require security context, production behavior, application dependencies, and business impact to come together at the point of decision,” theCUBE Research analyst Krista Case said, according to SiliconANGLE. She said this makes context part of the security architecture rather than an enrichment step added after an alert fires, and it changes how enterprises should evaluate AI security.

Editor’s Summary: SiliconANGLE reports that enterprises are moving beyond model capability and focusing on data access, control and modular AI infrastructure as they push AI into production. Dell is positioning PowerStore, agent-ready data layers and context-aware security as parts of that shift, while analysts say the winners will build complete systems around models and shared data. The outcome will shape how enterprises scale AI, govern risk and organize work.