Enterprise AI is entering a new phase where competitive advantage depends less on foundation models and more on the ability to connect data and business knowledge through an enterprise context layer. As organizations scale AI initiatives, intelligent agents are emerging as the new interface between information and action.

Many organizations had written off big data after years of unmet expectations, but generative AI is finally unlocking the value that massive datasets promised all along. The challenge now is not recognizing the importance of enterprise data, but overcoming the complexity of connecting to and operationalizing it for AI-driven applications, according to Bob O’Donnell (pictured, right), founder and chief analyst of TECHnalysis Research LLC.

“It makes so much sense because people have recognized [that] to make AI productive and effective in their organization, they obviously have to tap into their own data,” O’Donnell said. “That sounds great in theory, but it turns out the nuts and bolts of actually doing that are pretty darn hard.”

O’Donnell and Sanjeev Mohan (left), founder and owner of SanjMo, spoke with theCUBE’s Dave Vellante at Snowflake Summit 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how generative AI is finally delivering on the long-promised value of big data and why the enterprise context layer is emerging as the foundation for the next generation of AI agents. (* Disclosure below.)

Enterprise context layer in focus

Many organizations recognize that effective AI initiatives require a strong data foundation, but the complexity of consolidating and organizing enterprise data has made unified platforms and an enterprise context layer increasingly important advantages. Today, organizations are moving away from centralized data warehouse strategies and toward federated approaches that allow data to remain in place while being connected and analyzed across systems, according to Mohan.

“The entire attempt to create a corporate data warehouse is fraught with problems,” Mohan said. “It’s not for everybody.”

Data federation has gained momentum as organizations increasingly access and analyze data where it resides rather than moving it into centralized repositories. Faster networks, reduced data-transfer costs and the adoption of open standards such as Apache Iceberg have made this approach far more practical at scale, according to Mohan.

“You can leave data where it is … but now the moat has moved to a layer above it, which is the context layer or the metadata layer because you can apply security there,” Mohan said. “You can disambiguate what is the meaning of customer in SAP. This whole SAP-Snowflake bidirectional metadata sync is a game changer in my opinion.”

Traditional semantic layers were designed for structured data, but modern AI environments must also incorporate semi-structured and unstructured information. As a result, organizations are increasingly building context layers or knowledge graphs that agents can then query across tools like Slack and Jira, Mohan explained.

For O’Donnell, that shift represents a fundamental architectural leap — from systems that retrieve and respond to ones that remember and reason.

“There’s no memory, there’s no state being maintained,” O’Donnell said. “I think we are finally getting to a point where I can fine-tune or train a small model. That has not yet happened — but that’s coming.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of Snowflake Summit 2026:

(* Disclosure: TheCUBE is a paid media partner for Snowflake Summit event. Neither Snowflake, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE