Data context and governance are the missing ingredients keeping enterprise AI from scaling

The next phase of enterprise AI is shifting focus from models to the data that fuels them, with organizations increasingly investing in AI-ready data foundations. As regulatory requirements grow and data environments become more complex, companies are prioritizing data intelligence strategies that provide the visibility, context and governance needed to scale AI responsibly.

AI is transforming how work gets done, with intelligent agents increasingly able to make decisions and carry out tasks independently. To unlock that potential, organizations must understand their business processes and apply the right intelligence and context, enabling faster execution while freeing up resources for innovation, according to Ashish Gupta (pictured), chief executive officer, president and chairman and board member of 1touch.io Inc.

“I feel AI is going to be very, very productive for everyone in the market,” Gupta said. “But it’s going to be very productive only if you’ve got the right data context driving that accuracy.”

Gupta spoke with theCUBE’s Christophe Bertrand and co-host Alison Kosik at the Pure Accelerate 2026 event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the growing importance of data context for AI accuracy and the role of governance in building AI-ready data foundations. (* Disclosure below.)

AI-ready data foundations in focus

Many organizations struggle to achieve AI-ready data, which is one reason AI projects often fail to move beyond the pilot stage. Success requires four things working together: a clear vision of what AI should do, the right data to make it accurate, cultural adoption across the organization and continuous monitoring to ensure the system keeps learning, Gupta noted.

“The first thing is that you need to understand what you want to let AI do,” Gupta said. “From that second perspective comes around is, what is the data that is going to make it more accurate in doing what it needs to do?”

When those elements are missing — or when AI costs aren’t actively managed — the effort quickly becomes unmanageable, Gupta noted.

“You need to continue to make sure that it’s accurate and learning as it moves along,” Gupta said. “When you take all of these things together, in addition to the fact that costs can burgeon if you don’t do it correctly, it becomes quite a large task if it’s not done in a coordinated manner.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the Pure Accelerate 2026 event:

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

Photo: SiliconANGLE