Enterprises are learning that the race to AI-native operations is not won by deploying the most tools or agents — it is won by the organizations that build the most trusted AI data foundations underneath them.

As Snowflake’s agentic enterprise vision takes shape at its Summit 2026 conference in San Francisco, the practical lesson from customers doing the work is becoming clear: Ungoverned AI scales chaos faster than it scales value. Anahita Tafvizi (pictured, right), chief data and AI officer of Snowflake Inc., has been at the center of that lesson — running Snowflake’s own AI-native transformation as customer zero for every product the company ships.

“You want to go from data to insights to action,” Tafvizi said. “We spend a lot of our time making sure it’s well governed, well articulated, it’s accurate, it’s fresh. But now […] you can even have entire workflow automations […] the action is taken on your behalf as well.”

Tafvizi and Durgesh Das (left), vice president of data, analytics and governance at Intercontinental Exchange Inc. and the New York Stock Exchange, spoke with theCUBE’s Dave Vellante and Rebecca Knight at Snowflake Summit 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how strong AI data foundations accelerate enterprise AI deployments and how governance functions as an engine of innovation rather than a constraint. (* Disclosure below.)

AI data foundations and why governance accelerates enterprise AI

The conversation challenged a persistent assumption in enterprise AI: that governance slows innovation. The governance-as-accelerator argument is backed by real deployment experience. Because ICE had already invested deeply in Snowflake’s governance infrastructure — including access controls, data quality and trust policies — it was able to move faster when AI arrived, not slower, Das noted.

“Our role is actually to erase the friction,” Das said. “We were already doing right data for right people with governance, but now, the opportunity for us with AI is to provide that information on time without any friction so that the business can bring their own tools and get the answers quickly.”

That foundation is now producing tangible results. Tafvizi described Snowflake’s own Cortex-powered go-to-market AI assistant — Cortex is Snowflake’s native AI analytics engine — as having answered more than a million questions across the company’s sales organization, handling roughly 40,000 queries a week. The key, she explained, is a governance infrastructure robust enough to eliminate manual intervention entirely — not just speeding up workflows but removing the human bottleneck from routine operations.

“If a data pipeline breaks, [an] analyst on my team in the middle of the night, on-call, has to come in, has to understand and diagnose what happened, propose a solution, submit a PR, then another person [has] to approve that for it to go in effect,” Tafvizi said. “Now that entire workflow is automated. The data pipeline breaks at two in the morning. It triggers an AI agent to wake up, to start doing the analysis of what happened, to propose a solution, to submit a PR. … A human just, when they wake up in the morning, you have it in your inbox just to review and approve, and then it goes into production.”

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