Companies are starting to use generative and agentic AI to surface sharper insights from the data they already have — sometimes even generating full reports with no human touch.

S&P Global Inc. is already tapping Snowflake Inc.’s AI agents to make this vision real — enabling clients to talk directly to their data. The two companies, fresh off a series of announcements at Snowflake Summit, see agentic AI as a way to unlock decision-making at speed and scale

Snowflake’s Baris Gultekin and S&P Global’s Liam Hynes discuss their growing partnership and the role of AI in transforming data insight.

“Our data sits on the Snowflake platform,” said Liam Hynes (pictured, right), head of new product development at S&P Global. “It enables our clients to be able to, first of all, seamlessly get all the data essentially from Snowflake, and then we can tap into things like the Cortex AI, and all of that functionality that Snowflake has. Data is data, but you need to be able to tell a story and pull the narrative out of the data, and that’s what Snowflake [does].”

Hynes and Baris Gultekin (pictured, left), head of AI at Snowflake, spoke with theCUBE’s Rebecca Knight and George Gilbert at Snowflake Summit, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how Snowflake enhances S&P Global’s operations by enabling faster, AI-driven insights across data-rich workflows. (* Disclosure below.)

AI-powered insights at the executive-level

At Snowflake Summit, attendees learned about Snowflake Intelligence, which enables business users to directly interact with data, AISQL, which gives analysts tools to work with multi-modal data, and a series of AI agents. S&P Global has already started using agentic AI to analyze earnings calls.

“We transcribe those earnings calls, and we put them into a machine readable format,” Hynes explained. “In that machine readable format, we have all of the metadata associated with those calls. The earnings call is one of the last instances where you have direct access to a human being speaking on a call, and there’s tons of really interesting things that you can analyze.”

Insights can include whether or not an executive is being proactive about sharing key information to an analyst. At the end of a call, agents will automatically generate earnings reports to save time.

Snowflake is planning to build on these agentic AI and automation offerings by investing in semantic models and better semantic context for its agents, according to Gultekin.

“We want to bring AI right next to the data so that it’s all running in a secure, governed environment,” he said. “Then, on top of this, we want to build highly powerful capabilities to glean insights out of AI. Ultimately, the goal is to democratize access to insights.”

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

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

Photo: SiliconANGLE