Snowflake's Baris Gultekin on the Shift Toward AI Agents in Business

Snowflake Inc., a Montana, United States-based cloud data company, is reshaping enterprise AI adoption by placing AI agents directly alongside data sources. In a recent interview with YourStory, Baris Gultekin, Head of AI at Snowflake, detailed the company's direction in AI, its evolving enterprise solutions, and the significance of markets like India in the global expansion of AI technologies.

Founded in San Mateo, California, in 2012 by Benoit Dageville, Thierry Cruanes, and Marcin Zukowski, Snowflake has grown to serve over 10,000 customers worldwide, including more than 800 members of the Forbes Global 2000 list. Operating on Amazon Web Services, Microsoft Azure, and Google Cloud Platform, the company enables large-scale data storage, sharing, and analysis across a secure and governed environment.

AI integration where data lives

Gultekin emphasized that the company's AI offerings are designed to integrate directly with enterprise data, removing the need for complex data movement processes. "Our customers primarily use Snowflake as a data platform to secure, govern, and analyse their most valuable data assets," he noted. As organizations adopt AI, there is a growing preference to run models alongside existing datasets. This approach improves efficiency and reduces reliance on external data pipelines.

Tools like Cortex Analyst, Snowflake’s natural language-to-SQL interface, allow users to bypass rigid business dashboards by querying data directly in natural language. Gultekin highlighted how many businesses struggle with long wait times to access deeper insights due to dependencies on data analysts. By integrating AI within Snowflake’s infrastructure, these processes can be simplified significantly.

Trust, accuracy, and cost are key concerns

Discussing enterprise AI adoption, Gultekin identified three primary concerns: data security, accuracy, and cost. He explained that customers are cautious about moving data outside their approved systems. "Snowflake provides an easier on-ramp to AI without requiring data movement," he said. To mitigate risks like model hallucinations, Snowflake allows businesses to ground models in their own trusted data.

On cost, he acknowledged that while proof-of-concept stages often prioritize quality, enterprises eventually turn their focus to ROI. Optimizing prompts and fine-tuning models within the platform has enabled customers to reduce costs while maintaining performance.

Cortex Agents and enterprise-scale use cases

The introduction of Cortex Agents marks Snowflake’s deeper involvement in orchestrated AI workflows. These agents are designed to work with both structured data from databases and unstructured data from documents, PDFs, or platforms like SharePoint. Gultekin described use cases such as combining sales data with customer feedback to understand product performance—scenarios where both data types are needed for full context.

Enterprises such as Bayer, Siemens Energy, and S&P Global are already using Snowflake’s AI solutions. Bayer applies AI for flexible business intelligence, while Siemens Energy uses internal research documents to enhance chatbot capabilities. S&P Global is processing hundreds of thousands of financial transcripts to support analytics in the finance sector.

India as a major growth market

Highlighting Snowflake’s global outlook, Gultekin pointed to India as a fast-moving market for AI adoption. “India has long been at the forefront of technology adoption,” he said, adding that its young, dynamic population creates strong potential for early AI integration. Snowflake is expanding its product availability, including Cortex Search, which now supports over 100 million documents and comes with a 30% cost reduction to improve accessibility.

In Gultekin’s view, agents are becoming the atomic unit of AI. "Each technological paradigm had an atomic unit—web pages for the internet, applications for mobile... for AI, it is agents," he said. With tools that can reason, call other services, and operate within enterprise-grade data governance frameworks, Snowflake positions itself to lead this transition.