In this NYSE Live interview, Anahita Tafvizi, Chief Data and AI Officer at Snowflake, discusses how enterprises are moving beyond AI experimentation and into large-scale production deployments powered by trusted data foundations.

Tafvizi explains that organizations increasingly view AI success as a data challenge as much as a model challenge. As enterprises expand their use of AI, they are seeking platforms that can unify data, governance, security, and deployment workflows within a single environment. She discusses how this trend is contributing to greater adoption of Snowflake's data and AI offerings.

A significant portion of the conversation focuses on operational AI and workflow automation. Tafvizi shares examples of how Snowflake has implemented AI internally to automate business processes, streamline complex workflows, and improve efficiency across departments. She describes use cases ranging from financial reporting and earnings preparation to sales operations and internal knowledge management.

The interview also examines the growing role of AI agents within organizations. Tafvizi explains how AI-powered systems can answer questions, automate repetitive tasks, and support employees with real-time access to information. These capabilities are helping organizations transition from isolated AI experiments toward practical applications integrated into daily workflows.

Another major theme is the importance of data readiness. Tafvizi emphasizes that organizations must establish strong governance, security controls, trusted metrics, and consistent data definitions before AI systems can operate effectively at scale. Without those foundations, she notes, AI can amplify existing data quality and governance challenges.

The discussion further explores Snowflake's "customer zero" philosophy, in which products are deployed internally before being released to customers. Tafvizi explains how using AI internally helps the company refine products, validate use cases, and identify opportunities for workflow automation that ultimately benefit customers.

The interview also highlights customer examples demonstrating how organizations are using AI-powered data platforms to enable trusted access to information, improve productivity, personalize user experiences, and support decision-making across large workforces.

Throughout the conversation, Tafvizi provides perspectives on enterprise AI adoption, data governance, workflow automation, AI agents, productivity gains, trusted data infrastructure, and the next phase of scaling AI across organizations.

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