AI models are fluent but not inherently accurate with complex business data. A "semantic layer" that defines business logic (e.g., "how to calculate revenue") on top of raw data is essential for AI to query structured information correctly and provide reliable, single-truth answers.
Traditionally, business users must queue up requests with data science teams for insights, causing delays. AI changes this by enabling non-technical users to query enterprise data directly using natural language, receiving answers in seconds and empowering faster, data-driven decisions.
Many large companies cite a lack of perfect governance or clean data as reasons to delay AI projects. The effective path forward is to start with a small, high-ROI use case, building a scoped semantic model and governance layer for that specific project before attempting to solve it for the entire enterprise.
Snowflake boosted revenue with AI not through internal productivity gains, but by embedding AI capabilities directly into its core analytics product. This made the platform more valuable and easier for customers to use, which in a consumption-based model, directly drove more usage and revenue.
Many enterprises delay AI adoption by blaming messy data. Snowflake's VP of AI argues that a solid data strategy—breaking silos, centralizing, and governing data—is the non-negotiable prerequisite for any successful AI initiative. AI models must be brought to the data, not the other way around.
Snowflake drove internal AI transformation through a dual approach. The CEO issued a top-down mandate making AI non-negotiable, while the company simultaneously provided bottom-up empowerment by giving all employees access to a coding agent to build their own tools and solutions.