Snowflake, the AI Data Cloud company, announced innovations to help enterprises deliver real business impact with AI, which requires more than high-quality models alone. Snowflake is unveiling Semantic View Autopilot (now generally available), an AI-powered service that automates the creation and governance of semantic views, giving AI agents a shared understanding of business metrics to deliver consistent, trustworthy outcomes. Snowflake is also introducing new capabilities across agent evaluations and observability, end-to-end machine learning (ML), and AI cost governance. These innovations build on Snowflake’s existing enterprise-grade foundations, ensuring that AI systems such as Snowflake Intelligence are trusted, governed, and ready to operate reliably at scale, all while working directly on organisations’ most valuable data.
“AI is quickly becoming part of the operating fabric of the enterprise, not a side project,” said Christian Kleinerman, EVP of Product, Snowflake. “Our focus is to make that future a reality now by ensuring AI agents operate on consistent business logic, behave as expected, and scale without surprises. By unifying trust, governance, and execution on one platform, we’re delivering AI that actually works in the environments our customers care about.”
Enterprises are deploying AI agents into environments where business metrics are manually defined and inconsistently governed, leaving them without a shared understanding of business context. This fragmented approach to building the semantic layer is a bottleneck for AI adoption, producing unreliable outputs and weakening trust in AI.
Semantic View Autopilot addresses this challenge by automatically building, optimising, and maintaining governed semantic views, potentially eliminating the need for manual, error-prone semantic modelling. This builds on Snowflake’s commitment to initiatives like the Open Semantic Interchange (OSI), which establishes an interoperable semantic layer across ecosystem leaders. While OSI provides the connectivity to share business logic across the ecosystem, Semantic View Autopilot adds the intelligence to create and continuously maintain it, making it the connective layer for trustworthy, scalable AI across all data, wherever it lives.
By learning from real user activity and using AI-powered generation, Semantic View Autopilot will help ensure business logic remains accurate and up-to-date across Snowflake data and consumption tools, including dbt Labs, Google Cloud’s Looker, Sigma, and ThoughtSpot (generally available soon). Customers can create semantic views using business definitions not only from Snowflake, but also from the business intelligence tools they already rely on. As a result, enterprises can minimise AI hallucinations while cutting semantic model creation from days to minutes, accelerating time-to-market and delivering a decisive competitive advantage.
Leading organizations including eSentire, HiBob, Simon AI, and VTS are already using Semantic View Autopilot to dramatically reduce data-to-insight timelines and free data teams to focus on higher-value AI innovation.
"At Simon AI, our focus is on helping businesses turn data into real, actionable outcomes. But inconsistencies between business logic have historically slowed how far AI can be applied," said Matt Walker, CTO at Simon AI. "Semantic View Autopilot provides our AI systems with a consistent, governed understanding of business metrics that we can collaborate upon with our customers. This allows us to deliver reliable personalisation and AI-driven engagement that our customers can trust to drive measurable results."
