With a record-breaking number of sessions, customer stories, and new product announcements, Snowflake Summit 2025 made one thing clear: Snowflake is doubling down on its mission to unify AI and data in a single, secure, and accessible platform. At Select Star, we were excited to be there and share how we've helped customers get AI-ready with new features like automatically generating semantic models from logic in your BI tools. If you missed the live event or are looking for a recap, this post covers the major platform updates, customer use cases, and the direction Snowflake is heading in 2025.
Table of Contents
- Platform Announcements: Building the AI Data Cloud
- Case Studies in Applied AI
- Why This Matters: A Strategic Shift for Data Teams
Platform Announcements: Building the AI Data Cloud
Snowflake introduced a series of product enhancements designed to simplify how organizations build, manage, and operationalize data and AI workloads.
Adaptive Compute and Gen 2 Warehouses
Snowflake is introducing Adaptive Compute, a policy-driven, zero-ops compute model that automatically adjusts resources based on workload patterns. This is complemented by Generation 2 Warehouses, delivering 2x faster performance with improved economics for both structured and open formats like Apache Iceberg.
Snowflake debuted Cortex AI SQL, allowing users to run AI-powered queries on text, images, and audio using plain SQL. This makes multi-modal analytics accessible to every SQL user. At the same time, Cortex Agents enable developers to build AI-driven apps and workflows with orchestration logic, memory, and tool integration, all natively on Snowflake.
Snowflake Intelligence
Another major highlight was the launch of Snowflake Intelligence, an AI assistant that lets business users query structured and unstructured data through natural language. It incorporates verified queries, semantic views, and integrates with tools like Gmail and Teams to take action directly within workflows.
Horizon Catalog Enhancements
Snowflake continues to expand its Horizon governance framework with features like sensitive data propagation, role-based model access, and lineage import from tools like dbt and Airflow. There's also support for assets outside Snowflake, including Tableau and Power BI dashboards. To learn more about how Horizon compares with other catalog approaches, read our breakdown in our guide on Snowflake Catalogs.
Case Studies in Applied AI
The real-world impact of Snowflake's platform innovations came to life through customer keynotes and technical sessions at Summit. From healthcare to hospitality, companies showcased how Snowflake’s AI-native capabilities are solving high-value challenges across industries. Canva demonstrated how Cortex AI SQL enables scalable marketing insight generation across diverse data formats. Marriott highlighted the value of Adaptive Compute for efficient, automated warehouse management across a global footprint. AstraZeneca underscored the power of integrating unstructured clinical data through OpenFlow and accelerating AI deployment in healthcare R&D.
nib: Leveraging Snowflake Cortex to Reduce Manual Work in Insurance Claims
nib, a leading health insurer in Australia supporting more than 1.9 million people across Australia and New Zealand, showcased its adoption of Snowflake Cortex and Select Star. By operationalizing Cortex for classifying customer support emails, nib accelerated handling customer requests 6 months faster, all while ensuring data compliance within the healthcare domain. Their talk illustrated the promise of embedded AI without the friction of moving sensitive data between systems.
This is also where Select Star adds a critical layer of value. For nib, Select Star automatically generates and maintains semantic models on top of Snowflake tables, tagging business metrics, definitions, and relationships in a way that aligns with tools like Cortex AI SQL and Snowflake Intelligence. This ensures consistent interpretation of key entities across teams and use cases.
By capturing metadata and context from query logs, dashboards, and documentation, Select Star enables data teams to expose their semantic layer to AI agents in a way that is both scalable and governed. It's a practical foundation for AI-readiness, allowing enterprises to go from experimentation to production faster with higher confidence in their data.
Why This Matters: A Strategic Shift for Data Teams
The announcements at Snowflake Summit 2025 indicate a future where AI is a first-class citizen in the data platform, not a bolt-on. For data teams, this means:
- Less time spent on infrastructure and more time delivering value.
- Tighter integration between governance and experimentation.
- A growing expectation that business users can self-serve with AI.
Snowflake is betting big on agentic workflows, metadata-rich context, and seamless data collaboration across clouds and platforms. Snowflake Summit 2025 wasn’t just a roadmap reveal. It was a blueprint for how AI and data will converge in practice. Whether you're a data engineer, analyst, or leader, the tools to deploy, govern, and scale AI are now available natively inside Snowflake.
If you’re building a semantic layer, deploying AI agents, or enabling self-service, now is the time to ensure your data is AI-ready. Connect with an expert at Select Star to learn how we can help you establish a single source of truth for your data and AI.
