Snowflake unveils 6 AI enhancements at summit drawing 20K professionals

Snowflake, the cloud-based data platform that helps organizations store and analyze massive amounts of information, unveiled a series of AI-focused enhancements at its annual Summit conference in San Francisco. The event drew more than 20,000 data and AI professionals—making it the largest gathering in the company’s history—and featured keynotes from Snowflake CEO Sridhar Ramaswamy and OpenAI CEO Sam Altman exploring how artificial intelligence is shifting from experimental projects to operational business tools.

While these updates represent incremental rather than revolutionary progress, they signal Snowflake’s strategic push to position itself as a comprehensive platform for AI-powered business applications. The announcements span everything from enhanced data processing capabilities to simplified migration tools, all designed to help organizations integrate AI more seamlessly into their existing workflows.

Here are the six key developments that emerged from the summit:

1. Expanded AI Data Cloud through Crunchy Data acquisition

Snowflake announced its acquisition of Crunchy Data, a specialized provider of PostgreSQL database services. PostgreSQL is an open-source database system widely used for handling both day-to-day business transactions (like processing customer orders) and complex analytical tasks (like generating sales reports).

This acquisition significantly expands Snowflake’s capabilities beyond its traditional strength in data analytics. Previously, organizations often needed separate systems for operational work—such as managing customer accounts or inventory—and analytical work like business intelligence reporting. By integrating PostgreSQL support, Snowflake now offers a unified platform where developers can build, deploy, and scale AI applications without moving data between different systems.

The strategic implications are substantial. This move positions Snowflake as a stronger competitor against full-stack platform providers like Amazon Web Services and Microsoft Azure, which already offer comprehensive database and analytics services under one roof.

2. Deepened partnership with OpenAI for enterprise AI

OpenAI CEO Sam Altman highlighted the rapid evolution of AI agents—software programs that can perform tasks autonomously—from simple assistants to sophisticated collaborators capable of advanced knowledge discovery. Building on this trend, Snowflake announced an expanded partnership with OpenAI that enables enterprises to build and deploy AI-powered applications directly within Snowflake’s secure, governed platform.

This integration allows organizations to embed generative AI capabilities into their analytical workflows and decision-making processes. For example, a retail company could use this combination to automatically generate inventory forecasts by having AI analyze sales patterns, weather data, and economic indicators simultaneously—all without moving sensitive data outside their secure Snowflake environment.

The partnership addresses a critical enterprise concern: maintaining data security and compliance while leveraging powerful AI models. By keeping everything within Snowflake’s platform, organizations can implement AI solutions without exposing confidential information to external systems.

3. Snowflake Intelligence as foundation for agentic AI

Snowflake launched Snowflake Intelligence, a secure, integrated AI chatbot that enables users to interact with their data using natural language queries. Instead of requiring employees to learn complex database query languages, they can simply ask questions like “What were our top-selling products last quarter?” and receive immediate, accurate responses.

Behind this seemingly simple interface lies what Snowflake calls an “agentic platform”—a system capable of orchestrating multiple AI agents, integrating with various data sources, optimizing semantic models, and parsing documents automatically. Agentic AI refers to artificial intelligence systems that can take independent action to achieve specific goals, rather than simply responding to direct commands.

While the current capabilities appear foundational, this platform establishes the groundwork for more sophisticated automation. Future versions could potentially handle complex multi-step analytical tasks, such as automatically identifying sales trends, investigating their causes, and generating actionable recommendations—all without human intervention.

4. Enhanced performance with Adaptive Compute and Gen2 Warehouse

Snowflake unveiled Adaptive Compute, a new service that intelligently routes database queries to the most appropriate computing resources. Think of it as a smart traffic controller that automatically directs different types of data processing tasks to the computing clusters best suited to handle them efficiently.

This intelligent routing ensures balanced resource utilization and optimal performance, particularly important as organizations run increasingly complex, real-time data workloads. Snowflake plans to evolve this service into a fully abstracted, intelligent compute and storage layer that automatically adapts to changing demands.

Additionally, the company introduced its Gen2 Warehouse, offering twice the performance of previous hardware generations. This improvement is particularly significant for organizations running real-time, low-latency applications that require immediate responses to data queries—such as fraud detection systems or dynamic pricing engines.

5. Multimodal data analysis capabilities

Snowflake traditionally excelled at analyzing structured data—information organized in neat rows and columns like spreadsheets. The introduction of Cortex AISQL expands this capability by enabling users to query and analyze diverse data types, including text documents, images, and audio files, directly using SQL (Structured Query Language).

This development is significant because most business data isn’t neatly structured. Consider a retail company that wants to analyze customer feedback: they might have survey responses (text), product photos (images), and customer service call recordings (audio). Previously, analyzing this mix of data types required multiple specialized tools and complex integrations.

With Cortex AISQL, analysts can write standard SQL queries that work across all these data types. For instance, they could identify products with negative sentiment in customer reviews, correlate them with specific product images, and analyze related customer service calls—all within a single query. This capability unlocks new analytical possibilities without requiring data movement between different systems.

6. Streamlined migration tools with SnowConvert AI

Snowflake announced SnowConvert AI, a free, AI-powered migration solution designed to help organizations transition their entire data ecosystem to Snowflake. This includes data warehouses, ETL processes (Extract, Transform, Load—the systems that move and prepare data for analysis), and business intelligence workloads.

Migration projects have historically been complex, time-consuming, and error-prone. Organizations often spend months manually converting code, testing systems, and validating data accuracy. SnowConvert AI automates much of this process, automatically generating test cases using AI to validate converted code and ensure consistency throughout the migration.

The tool addresses a significant business pain point: the manual effort, time, and risk associated with moving critical data systems. By reducing these barriers, Snowflake makes it easier for organizations to modernize their data infrastructure and take advantage of cloud-based analytics capabilities.

Business implications

These enhancements collectively represent Snowflake’s strategy to become a comprehensive platform for AI-powered business applications rather than just a data storage and analytics provider. The developments address several key enterprise needs: simplified data management, enhanced security for AI applications, improved performance for real-time workloads, and reduced complexity in system migrations.

For organizations considering AI implementation, these updates lower technical barriers and reduce the need for multiple specialized vendors. However, as Forrester analysts noted, these improvements are more evolutionary than revolutionary—representing steady progress rather than breakthrough innovations that fundamentally change how businesses approach data and AI.

The true test will be how effectively these enhancements translate into measurable business value for Snowflake’s customers, particularly as competition intensifies in the rapidly evolving AI and data platform market.