Snowflake (NYSE: SNOW) has announced the general availability of Snowflake Intelligence, an enterprise intelligence agent designed to enable its global customer base to answer complex questions using natural language and access insights across their data. This development allows users to conduct deep research and suggest solutions to business problems, moving beyond surface-level information to understand underlying causes. In the three months prior to this announcement, over 1,000 Snowflake customers had already deployed more than 15,000 AI agents within their operations. The company also introduced new innovations to facilitate the deployment of agentic AI at scale, aiming to enhance return on investment for enterprises.\n\nChristian Kleinerman, EVP of Product at Snowflake, stated that the company’s evolution involves integrating AI with customer data to unlock unique intelligence. He highlighted Snowflake Intelligence as a tool that democratizes AI, enabling employees to make informed decisions more rapidly and potentially transforming innovation strategies for customers.\n\nTraditionally, obtaining answers to business questions, such as sales trends, has been challenging due to data fragmentation across various systems. Snowflake Intelligence aims to streamline this process by allowing employees to interact securely with all company data through natural language. This approach is intended to reduce reliance on data teams for complex coding, minimize guesswork in decision-making, and foster a data-driven culture across enterprises.\n\nGlobal organizations including Cisco, Fanatics, Toyota Motor Europe, TS Imagine, USA Bobsled/Skeleton Team, and Wolfspeed are utilizing Snowflake Intelligence. The agent unifies diverse enterprise data sources, from structured tables and unstructured documents to third-party application data like Salesforce Data 360, through Zero Copy. It is designed for deeper analysis across multimodal data, functioning as a thought partner to identify nuances and relationships. The platform prioritizes trust, governance, and security, ensuring confidential information remains protected while employees analyze data via natural language.\n\nSnowflake Intelligence is powered by AI models from providers such as Anthropic, translating complex queries into conversational insights to democratize data and AI access. Innovations from Snowflake’s AI Research Team have reportedly made text-to-SQL queries up to three times faster, delivering real-time answers with accuracy. The team also developed the Agent GPA (Goal, Plan, Action) framework, a novel evaluation method that demonstrated up to 95% error detection on standard datasets, achieving near-human levels of accuracy.\n\nTo support the deployment of AI applications and agents at scale, Snowflake introduced new AI innovations. Cortex Agents, now generally available, enables developers to build data agents capable of planning tasks, using tools, and generating responses from both structured and unstructured data. Additionally, Snowflake’s managed Model Context Protocol (MCP) Server, also generally available, provides a secure, standardized connection to external AI agents like Anthropic, Cursor, and Salesforce’s Agentforce, which simplifies integration and application architecture.\n\nFurther enhancing agent capabilities, users can leverage Cortex Knowledge Extensions and Sharing of Semantic Views, now generally available, to enrich AI agents with trusted data from an ecosystem of providers including FactSet, IPinfo, and MSCI.\n\nAnahita Tafivizi, Chief Data and Analytics Officer at Snowflake, reported on the internal use of Snowflake Intelligence, where their data team developed a GTM AI Assistant. This assistant provides sales and marketing teams with instant access to knowledge and data, answering over 12,500 questions per week from more than 6,000 employees, saving significant time on tasks ranging from customer profile lookups to complex analytics.\n\nSrini Namineni, SVP and Chief Automation Officer at Cisco, noted that Snowflake Intelligence supports their focus on AI-driven intelligence, enabling the development of internal AI agents for integrating and analyzing large data volumes, leading to greater automation and faster decision-making.\n\nMaddy Want, VP of Data at Fanatics, highlighted Snowflake as the engine for their FanGraph, a fan identity graph. With Snowflake Intelligence, business users at Fanatics can perform accurate segmentation, accelerate cross-sell opportunities, and enhance advertising by building addressable audiences from their data, transforming data into fan experiences and enabling faster decisions.\n\nThierry Martin, Head of Data and AI at Toyota Motor Europe, stated that Snowflake Intelligence reduced agent deployment timelines from months to weeks. This shift allowed his team to prioritize building business context and robust semantic models over coding, resulting in faster market delivery of secure, compliant data solutions and elimination of data movement risks.\n\nThomas Bodenski, COO and Chief Data & Analytics Officer at TS Imagine, emphasized the importance of speed in financial markets. He explained that by combining their domain expertise with Snowflake Intelligence and Cortex AI, users can act on insights more quickly, saving time and effort. This democratization of AI-driven data analysis empowers users to assess performance in minutes rather than days.\n\nCurt Tomasevicz, Director of Sport Performance for USA Bobsled/Skeleton, noted that Snowflake’s approach to data intelligence aligns with the team’s values. By unifying data and making insights accessible through AI with Snowflake Intelligence, trainers, coaches, and athletes gain crucial information for a measurable advantage.\n\nPriya Almelkar, Chief Information Officer at Wolfspeed, described their journey from consolidating over 200 SQL reporting silos to deploying over a dozen AI agents in production with Snowflake Intelligence. This has resulted in significant improvements, such as troubleshooting equipment issues in two minutes instead of two hours, faster access to information, better insights, and enhanced productivity across all functions.