Tatsuya

Posted on May 18

Just Before Snowflake Summit 26: From an AI Perspective - The Current State of "Snowflake - The AI Data Cloud"

#ai #cloud #data #news

Note:
This article represents my personal views and not those of Snowflake.

Information regarding Snowflake Summit 26

Snowflake Summit 26 will be held in San Francisco from June 1 to June 4, 2026. Various updates are expected to be announced, so please stay tuned.

Snowflake Summit 26

Explore the future of data, AI, and applications at Snowflake Summit 26. Join experts, attend workshops, and get SnowPro certified while learning new data trends.

snowflake.com

I am planning to attend in person, so I look forward to meeting anyone who will be there!

Introduction

As mentioned at the beginning, Snowflake Summit 26 is finally approaching next month. In this article, I would like to provide an overview of the current standing of Snowflake - The AI Data Cloud from an AI perspective and broadly organize the situation as of May 17, 2026. In my professional role, I have had many opportunities to discuss these topics recently, so I decided to verbalize them in an article. Please note that this article is intended to summarize the overall picture and may not cover every single detail. Let us dive in!

There is No AI Strategy Without a Data Strategy

"There is no AI strategy without a data strategy" - Interestingly, the reason I chose to join Snowflake about a year ago was very close to this. Even then, there were many requests to utilize AI in the enterprise, but I felt that a well-organized and easy-to-use data foundation was necessary for that. Recently, keywords like ChatGPT and Claude have become common, but these services—and the models behind them—do not inherently possess the "context of the company trying to use them" in their raw state. They become more useful within a company by combining them with internal data. However, to do so, it is necessary to prepare the data foundation and utilize it as appropriate context.

Given this situation, I believe "There is no AI strategy without a data strategy" is a keyword that truly captures the essential point.

Two Pillars of AI Utilization in Snowflake

Regarding how AI can be utilized in Snowflake under these circumstances, I believe there are two main pillars. These are "Making data AI-Ready/analyzable" and "Using AI-Ready data."
In the diagram above, these correspond to the left and right sides of "AI Ready GOLD." Let me organize these two pillars below.

AI Utilization for Making Data AI-Ready/Analyzable

Snowflake is expanding its features to enable the use of AI in various ways to make data AI-Ready or analyzable.

Data Engineering Perspective

It is said that 80% of the world's data is unstructured data, such as images, videos, and PDFs. Snowflake is also expanding various functions to handle this unstructured data. By using features like AI Functions and the Document Processing Playground, it is possible to convert unstructured data into analyzable data.

AI Functions include the following types of functions:

By using these, for example, it is possible to perform the following processing based on audio recording data:

This allows various data points to be generated from a single recording and treated as analytical dimensions.

Handling Document Data

Furthermore, it is now possible to handle various document data, including PDFs. By using the AI_PARSE_DOCUMENT function, the following conversion process can be performed:

AI_PARSE_DOCUMENT supports various file formats such as PDF, PPTX, DOCX, JPEG, JPG, PNG, TIFF, TIF, HTML, and TXT.

An update on May 4, 2026, enhanced the OCR mode of AI_PARSE_DOCUMENT, improving the accuracy of OCR mode and multilingual support. Please give it a try.

May 04, 2026: AI_PARSE_DOCUMENT OCR quality improvements

Additionally, the Document Processing Playground feature has been released. This feature helps you by suggesting what SQL or Python code to write when uploading document data to extract necessary information.

Updates for these functions handling document data are frequent, and features such as figure/table extraction have recently been released. These functions are also introduced in the following blog posts (Note: Links are to Japanese articles).

By vectorizing the data extracted using these functions, semantic search based on meaning becomes possible. This can be used with the Cortex Search feature, which will be described later.

Handling PII (Personally Identifiable Information)

The AI_REDACT AI Function for processing personal information has also been released.
This function identifies and masks PII contained within text. The image below illustrates the concept:

Handling Video and Audio Data

Additionally, as of May 6, 2026, although in Public Preview (PuPr) status, it has become possible to directly handle video and audio data using specific models in the AI_COMPLETE function. While there are limitations on models and file sizes, the patterns for approaching unstructured data have increased further.

May 04, 2026: Multimodal video and audio analysis for marketing and brand insights (Public Preview)

A detailed analysis of the results can be found in the blog post "Beyond Transcription - Multimodal Video and Audio Analysis with Snowflake AI_COMPLETE" so please take a look.

Regarding audio data, it was already possible to convert it to text using the AI_TRANSCRIBE function, but the variations have now expanded.

A great advantage of these AI Functions is that they can be executed as part of SQL close to the data, allowing for efficient processing of large volumes of data.
In this way, Snowflake provides various services for handling unstructured data, enabling conversion into AI-Ready and other easily analyzable formats. I believe this reflects a design philosophy of "AI utilization close to the data."

AI Utilization for People and Systems to Use AI-Ready Data

For data organized in the manner described above, Snowflake provides various ways to utilize that data. While traditional data handling using SQL and BI tools remains possible, AI has come to play a significant role in the data utilization field. Snowflake provides the following major services as related functions:

Services for searching data: Cortex Search, Cortex Analyst
Services for developing arbitrary AI Agents to search data or gain insights: Cortex Agents
Applications for working with data: Snowflake Intelligence, Cortex Code in Snowsight / CLI
Integration with other AI tools: Snowflake-managed MCP Server

Services for Searching Data

Cortex Search

Cortex Search is a hybrid search service supporting both keyword and vector search. Cortex Search is receiving useful updates, such as Cortex Search Multi-index and Index-specific Boosts to enhance search flexibility and accuracy, as well as Cortex Search Batch, which covers use cases for processing large volumes of queries in batches, such as entity resolution, catalog mapping, and deduplication.

Cortex Analyst

Cortex Analyst is a service for achieving Text-to-SQL (converting natural language into SQL). It utilizes defined Semantic Views and leverages AI to perform searches against table data.
In analytical tasks and AI-Ready data utilization, the Semantic Layer is a crucial element. In Snowflake, this feature is named "Semantic View," allowing for the organization of metadata such as logical tables, analytical dimensions, and items to be aggregated. Additionally, Snowflake has a feature called Semantic View Autopilot that automatically generates these "Semantic Views." Currently, generation is in English, so care is needed, but it is a feature that assists in the often labor-intensive task of preparing Semantic Views.

A part of the Semantic View Autopilot feature includes automatically generating Semantic Views by importing definition information from other tools. For example, it can read a Tableau Workbook to automatically generate a Semantic View.

Furthermore, Snowflake is advancing an initiative called the Open Semantic Interchange (OSI) Initiative, working with various partners to standardize the Semantic Layer. The repository for this activity is below:

open-semantic-interchange/OSI

Services for Developing Arbitrary AI Agents to Search Data or Gain Insights

Cortex Agents

Cortex Agents is a service for building AI Agents on Snowflake. It functions as an AI Agent that operates by linking data search services like Cortex Search and Cortex Analyst with custom tools such as stored procedures. In a recent update, Cortex Agents added support for Agent skills, improving execution efficiency for repetitive tasks, and support for MCP Connectors, allowing integration with external tools and services.

Cortex Agents can be used not only within Snowflake Intelligence but also via REST API or through a Snowflake-managed MCP Server, allowing calls from any application or AI Agent.
Examples of integration include:

Integration with Teams/Microsoft 365 Copilot: Cortex Agents for Microsoft Teams and Microsoft 365 Copilot
Integration example with Amazon Q (Simple hands-on): Multi-Agent Supply Chain Orchestrator with Snowflake Cortex MCP and Amazon Q Business
Integration example with Microsoft AI Foundry (Simple hands-on): Multi-Agent Agentic Orchestrator with Snowflake Cortex MCP and Microsoft AI Foundry

Recently, on April 13, 2026, an update was made allowing Cortex Agents to directly reference Semantic Views and generate SQL without going through Cortex Analyst. This update is expected to improve the accuracy of analytical queries and reduce latency.

Apr 13, 2026: Improved SQL generation in Cortex Agents

Applications for Working with Data

Snowflake Intelligence

Snowflake Intelligence is a service that allows users to perform deep dives into data by querying the organized data foundation on Snowflake using natural language. Cortex Agents operate behind this application. With the support for Agent Skills and MCP Connectors mentioned above, these can also be utilized within Snowflake Intelligence. Furthermore, as mentioned in recent press releases, there are many feature updates for business use, such as the DeepResearch feature for in-depth investigations (scheduled for PuPr soon) and the Artifact feature for managing and sharing generated charts and tables (scheduled for GA soon).

Snowflake Intelligence is also used internally at Snowflake, with over 6,000 members across various roles using the tool. A behind-the-scenes look at this build has been published on Medium as "From Data Maze to Intelligence Layer: GTM AI Assistant with Semantic Views on Snowflake Intelligence".

Cortex Code in Snowsight / CLI

Cortex Code in Snowsight / CLI is an AI Agent tool that supports various development tasks on Snowflake. In addition to such tasks, it can be used for data discovery via Horizon Catalog (Snowflake's native catalog feature), allowing for data exploration using Cortex Code against the data organized on Snowflake.

Similar to Snowflake Intelligence, Cortex Code is utilized by internal members at Snowflake. The appearance of these AI tools seems to have increased the utilization of the internal data foundation. In fact, I have personally started using Cortex Code frequently to utilize internal data.

Recent updates for Cortex Code include:

Support for more external data systems like AWS Glue, Databricks, and Postgres
Support for MCP and Agent Communication Protocol (ACP)
New VS Code extension (in Private Preview) and Claude Code plugin
New Agent Software Development Kit (SDK) supporting Python and TypeScript, allowing Cortex Code features to be integrated directly into your own apps or workflows
Updates to Cortex Code on Snowsight:
- Cloud Agents (in Private Preview) allow users to execute code and run workflows directly in the browser
- New Plan Mode allows for previewing and approving workflows before execution
- Snap & Ask feature allows direct interaction with data artifacts like charts and tables

For Claude Code users, please try the snowflake-cortex-code plugin mentioned above.

Snowflake Cortex Code - Claude Marketplace

Integration with Other AI Tools

Snowflake allows several functions to be published as a Managed MCP Server on Snowflake.

This MCP Server can be used as a remote MCP Server. It can be utilized from AI Agent tools acting as MCP Clients, including ChatGPT, Claude Desktop, and Claude Code.

Security and Governance Supporting AI Utilization

The above summary focused primarily on AI for data utilization on Snowflake. Snowflake also covers security and governance elements in this AI utilization. As the use of AI advances further, these elements will become extremely important.

I personally consider Snowflake RBAC (Role-Based Access Control) to be very powerful. As shown in the diagram above, when using various services and features related to AI, the same RBAC managing the data is applied, and access control is performed based on the roles linked to the user. Since all answers reflect only the data the user has permission to view, it is possible to utilize AI and data under appropriate governance.

Furthermore, Snowflake has observability features centered on AI Observability. For example, various logs related to Cortex Agents are managed here. Additionally, a quality evaluation feature called Cortex Agents Evaluation has been released, allowing for the quality assessment of AI Agents using the GPA framework. The GPA framework evaluates AI Agent quality based on Goal (is the user's objective eventually achieved?), Plan (do planning and re-planning provide effective high-level instructions?), and Act (do the agent's actions follow the plan, call tools appropriately, and progress toward the goal?).

From a budget management perspective, budget management features for Cortex Agents / Snowflake Intelligence have been released (GA on April 10, 2026). This allows for:

Flexible tag-based scope settings - management by department or project
Multi-stage threshold settings - defense-in-depth combining ACTUAL and PROJECTED
Automated access control - automatic response when thresholds are reached
Cycle-based management - automatic reset/recovery at the start of the month

Other features like row-level policies also serve as a foundation, with various AI-related elements being built upon them.

Updated on May 17, 2026

On May 14, 2026, Cortex AI Guardrails expanded its support beyond Cortex Code to include Snowflake Intelligence and Cortex Agents. Cortex AI Guardrails is part of the Snowflake Horizon Catalog and is designed to use context-based reasoning to detect and neutralize threats such as indirect prompt injections embedded in tool calls. Although the configuration is applied at the account level, it can be easily enabled by changing the AI_SETTINGS parameter.

May 14, 2026: Cortex AI Guardrails support for Snowflake Intelligence and Cortex Agents (General availability)

This enhancement enables you to use these capabilities even more securely, so we encourage you to consider adopting it as well.

Conclusion

Recently, the paper that marked the beginning of Snowflake, "The Snowflake Elastic Data Warehouse," received the SIGMOD Test-of-Time Award.

I personally feel that the evolution of Snowflake as a service over the years is proving its worth in the current era of AI.
In transforming enterprise operations, data and AI are inseparable. Snowflake provides Cortex AI based on the concepts of being easy, efficient, and trusted. I hope you will fully utilize Snowflake's capabilities as a platform where you can easily and efficiently leverage AI while maintaining governance and security close to your data.
Snowflake has many feature updates, and it is difficult to cover them all, but I hope this article helps you grasp the overall current state of AI utilization in Snowflake.

And as mentioned at the beginning, Snowflake Summit 26 will finally be held from June 1 to June 4, 2026. I am very much looking forward to what will be announced!