In June 2025, Snowflake revolutionized enterprise data analytics by launching Snowflake Intelligence: an AI-powered conversational agent that transforms how businesses interact with their data. If you’re wondering whether your organization should adopt this technology or how to maximize its value, this comprehensive guide covers everything you need to know.
What is Snowflake Intelligence?
Snowflake Intelligence represents a fundamental shift in data accessibility. Unlike traditional data warehouses that require SQL expertise and technical knowledge, this AI-driven conversational interface enables any business user: from marketing managers to financial analysts, to query complex datasets using natural language.
The platform delivers four game-changing capabilities that distinguish it from conventional analytics tools:
Natural language to SQL translation: Business users can ask questions like “Show me customer acquisition costs by channel for Q3” without writing a single line of code. The AI automatically generates optimized SQL queries that run directly on your Snowflake warehouse.
Unified data interface: Whether you’re analyzing structured data in tables or unstructured content in documents, Snowflake Intelligence provides a single conversational interface for all your data assets.
Enterprise-grade security: Every query respects your existing Snowflake permissions, security policies, and governance rules, ensuring data access remains controlled and compliant.
Business-user focus: Designed to empower non-technical roles such as marketing managers, financial analysts, and product leaders.
Why Snowflake Intelligence is the next big thing in data
The enterprise data landscape is undergoing a major shift. For the past decade, the race was about infrastructure scale and performance: who could process the most data, fastest, at the lowest cost. In 2025, the race is about access and empowerment. The winners won’t be those with the biggest data warehouses, but those who can unlock insights from their data most effectively.
Snowflake Intelligence is emblematic of this new era:
- Democratized access: Anyone in the organization can query data directly, without waiting on analysts or data engineers.
- Metadata-aware responses: Answers are informed by schema, descriptions, and tags, making them more accurate than generic LLM guesses.
- Conversational experience: Instead of static dashboards that answer yesterday’s questions, users interact with their data like they would with ChatGPT. They can explore unexpected patterns, and dig deeper into insights without switching tools or waiting for report updates.
Enterprise benefits include:
- Faster time to insight: from days to seconds
- Reduced analyst bottlenecks: freeing data teams for strategic work
- Broader adoption of warehouse data across the company: turning data from a specialist resource into a company-wide asset
Conversational agents are redefining how organizations access their own data. The implications are profound: decisions become more data-driven, insights emerge faster, and the entire organization becomes more analytically empowered.
How Snowflake Intelligence works
Understanding the mechanics behind Snowflake Intelligence helps organizations implement it effectively. Here’s the step-by-step process that occurs when a user submits a query:
When a business user asks a question, Snowflake Intelligence initiates a decision-making process. First, it analyzes whether the query requires structured data analysis through SQL generation via Cortex Analyst or unstructured data search through Cortex Search. This intelligent routing ensures optimal query execution.
The system leverages semantic views (when they exist) and Snowflake metadata to interpret business terminology. For instance, when someone asks about “churn rate,” the AI searches for relevant definitions within available semantic models and metadata descriptions to understand what this means in your organization’s context.
Queries execute securely within your Snowflake environment, always respecting existing permissions and governance rules. Results are returned in conversational format: sometimes as numbers, summaries, or visualizations, depending on what’s most appropriate for the question asked. The entire process happens in seconds, transforming complex data queries into instant business insights.
Building a secure Snowflake Intelligence context
While Snowflake Intelligence offers impressive capabilities, enterprises face a critical challenge that can undermine its effectiveness: the context gap. This limitation affects not just Snowflake but all major data warehouse AI agents including Databricks Genie and homegrown solutions.
The blind spot problem
Consider this real-world scenario: A product manager asks Snowflake Intelligence, “What’s our customer churn rate for enterprise accounts?” The AI confidently returns an answer based on the data it can see within Snowflake. But here’s what it doesn’t know: your organization has seven different churn metrics across various systems, and the “official” calculation (the one your board reviews monthly) ives in a Tableau workbook with complex cohort logic that exists entirely outside Snowflake’s visibility.
This isn’t a bug or oversight, it’s a fundamental architectural limitation. Snowflake Intelligence is warehouse-bounded. It operates with complete blindness to downstream business logic.
Without visibility into these downstream layers, the AI might confidently return accurate SQL results that are completely wrong from a business perspective.
Why Semantic Views and warehouse metadata aren’t enough
You might think semantic views solve this problem. After all, Snowflake provides these governed models to help agents map business questions to the right data. But here’s where the limitations become clear:
The business problem: Snowflake Intelligence relies on metadata that only exists within Snowflake’s walls. It’s completely blind to the downstream BI layer where most business logic actually lives. Your semantic views might define “revenue,” but they can’t see that your CFO’s trusted revenue calculation includes adjustments made in a Tableau calculated field, or that the board-approved version excludes certain transaction types filtered in a Looker dashboard.
The migration challenge: Even if you wanted to move all business logic into Snowflake semantic views, it’s a massive undertaking. Most enterprises have years of accumulated logic in BI tools: complex calculations, custom cohorts, and specialized metrics, that would take years to reverse-engineer and migrate.
The lag problem: Semantic models perpetually lag behind reality. While your data team updates semantic views quarterly, analysts are building new metrics daily. The most current business logic (the calculations your executives actually use) lives in BI tool expressions, custom SQL, and dashboard-specific transformations that Snowflake Intelligence simply cannot access.
Strengthening context with metadata
This is where metadata intelligence platforms like Euno become essential. Rather than replacing Snowflake Intelligence, these solutions amplify its capabilities by providing the comprehensive context needed for accurate, trustworthy answers.
Complete lineage visibility: Track data flow from source systems through warehouse transformations to final BI dashboards, ensuring AI agents understand the full trust chain.
Usage: Monitor which assets drive actual business value through dashboard impressions, query patterns, and user engagement metrics.
Active Metadata Tags: Dynamically updated signals indicate freshness, certification status, ownership, and compliance with organizational standards, helping AI agents assess data quality in real-time.
Cross-platform integration: Stitch together metadata from Snowflake, BI tools, and other data platforms to create a unified intelligence layer.
When Snowflake Intelligence operates with full metadata context, organizations experience transformative improvements:
Instead of returning potentially incorrect answers, the AI can now respond: “I found three churn rate definitions. The certified metric most frequently used by executive dashboards includes cohort calculations from your Customer Analytics Tableau workbook, which factors in trial conversions.”
This level of intelligence enables accurate decision-making, reduces analyst bottlenecks, and builds user trust in AI-generated insights.
Measuring success: KPIs for AI-powered analytics
Track these key performance indicators to evaluate your Snowflake Intelligence implementation:
- Query accuracy rate: Percentage of AI responses validated as correct by subject matter experts
- User adoption metrics: Number of unique users, queries per day, and repeat usage rates
- Time to insight: Average duration from question to actionable answer
- Analyst productivity: Reduction in ad-hoc query requests to data teams
- Business impact: Decisions influenced and value generated through AI-powered insights
The future of conversational data analytics
As we progress through 2025, the convergence of AI agents and data warehouses will accelerate. Organizations that successfully implement these technologies with proper context and governance will gain significant competitive advantages through democratized data access, faster decision-making, and reduced technical barriers.
The key to success lies not just in adopting AI agents like Snowflake Intelligence, but in ensuring they operate with complete, accurate context. By combining conversational interfaces with comprehensive metadata intelligence, enterprises can finally deliver on the promise of self-service analytics at scale.
Frequently Asked Questions
How does Snowflake Intelligence differ from ChatGPT or other LLMs? Unlike general-purpose AI, Snowflake Intelligence is specifically designed for enterprise data analysis, with direct access to your warehouse, understanding of your business context, and respect for security policies.
What types of questions can Snowflake Intelligence answer? It handles everything from simple queries (“total revenue last month”) to complex analyses (“customer lifetime value by acquisition channel, excluding test accounts”), as long as the data exists in your Snowflake environment.
Do users need technical training to use Snowflake Intelligence? No technical training is required. Business users can ask questions in natural language. However, understanding your organization’s data structure and metric definitions improves result interpretation.
How does Snowflake Intelligence handle data security? All queries respect existing Snowflake role-based access controls, row-level security, and column masking. Users only see data they’re authorized to access.
Can Snowflake Intelligence work with real-time data? Yes, it queries your live Snowflake warehouse, providing answers based on the most current data available in your tables and views.
Conclusion: transform your data strategy today
Snowflake Intelligence represents a paradigm shift in enterprise analytics, but its success depends on implementation quality. Organizations that combine conversational AI with comprehensive metadata intelligence will lead the next wave of data-driven innovation. Start by assessing your current data governance, evaluating metadata platforms, and creating a roadmap for AI-powered analytics adoption.
