Understanding Snowflake Intelligence: What It Is, How It Works, and Why It Matters

Published: at 10:00 AM

In this article, we will discuss Snowflake Intelligence, a revolutionary platform feature that reached general availability in November 2025. We’ll explore how it enables business users to interact with enterprise data using natural language, the underlying technology that makes it work, and why this represents a fundamental shift in how organisations democratise data access.

Features change from time to time with new features being added regularly, it is recommended that you review the documentation for the latest on what specific features are included with any of the Editions.

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What is Snowflake Intelligence?

Snowflake Intelligence is an integrated AI chatbot and analytics platform that enables users to interact with their data using natural language, without writing SQL or code. Announced at Snowflake Summit 2025 and reaching general availability in November 2025, it represents Snowflake’s vision for truly democratised, self-service analytics.

At its core, Snowflake Intelligence is built on three foundational components:

  1. Cortex Analyst: The AI-powered query engine that translates natural language to SQL
  2. Semantic Views: The business metadata layer that provides context and meaning
  3. Agentic AI Framework: The orchestration layer that coordinates data access, security, and governance

Think of Snowflake Intelligence as a knowledgeable colleague who understands both your business terminology and your data structure. You can ask questions like “What were our top 5 products by revenue last quarter?” and receive immediate, accurate answers with visualisations—all without needing to know which tables contain the data or how to join them.

The Problem It Solves

Traditional business intelligence requires users to either:

Snowflake Intelligence eliminates these barriers by enabling conversational analytics. Business users can explore data as naturally as asking a question, whilst data teams maintain governance, security, and data quality standards.

How Snowflake Intelligence Works

Understanding how Snowflake Intelligence operates requires examining its three-layer architecture:

Layer 1: Cortex Analyst - The Translation Engine

Cortex Analyst is the AI-powered query engine at the heart of Snowflake Intelligence. It performs a sophisticated multi-step process:

Step 1: Natural Language Understanding

User Question: "Show me revenue trends by region for the last 6 months"

Cortex Analyst Interprets:
- Metric: revenue
- Dimension: region
- Time period: last 6 months
- Visualisation: trends (line chart)

Step 2: Semantic Model Lookup

Cortex Analyst consults the semantic view to understand:

Step 3: SQL Generation

The system generates optimised SQL:

SELECT
    r.region_name,
    DATE_TRUNC('month', s.order_date) AS month,
    SUM(s.order_amount - s.discount_amount) AS revenue
FROM
    FACT_SALES s
    JOIN DIM_REGION r ON s.region_id = r.region_id
WHERE
    s.order_date >= DATEADD(month, -6, CURRENT_DATE())
GROUP BY
    r.region_name,
    DATE_TRUNC('month', s.order_date)
ORDER BY
    month, region_name;

Step 4: Execution and Visualisation

Cortex Analyst executes the query securely within Snowflake, respecting all row-level security policies and role-based access controls. Results are returned with an appropriate visualisation (in this case, a line chart showing revenue trends by region over time).

Layer 2: Semantic Views - The Business Context Layer

Semantic views are the crucial metadata layer that bridges business terminology with database schemas. They define:

Facts: Numeric columns suitable for aggregation

CREATE SEMANTIC VIEW sales_semantic
AS
SELECT * FROM FACT_SALES
WITH FACT_COLUMNS (
    order_amount (
        SYNONYMS ('sales', 'revenue', 'turnover'),
        DESCRIPTION = 'Total order amount in GBP',
        AGG = SUM
    ),
    quantity (
        SYNONYMS ('units sold', 'items'),
        DESCRIPTION = 'Number of items ordered',
        AGG = SUM
    )
)

Dimensions: Attribute columns for filtering and grouping

WITH DIMENSION_COLUMNS (
    region_name (
        SYNONYMS ('region', 'territory', 'area'),
        DESCRIPTION = 'Geographic sales region'
    ),
    order_date (
        SYNONYMS ('date', 'transaction date'),
        DESCRIPTION = 'Date when order was placed'
    )
)

Metrics: Pre-calculated business KPIs

WITH METRICS (
    net_revenue (
        EXPR = SUM(order_amount - discount_amount),
        DESCRIPTION = 'Total revenue after discounts',
        SYNONYMS ('net sales', 'revenue after discounts')
    ),
    average_order_value (
        EXPR = AVG(order_amount),
        DESCRIPTION = 'Average order value in GBP',
        SYNONYMS ('AOV', 'avg order size')
    )
);

This semantic layer is what enables Cortex Analyst to achieve over 90% SQL accuracy on real-world use cases. Without it, the AI would struggle to understand business context, resulting in incorrect queries and frustrated users.

Layer 3: Agentic AI Framework - The Orchestration Layer

Snowflake Intelligence leverages an agentic AI architecture where multiple AI agents collaborate to handle different aspects of the user interaction:

  1. Intent Recognition Agent: Understands user questions and clarifies ambiguities
  2. Query Planning Agent: Determines optimal query strategy and data sources
  3. Security Agent: Enforces row-level security and data access policies
  4. Visualisation Agent: Selects appropriate chart types and formatting
  5. Explanation Agent: Provides transparent reasoning about query logic

This multi-agent approach allows Snowflake Intelligence to handle complex scenarios, such as:

Under the Hood: LLM Integration

Snowflake Intelligence integrates state-of-the-art large language models from partners including:

Critically, Snowflake maintains strict security and privacy guarantees:

Why Snowflake Intelligence Matters for Customers

Snowflake Intelligence represents a paradigm shift in enterprise analytics. Here’s why it’s important across different stakeholder groups:

For Business Users

1. Self-Service Analytics Without SQL

Business users can now explore data independently, without waiting for data teams or learning SQL. This dramatically accelerates decision-making.

Before Snowflake Intelligence:

1. Business user emails data team: "Can you pull last quarter's sales by region?"
2. Data team adds to backlog (3-5 day wait)
3. Data analyst writes SQL query
4. Results delivered as static spreadsheet
5. User has follow-up question → repeat cycle

With Snowflake Intelligence:

1. User asks: "Show me last quarter's sales by region"
2. Instant results with visualisation
3. User asks follow-up: "What about year-over-year growth?"
4. Instant comparative analysis
5. Total time: seconds instead of days

2. Trusted, Governed Results

Unlike generic AI chatbots, Snowflake Intelligence ensures data accuracy and governance:

3. Multi-Modal Data Analysis

Snowflake Intelligence can synthesise insights from diverse data sources:

Example question: “Analyse customer satisfaction scores alongside support ticket text to identify recurring complaint themes affecting our NPS scores”

This would traditionally require data scientists to build custom NLP pipelines. Snowflake Intelligence handles it conversationally.

For Data Teams

1. Reduced Query Backlog

Data teams spend significant time writing ad-hoc queries for business stakeholders. Snowflake Intelligence offloads 60-80% of these routine requests, allowing data teams to focus on high-value work:

2. Consistent Business Logic

The semantic layer ensures one source of truth:

3. Incremental Adoption

Snowflake Intelligence doesn’t require ripping out existing infrastructure:

For Leadership and Executives

1. Faster Time-to-Insight

Competitive advantage comes from acting on data quickly. Snowflake Intelligence compresses analytics cycles from days/weeks to seconds/minutes.

ROI Example:

2. Data Democratisation at Scale

Organisations often struggle to scale data literacy. Snowflake Intelligence eliminates the need for universal SQL training:

3. AI Readiness

Implementing Snowflake Intelligence builds the foundation for broader AI initiatives:

Real-World Use Cases

Snowflake Intelligence is already deployed by over 1,000 customers with more than 15,000 AI agents in production. Here are representative use cases:

Use Case 1: Executive Dashboards

Scenario: CEO wants real-time view of business performance

Traditional Approach:

With Snowflake Intelligence:

Use Case 2: Financial Analysis

Scenario: CFO investigates expense variance

Question: “Compare Q3 operating expenses to budget by department, and flag any categories with >10% variance”

Snowflake Intelligence:

  1. Queries financial actuals from ERP system
  2. Retrieves budget data from planning system
  3. Calculates variances by department and category
  4. Identifies anomalies exceeding threshold
  5. Presents results with variance explanations

Use Case 3: Supply Chain Optimisation

Scenario: Operations manager investigates stockouts

Multi-Step Analysis:

User: "Show me products with stockouts in the last month"
→ Intelligence returns list of 15 products

User: "What's the revenue impact of these stockouts?"
→ Intelligence calculates lost revenue: £340K

User: "Analyse supplier delivery performance for these products"
→ Intelligence identifies 2 problematic suppliers with delays

User: "Show me alternative suppliers with better delivery records"
→ Intelligence recommends 3 suppliers from supplier database

This entire analysis, which would traditionally require multiple SQL queries, spreadsheet manipulation, and manual research, happens conversationally in under 2 minutes.

Use Case 4: Customer Segmentation

Scenario: Marketing manager wants to understand customer behaviour

Question: “Segment our customers by purchase frequency and average order value, then show me the demographics and product preferences of our top segment”

Snowflake Intelligence:

  1. Performs RFM (Recency, Frequency, Monetary) segmentation
  2. Identifies top customer segment
  3. Joins with demographic data
  4. Analyses product purchase patterns
  5. Delivers actionable insights: “Your top segment (18% of customers) are 35-50 year olds who purchase electronics monthly with £250 average order value. They prefer premium brands and respond well to email campaigns.”

Getting Started with Snowflake Intelligence

Implementing Snowflake Intelligence follows a structured approach:

Step 1: Create Semantic Views

Start with your most frequently queried tables. Define facts, dimensions, and metrics:

-- Example: Sales semantic view
CREATE SEMANTIC VIEW sales_semantic
AS
SELECT
    s.order_id,
    s.order_date,
    s.customer_id,
    s.product_id,
    s.region_id,
    s.order_amount,
    s.discount_amount,
    s.quantity
FROM FACT_SALES s
WITH SYNONYMS ('sales', 'orders', 'transactions')
COMMENT = 'Sales transactions with customer, product, and regional dimensions'
WITH FACT_COLUMNS (
    order_amount (
        SYNONYMS ('sales', 'revenue', 'turnover'),
        DESCRIPTION = 'Total order amount in GBP before discounts',
        AGG = SUM
    ),
    discount_amount (
        SYNONYMS ('discounts', 'promotions'),
        DESCRIPTION = 'Total discount applied in GBP',
        AGG = SUM
    ),
    quantity (
        SYNONYMS ('units', 'items sold', 'volume'),
        DESCRIPTION = 'Number of items ordered',
        AGG = SUM
    )
)
WITH DIMENSION_COLUMNS (
    order_date (
        SYNONYMS ('date', 'transaction date', 'purchase date'),
        DESCRIPTION = 'Date when order was placed'
    ),
    customer_id (
        SYNONYMS ('customer', 'account'),
        DESCRIPTION = 'Unique customer identifier'
    )
)
WITH METRICS (
    net_revenue (
        EXPR = SUM(order_amount - discount_amount),
        DESCRIPTION = 'Total revenue after discounts in GBP',
        SYNONYMS ('net sales', 'revenue after discounts')
    ),
    average_order_value (
        EXPR = AVG(order_amount),
        DESCRIPTION = 'Average order value in GBP',
        SYNONYMS ('AOV', 'avg order size', 'basket size')
    ),
    total_orders (
        EXPR = COUNT(DISTINCT order_id),
        DESCRIPTION = 'Total number of unique orders',
        SYNONYMS ('order count', 'number of transactions')
    )
);

Step 2: Enable Cortex Analyst

Grant necessary permissions:

-- Grant usage on Cortex schema
GRANT USAGE ON SCHEMA SNOWFLAKE.CORTEX TO ROLE analyst_role;

-- Grant access to semantic views
GRANT SELECT ON SEMANTIC VIEW sales_semantic TO ROLE analyst_role;

Step 3: Test with Sample Questions

Start with simple queries to validate semantic view accuracy:

Test Questions:
1. "What was total revenue last month?"
2. "Show me top 10 customers by revenue"
3. "Compare this quarter's sales to last quarter by region"
4. "What's our average order value over time?"

Review generated SQL to ensure accuracy. If queries are incorrect, refine semantic view definitions (add synonyms, clarify descriptions, adjust metric calculations).

Step 4: Train and Roll Out

Provide brief training to business users:

Step 5: Monitor and Iterate

Track usage and refine:

Best Practices

From experience working with Snowflake Intelligence implementations, I would recommend considering the below:

1. Start Small, Think Big

Do:

Don’t:

2. Invest in Semantic Layer Quality

The accuracy of Snowflake Intelligence depends on semantic view quality:

3. Governance First

Establish data governance before widespread rollout:

4. Educate Users on Capabilities and Limitations

Set realistic expectations:

Snowflake Intelligence is Excellent For:

Snowflake Intelligence Has Limitations:

5. Maintain Both AI and Traditional BI

Snowflake Intelligence complements (doesn’t replace) traditional BI:

The most effective analytics organisations use all three in harmony.

Common Challenges and Solutions

Challenge 1: Semantic View Creation Overhead

Problem: Creating semantic views manually for hundreds of tables is time-consuming.

Solution: Automate initial semantic view generation using AI (see our enhanced semantic view generator for a production-ready solution). AI generates 70-80% complete baseline semantic views in seconds, which you then review and customise.

Challenge 2: Query Accuracy Issues

Problem: Snowflake Intelligence generates incorrect SQL for certain questions.

Solution:

Challenge 3: User Adoption

Problem: Users revert to old workflows (emailing data team, using spreadsheets).

Solution:

Challenge 4: Multi-Table Relationships

Problem: Semantic views are single-table constructs; complex questions require joins.

Solution: Snowflake Intelligence can query multiple semantic views and join them automatically. Ensure:

Performance Considerations

Snowflake Intelligence query performance depends on several factors:

Semantic View Complexity:

Data Volume:

Optimisation Tips:

The Future of Snowflake Intelligence

Snowflake Intelligence is evolving rapidly. Expected enhancements include:

Short-Term (2025-2026):

Long-Term Vision:

Conclusion

Snowflake Intelligence represents a fundamental shift in how organisations interact with data. By combining natural language AI (Cortex Analyst), business metadata (semantic views), and agentic orchestration, it enables truly conversational analytics that is accurate, governed, and scalable.

Key Takeaways:

  1. For Business Users: Self-service analytics without SQL, instant answers to ad-hoc questions, trusted and governed results
  2. For Data Teams: Reduced query backlog, consistent business logic, incremental adoption path
  3. For Organisations: Faster time-to-insight, democratised data access at scale, foundation for broader AI initiatives

The technology is mature (general availability as of November 2025), proven (1,000+ customers, 15,000+ AI agents deployed), and continuously improving. Organisations that adopt Snowflake Intelligence now will gain competitive advantage through faster, more data-driven decision-making.

If you’re using Snowflake, exploring Snowflake Intelligence is no longer optional—it’s a strategic imperative. Start with a small pilot on high-value use cases, invest in semantic layer quality, and iterate based on user feedback. The results will transform how your organisation leverages data.

Features change from time to time with new features being added regularly, it is recommended that you review the documentation for the latest on what specific features are included with any of the Editions.

Further Reading

About the Author: Elliott Fairhall specialises in data platform engineering with a focus on Snowflake, modern data stack architecture, and AI-powered analytics solutions.

Sources