Snowflake AI in 2025: Making Enterprise AI Simple, Secure, and Impactful
Introduction
AI adoption is no longer a future ambition — it’s a present-day necessity. In 2025, Snowflake has taken bold steps to make artificial intelligence (AI) practical for everyday business. Whether you’re a business analyst, team lead, or executive, these enhancements make it easier to use AI safely, efficiently, and with measurable value.
This article walks through:
- Snowflake’s newest AI tools like Cortex and Document AI
- Real-life examples and SQL code
- Economic results from actual businesses, and
- How different teams — from HR to Finance — are using AI today.
What’s New in Snowflake AI?
Cortex AI — Ask Questions, Get Answers Instantly
What it is:
Cortex AI is Snowflake’s built-in AI assistant powered by large language models (LLMs) like Meta’s Llama 3 and Mistral. It enables users to run AI queries using simple English — directly inside Snowflake’s Data Cloud — without needing separate tools or custom ML pipelines.
Why it matters:
Instead of waiting days for a data analyst to write a query, anyone can ask questions like:
“Who are our top-performing customers this quarter?”
… and get a complete, contextual answer instantly.
In Snowflake’s AI Predictions 2025 report, over 68% of leaders said they expect every business user to interact with data using natural language within the next 12 months .
Example:
Using this in Snowflake SQL is easy thanks to their CORTEX_COMPLETE() function.
CORTEX_COMPLETE(
‘llama3-70b’,
‘List customers who used our app fewer than 3 times in April 2025 and might be at risk of churning.’,
OBJECT_CONSTRUCT(‘table’, ‘user_activity’)
) AS ai_insight;
This single line replaces hours of analysis and gives customer success teams immediate, actionable insights.
Business Value:
- Analysts save time on repetitive questions.
- Sales and support teams become more self-sufficient.
- Executives get instant, data-driven answers without dashboards.
Document AI — From PDFs to Insight
What it is:
Document AI lets Snowflake read and interpret unstructured documents — contracts, invoices, claims, scanned forms — and convert them into clean, searchable tables.
Why it matters:
Over 80% of business data is unstructured. Most of it goes unused because extracting insights manually is time-consuming. Document AI solves that by turning scanned text into structured fields — ready for automation, analysis, or audit.
According to the GenAI Research Report, only 11% of enterprises said their unstructured data was AI-ready. Snowflake’s Document AI closes that gap.
Example:
Scanning documents in SQL is likewise easy with Snowflake’s EXTRACT_DOCUMENT_CONTENT() function.
SELECT
INVOICE_ID,
AMOUNT
FROM
TABLE(
EXTRACT_DOCUMENT_CONTENT(
'@finance_pdf_stage/invoices/',
'invoice_template'
)
);
This lets finance teams auto-extract key data points from bulk PDFs and plug them into downstream systems — no copy-pasting required.
Business Value:
- Save hours of manual data entry per document.
- Use AI to extract contract expiration dates, payment terms, and more.
- Make regulatory reporting faster and more accurate.
AI That’s Ready for Production, Not Just Prototypes
What it is:
Snowflake’s AI platform now includes tools to scale and govern models in production: infrastructure automation, cost tracking, scheduling, security controls, and integration with external ML services (like Azure, SageMaker, or Hugging Face).
Why it matters:
Many AI initiatives fail because they’re hard to maintain after the prototype stage. Snowflake’s approach removes infrastructure overhead and turns models into managed services.
In Forrester’s Total Economic Impact™ study, organizations saw a 354% return on investment, recovering costs in under 6 months by moving from siloed AI experiments to Snowflake-managed production pipelines.
Example:
CREATE OR REPLACE FUNCTION predict_sales(product_id STRING)
RETURNS FLOAT
AS
$$
SELECT CALL_EXTERNAL_MODEL(
'forecast_model',
OBJECT_CONSTRUCT('product_id', product_id)
);
$$;
SELECT
product_id,
predict_sales(product_id) AS forecast_units
FROM product_catalog;
This example shows how Snowflake connects to external ML models (e.g., SageMaker, Vertex AI) and makes them callable inside SQL.
Business Value:
- Deploy ML models faster and cheaper.
- Avoid the high cost of separate AI infrastructure.
- Let business systems (e.g., supply chain apps) access AI via simple SQL.
AI Agents & Copilots — Automate Complex Workflows
What it is:
Snowflake now supports AI agents — advanced automation flows powered by LLMs that don’t just answer questions but complete entire tasks like summarizing reports, triggering alerts, or sending emails.
Why it matters:
AI is evolving from assistant (copilot) to autonomous actor (agent). That means fewer repetitive tasks for humans — and faster, more consistent outputs across teams.
According to Snowflake’s GenAI research, 75% of executives said AI agents will soon run critical internal workflows — like compliance monitoring, operational reporting, or support response generation.
Example: Weekly HR Feedback Automation
— Summarize weekly feedback with Cortex
CREATE OR REPLACE VIEW weekly_feedback_summary AS
SELECT
CORTEX_COMPLETE(
'mistral-7b',
'Summarize common themes in this week''s employee feedback.',
OBJECT_CONSTRUCT('table', 'employee_feedback')
) AS summary;
-- Email results every Monday
CREATE OR REPLACE TASK send_feedback_summary
WAREHOUSE = compute_wh
SCHEDULE = 'USING CRON 0 9 * * MON UTC'
AS
BEGIN
CALL send_email(
'hr@company.com',
'Weekly Employee Feedback Summary',
(SELECT summary FROM weekly_feedback_summary)
);
END;
HR no longer needs to manually review surveys — the agent does it weekly, automatically.
Business Value:
- Saves HR teams time each week.
- Provides consistent, well-formatted summaries.
- Boosts employee engagement with faster follow-up.
AI That Pays Off — Proven Economic ROI
Forrester’s 2025 Total Economic Impact report shows exactly how Snowflake AI translates to cost savings and business growth.
| Category | Three-Year Value (Composite Organization) |
| Increased profits from faster AI | $5.4M |
| Efficiency gains (Ops + Analysts) | $6.2M |
| Data team productivity boost | $7.7M |
| Infrastructure cost reduction | $5.6M |
| Faster time-to-market | 35% shorter development cycles |
| Total ROI | 354% return in less than 6 months |
Summary: Key Benefits of Snowflake AI in 2025
| Feature | What It Does | Benefit |
| Cortex AI | Use natural language to ask questions about your data | Empowers everyone to explore data |
| Document AI | Extract data from PDFs, scans and images | Automates manual work and speeds up decisions |
| Production AI | Run secure, scalable models with monitoring | Reduces AI costs, boosts reliability |
| AI Agents | Automate full workflows like alerts, summaries and emails | Saves time and improves consistency |
Use Cases by Department
| Team | How Snowflake AI Helps |
| Marketing | Generate product descriptions, analyze campaigns and summarize trends |
| Sales | Surface top prospects, recommend next actions, summarize call notes |
| Finance | Automate invoice processing, detect anomalies, create executive summaries |
| Operations | Predict demand, optimize workflows, automate alerts |
| HR | Summarize surveys, support employee Q&A, streamline onboarding |
| Support | Power chatbots, summarize tickets, recommend responses |
Final Thoughts
In 2025, Snowflake has removed the barriers to meaningful AI. Whether you’re trying to make better decisions, automate repetitive tasks, or unlock new insights from your data, Snowflake’s AI capabilities offer:
- Ease of use for non-technical teams
- Enterprise-grade performance and governance
- Real ROI with measurable business outcomes
This is the year AI goes from idea to reality — and Snowflake makes it easier than ever to take that step.
