For years, business intelligence was something you pulled—queries, dashboards, KPIs manually stitched together by analysts and served to decision-makers through layers of tooling. But something is shifting. Now, with the evolution of large language models and Snowflake’s integrated AI capabilities, we’re entering a new phase of analytics: one where you can actually talk to your data—and it talks back.

At Snowflake Summit 2025, this idea took center stage. But beyond the headlines, there’s a deeper movement underway: empowering every team — not just analysts — with real-time, reliable, and context-aware insights.

Let’s unpack what this means, why it matters, and what early adopters should know about putting it into practice.

What Is Snowflake Cortex and Why Should You Care?

Snowflake Cortex is the company’s integrated suite of AI and LLM tools, designed to bring generative intelligence directly into the Data Cloud. It’s not another tool bolted onto your BI stack — it’s within your data platform. That distinction is important.

Here’s what Cortex unlocks:

• Natural language querying of your structured and unstructured data

• On-the-fly document summarization, sentiment analysis, and classification

• Custom LLM workflows that stay inside your governance perimeter

• App dev frameworks to embed intelligent agents directly in operational tools

So instead of waiting two weeks for an analyst to answer ​“What’s driving churn in the Northeast?”, your team lead just asks—in plain English. And the answer? Context-rich, sourced, and actionable.

That’s a leap. Not because the tech didn’t exist — but because it’s now operationally viable.

Why Talking to Data Isn’t Just a Gimmick

For early adopters, this shift solves a longstanding problem: insight latency.

The issue isn’t a lack of data. It’s the friction between a business question and a useful answer:

• Too many handoffs between teams

• Too much context lost between systems

• Too little time for iteration

With GenAI interfaces and Snowflake’s native LLM functions, you close that gap. You shorten the loop between question, analysis, and decision.

Let’s say your marketing team wants to know why a recent promo underperformed. Before:

1. Submit a data request

2. Wait for an analyst to pull and clean data

3. Review results in a weekly meeting

4. Guess at what to try next

Now?

• The team asks the assistant in their workspace

• It pulls relevant campaign metrics, customer segments, and clickstream data

• Summarizes key drivers

• Suggests what to test next — immediately

That changes the rhythm of decision-making. And the speed of iteration becomes your new competitive edge.

Use Cases That Are Already Working

These aren’t just theoretical workflows. Real organizations are deploying ​“intelligent data conversations” across verticals:

1. Retail & CPG

Merchandisers are querying product-level sales trends across locations, comparing them to promotions, and identifying local pricing anomalies — all with natural language.

2. Healthcare & Life Sciences

Teams are reviewing unstructured clinical notes and summarizing trial performance. Snowflake’s native document functions let you process PDFs and EMRs alongside structured data.

3. Manufacturing

Supply chain managers are asking agents to detect anomalies in inventory flow, then generate root cause hypotheses by pulling from shipping logs and supplier contracts.

4. Financial Services

Advisory teams are creating client summaries from multiple account systems — then drafting outreach emails, all grounded in up-to-date customer data.

Each case shares a theme: no more switching between dashboards, spreadsheets, and static reports. Just answers.

Challenges You’ll Encounter (and How to Handle Them)

As with anything new, this isn’t plug-and-play. You’ll hit a few bumps. Here’s what to watch for:

1. Prompt Quality Matters

The model only knows what you ask — and how you ask it. Building reusable prompt templates and fine-tuning them to your schema is critical.

2. Trust and Explainability

“Because the AI said so” isn’t enough. Your outputs need source links, query tracebacks, and clarity about data freshness. Cortex provides that — but only if you wire it right.

3. Role-Based Access and Security

Talking to data doesn’t mean everyone should access all the data. Align Cortex with your existing Snowflake policies and RBAC (role-based access control).

4. Training and Culture Change

Non-technical users need guidance — what to ask, what not to assume, and how to interpret results. Don’t skip onboarding. And pair launches with real, measurable use cases.

Where to Start If You’re Early in the Journey

You don’t need a full-stack GenAI team to try this. Here’s a practical starting path:

• Start with one function—like document summarization or sentiment analysis in Cortex

• Create a sandbox with limited datasets and test your prompt engineering

• Choose one pilot team (e.g., RevOps, Finance) to give feedback

• Instrument everything—track query volumes, satisfaction, and downstream actions

Then iterate. The goal isn’t to replace analysts or BI dashboards. It’s to make everyday decisions faster, better, and closer to the front lines.

Final Thought: Talk to Your Data, or Compete With Someone Who Does

We’re entering an era where waiting for insight will be as outdated as waiting for a fax. The businesses that thrive will be the ones who think faster, because their data systems respond like teammates — not ticket queues.

Snowflake Intelligence isn’t hype. It’s the foundation for a more conversational, contextual, and competitive approach to analytics.

Early adopters aren’t asking ​“What’s the ROI on this AI tool?” They’re asking, ​“Why wouldn’t we want our best people talking directly to our most valuable data?”

That’s the shift. And it’s already happening.