Snowflake Intelligence: How to Build an Infrastructure for AI Success

On a cold January morning in 1848, James W. Marshall stood in the shallows of the American River, inspecting a sawmill he was building for John Sutter. As sunlight hit the water, something flickered. He looked closer. There! A small yellow glint among the stones. Gold!

Within months, the world rushed west chasing fortune. Prospectors sought wealth in the riverbeds and soil. Others saw opportunity not in extraction but in construction, building the systems that made the rush possible.

Sam Brannon became California’s first millionaire not by panning for gold, but by selling picks, shovels and supplies to miners. The gold was the spark. The infrastructure was the engine. Roads, tools, supply chains, and systems all sprung up to support the prospectors’ race for riches. Meanwhile, infrastructure builders who created this foundation enabled profound and enduring economic growth for the future.

Both prospectors and builders played a role. But it was the builders who turned a moment into a movement, transforming chaos into commerce and ambition into industry.

As Mark Twain once said, history never repeats itself, but it does often rhyme.

Today's AI market is exploding toward $600 billion, with growth rates near 30% annually. Every company has AI on their 2025-2026 roadmap. Across industries, the mandate is clear: "Infuse AI into everything we're doing." The rush on AI mines has been heating up for the last few years and it’s now in full force.

But here's the uncomfortable truth: analyst firms report that up to 80% of AI pilots fail to reach production. Most organizations can’t adequately describe the value they’re getting from AI currently. The problem isn't lack of ambition. It's a lack of foundational strategy and infrastructure. Companies have been rushing to find gold, but the foundations for AI success and enablement are lagging.

The question isn't whether to invest in AI, or even where. The question is: How do you build the foundations and infrastructure in your organization that make achieving value with AI easy and inevitable?

Let’s start by looking at the technical components that must be in place for AI to operate in the enterprise, and then further break those down into capabilities and tools under each of those components. Just be careful not to think of this as a stack of tools that you need. Instead, think of it as a stack of capabilities grouped into these four basic components of AI.

Every AI system needs these four core components:

1. Models

• Whether it’s an LLM predicting tokens or an ML model forecasting sales, you need something that captures how the world behaves.
• LLMs, ML models, agents, workflows, processes, algorithmic system models, rule-based models and embedded AI applications within vendor products

2. Data

• Models learn from data. If you’re using a pre-trained model as-is, great. If not, you’ll need to supply your own data to make it relevant in your context.
• Storage, compute, governance (security and quality etc.), data integration, data modeling, files, tables, observability and machine learning operations (MLOps)

3. Context and Ecosystem

• Foundation models don’t automatically know your business, systems or what tools they can use. You must teach them to operate within your world.
• Partners, APIs, agentic and other frameworks, Model Context Protocol (MCP) or other protocols, systems, tools and marketplaces

4. Compute

• None of it works without scalable compute to power inference and automation.

Cloud or on-premises, memory storage, CPUs and GPUs

When you are beginning your AI journey, start by mapping business goals and use cases to the different capabilities. Start small and deliver quick wins but keep the roadmap in view.

As you start to scale up your AI program, you’ll go quickly from just a few use cases to dozens of use cases all requiring different capabilities. Managing the technical landscape can get complicated very quickly. This is where a platform partner makes the difference between pilot purgatory and production success.

At CDW, we’re excited about what Snowflake is doing with Snowflake Intelligence. The new offering is the next evolution of the Snowflake Data Cloud. It brings intelligent data agents, natural language interaction and secure AI workflows directly into the platform. At its core, it allows organizations to operationalize AI without exporting data or managing disconnected tools. Instead of moving your data to your AI tools, you're bringing AI capabilities to where your data already lives.

What it includes:

• Native AI agents that query, reason and act on enterprise data
• Retrieval-augmented generation (RAG) and vector search for working with structured and unstructured data
• Document understanding integrated with models and agentic workflows
• Agentic workflows that can summarize, analyze and execute based on insights
• Seamless integration with apps like Salesforce and Slack

Built-in features:

• Native support for structured and unstructured data
• Understands context through built-in metadata framework (semantic layer)
• Agents work across your organization’s full data landscape, from tables and files to documents and external applications
• Lineage, auditability and role-based access control (RBAC)
• Consistency in governance across analytic and AI workloads and applications

Because this all runs inside Snowflake's governed, multi-cloud environment, your AI maintains the same security, compliance and governance that you’ve grown accustomed to enjoying with your traditional data warehouse operations.

Most organizations are struggling to operationalize even single proof-of-concept deployments of AI. Aside from a few notable exceptions, most AI use cases are isolated and sporadic, sprinkled throughout organizations without enterprise governance or visibility. Snowflake Intelligence provides a solution.

Snowflake spent over a decade building one of the most trusted data warehousing platforms in the market, with deep focus on governance, security and data management. Snowflake Intelligence extends that same rigor to AI workloads.

When your AI agents access customer data, they respect the same access controls your analysts use. When you audit a decision, you can trace it back through the data lineage. When you scale from three use cases to thirty, you're not retrofitting governance and creating frameworks for new workflows and applications. It's already built-in.

This is what separates AI pilots from AI production. And building with a platform allows organizations to go from a few random AI use cases to an enterprise program, systematically creating value and ROI for the organization.

With Snowflake Intelligence, organizations can now interact with their data using natural language. But beyond that, businesses want to automate reasoning and action. With intelligent agents and secure workflows integrated with your business context, the Snowflake Intelligence platform now opens entirely new possibilities. Here are a few examples of how early adopters are beginning to apply these capabilities:

• Sales teams can explore performance across regions, uncover anomalies and take actions like adjusting forecasts or notifying stakeholders without ever writing queries.
• Support teams can surface patterns in tickets and logs, identify emerging issues and streamline resolution by connecting insights across systems.
• Marketing teams can analyze campaign performance, flag underperforming segments and refine targeting strategies all from a conversational interface.
• Operations teams can combine structured and unstructured data to answer complex questions like “Why did product views drop last quarter?” and initiate follow-up actions automatically.

Snowflake Intelligence enables organizations to move from insight to action within its secure and scalable platform. With this foundation in place, organizations can enable their teams to get creative in finding valuable workflow enhancements and new capabilities altogether.

Sam Brannon didn't strike gold. He sold the tools that made the gold rush possible.

Today, the tools aren't picks and shovels. Instead, we dig with capabilities that unify data, models, context and compute with a layer of governance to create safe and ethical AI solutions for our organizations. Platforms bringing those pieces together let you scale from pilot to production without rebuilding your foundation every time.

Most of us will never get a chance, like James Marshall, to sit on a secret of such import as discovering gold. And yet, didn’t some of us feel the same awe when we first tried ChatGPT, and perhaps stole a furtive glance behind us wondering if anyone else was seeing what we were seeing? Or perhaps we felt like Sam Brannon, seeing the immense opportunity; we wanted to be the first to capitalize on it.

The opportunity is still ripe with potential, and Snowflake Intelligence can act as our modern picks and shovels. The platform brings together the needed capabilities into a foundation that organizations can use to scale up prolific and successful AI programs.

If you're exploring how to bring AI to your enterprise, CDW can help. Our data and AI experts can assess your current environment, identify where Snowflake Intelligence fits best and design a roadmap to get measurable value fast.

Instead of bringing your data to AI, bring AI to your data.