The Missing Layer: Why Enterprise AI Stalls Before It Starts

Your cloud data warehouse has the data. Sigma gives your team the interface to explore it, build apps on it and take action. But when AI agents answer questions about your business, they need more than tables and columns. They need to know what the data means — which metrics are certified, how "revenue" is defined in your org and which tables are the source of truth versus someone's one-off analysis from two years ago.

That context has traditionally lived in people's heads, scattered across Confluence pages and tribal knowledge. Getting it connected to AI in a structured, governed way is the gap that separates impressive demos from production-ready AI.

Sigma and Atlan are closing that gap together. Here's how.

How Sigma and Atlan already work together

Sigma has had a native integration with Atlan for years. Atlan crawls Sigma metadata — workbooks, pages, data elements, fields and lineage — before it flows into Atlan’s Enterprise Context Layer, alongside your warehouse assets, dbt models and everything else in your data stack.

For teams already using both platforms, this means:

• Lineage from warehouse to workbook. Atlan traces how data flows from source tables through transformations into Sigma visualizations. When something changes upstream, you can see exactly which Sigma workbooks are affected. 
• Semantic context at point of use. Atlan's Chrome extension surfaces certified definitions, ownership, quality signals, and governance status, directly inside Sigma workbooks — so AI agents get full context without leaving their workflow.
• Governed discovery. Teams and AI agents can search for Sigma workbooks in Atlan alongside warehouse tables, dbt models and other assets, with previews that help them find trusted content fast.

What breaks when AI doesn't have context

Sigma Assistant and Sigma Agents are already helping teams ask questions in natural language and get answers grounded in live warehouse data. But the quality of AI answers depends directly on the quality of the context available to the AI.

Consider what a great human analyst knows that a schema doesn't capture: "closed won" means something different in Salesforce than in the finance system. The Southeast region's numbers always lag by a week. "Enterprise ARR" was redefined after last quarter's acquisition. A metric defined in Q1 may carry a completely different meaning by Q3. 

Without this context, AI agents don't produce errors. They produce confidently wrong answers — the kind that erode trust before anyone notices.

Static documentation doesn't solve this. Column comments and data dictionaries go stale within weeks. The people who truly know what "revenue" means in your org are spread across finance, sales ops and data engineering. Getting that knowledge captured, structured and connected to live data isn't a documentation project. It's an infrastructure problem.

Atlan's Enterprise Context Layer

This is the problem Atlan's Enterprise Context Layer is built to solve. Instead of treating

context as a passive reference, Atlan provides AI agents with machine-readable business meaning: certified metric definitions, entity relationships, data quality signals and governance rules — all connected to live data and updated as your business evolves.

For Sigma users, this matters because richer context flowing into the platform means more

accurate AI responses and higher trust from the business teams relying on them. When Sigma Assistant or a Sigma Agent can access not just the data, but the full context around it — what's certified, what's deprecated, who owns it, how it's defined — answers get materially better.

What comes next

Sigma is proud to join Atlan as a Context Layer Partner at Atlan Activate on April 29. The event brings together data and AI leaders to discuss how enterprise context infrastructure accelerates production AI.

We've spent years building a platform that makes data accessible and actionable for business users. Atlan has spent years building the infrastructure that gives data its meaning. Together, the combination delivers what enterprise AI needs to move from pilot to production: governed data, trusted context and the interface where teams actually get work done.

Explore the Sigma + Atlan integration, or try Sigma free to see how Sigma Assistant and Sigma Agents work on your warehouse data.