Context Silos: How Warehouse-Native AI Is Rebuilding the Data Silos We Just Escaped

For most of the last decade, the central project of data teams was tearing down silos. Data sat trapped in departmental databases, spreadsheets, and applications that never spoke to each other. The answer was consolidation: cloud warehouses, lakehouses, and a data catalog on top, so the whole organization could query one governed store instead of a dozen disconnected ones. That battle is mostly won. Your data is more centralized than it has ever been.

AI is quietly reopening it from a different angle. The storage silos are gone, but a new kind of fragmentation is forming one layer up, where business context lives. Every AI feature has to know what your data means before it can answer a question or take an action, and right now each tool is building that understanding on its own, in its own format, behind its own walls. The data is shared. The meaning is not.

A context silo is an isolated store of business context that one AI tool builds and uses, invisible to every other tool. It holds the things an AI needs to be useful: the definitions of metrics and entities, the relationships between them, the access rules, and the signals about what can be trusted. A semantic layer or model authored inside a single platform is the most common form.

The idea is starting to get a name. Engineers increasingly call these context silos, the meaning-layer echo of the storage silos we just escaped, a shift the database community has framed directly as a move from data silos to context silos.

The trap is that it looks like progress. Defining "active user" or "net revenue" so a tool can answer questions about it is governance work, and it is worth doing. The problem is where that definition lives. When it sits inside one warehouse's AI service, it cannot be read by the BI assistant next door, the agent platform your operations team just bought, or the copilot embedded in your CRM. You have governed the meaning for one consumer and left every other consumer to guess, or to build its own version that drifts apart from yours.

The major platforms have shipped genuinely good AI inside their walls, and each one builds context the platform owns. Snowflake Cortex Analyst reads a YAML semantic model and semantic views you author, governed by Horizon, and turns plain-English questions into SQL that runs on Snowflake. Databricks AI/BI Genie leans on Unity Catalog, so its row-level security and column masking flow straight into the assistant. Gemini in BigQuery does the same for Google's stack. All three reached general availability by 2026, and inside their own platform each is excellent.

The boundary is the whole point. The "active user" you define in Cortex's semantic model is invisible to Genie. The masking policy Unity Catalog enforces for Genie does not travel to Cortex. Each assistant is governed, and each is governed only within the four walls of its platform. Most enterprises run more than one warehouse, plus a BI tool, plus a handful of SaaS applications with their own embedded AI. The native catalogs are warehouse-bounded by design, so the same metric ends up defined three times, slightly differently, with no system responsible for reconciling them.

This is not an argument against Cortex or Genie. They are the right tools for querying their own platforms. It is an argument about what happens when you have several of them and no layer above that holds a single, shared definition of what your business means.

Assistants that answer questions are the gentle version of this problem. Agents that take actions are the sharp one. OpenAI Frontier, launched in February 2026, lets enterprises build and manage AI agents that connect to data and applications and execute real work across systems. Microsoft, Salesforce, and most major SaaS vendors now ship copilots that do the same inside their own products.

Frontier governs who an agent is and what it is allowed to do: identity, permissions, guardrails. What it does not supply is what your data means, whether it can be trusted, and where it came from. It assumes that governed context already exists somewhere it can reach. Every agent platform that fills that gap by building its own internal context creates one more silo, except this silo acts. A misread definition no longer produces a wrong sentence a human reviews; it produces a wrong action, propagated across systems at machine speed.

The pattern repeats with every new tool. The list of places your business context lives keeps growing, and each addition is governed in isolation. For a deeper look at the governance gap Frontier opens, see our Frontier-readiness checklist.

Fragmented context is not a theoretical tidiness problem. It shows up on the bill in four ways.

• Inconsistent answers erode trust. When the same question returns different numbers depending on which assistant you ask, people stop trusting all of them and go back to exporting to spreadsheets. The AI investment quietly stops paying off.
• Governance gaps open up. A masking policy or PII classification enforced in one tool is absent in the next. Compliance evidence you can produce for one platform does not exist for another, which is a real exposure under the EU AI Act and GDPR.
• Effort is duplicated. Your team redefines the same glossary, the same metrics, and the same relationships in every tool that needs them, and then maintains every copy as the business changes.
• Context gets locked in. When the institutional knowledge that makes your AI work is authored inside a vendor's platform, it is hard to move, hard to audit independently, and hard to reuse with the next agent platform you adopt.

The way out is not to ban warehouse-native AI or pick a single vendor and hope you never need another. It is to stop treating context as something each tool owns, and start treating it as a shared asset you own and every tool consumes.

Govern context once, in infrastructure you control: a catalog of what data exists and what is authoritative, a business glossary of what each term means, classification of what is sensitive, and lineage of where everything came from. Then serve that governed context to every AI tool through an open standard, the Model Context Protocol (MCP), so each platform reads from the same definitions instead of inventing its own. The warehouse assistants, the copilots, and the agents become consumers of your context, not custodians of it. Your meaning, your rules, and your lineage stay owned and governed on your side, and the answer to "what is an active user" is the same no matter which tool asks.

This is the job Dawiso is built for. It connects to more than 40 platforms and builds one governed foundation across all of them: a Data Catalog of what exists, a Business Glossary of what it means, classification of what is sensitive, and Interactive Data Lineage of where it came from. The meaning lives in one place, spanning every warehouse and tool, rather than fracturing across them.

Through the Context Layer and its MCP Server, Dawiso serves that governed context to any MCP-compatible assistant or agent. A Cortex query, a Genie dashboard, and a Frontier coworker can all draw on the same definitions, the same lineage, and the same policies, because they read from a layer you own instead of building their own. The warehouses keep their native AI. Your business keeps a single, governed understanding of its data, and the next tool you adopt becomes one more consumer rather than one more silo.

The data silos took a decade to dismantle. Context silos do not have to repeat the story. Govern context once, own it, and let every AI tool read from the same source of truth.