Why does AI give the wrong answers when I ask about company data?

Summary

• Usually it isn't the model, it's missing business context. When AI can't find how your company defines a metric or which table is the trusted one, it fills the gap with inference and returns answers that are generic at best and wrong at worst.
• Three root causes dominate: business context is scattered across dashboards, queries, pipelines, wikis, and docs; metric definitions are ambiguous, so two teams compute the same number differently; and data is ungoverned, so an agent sees only raw tables and columns with no business meaning.
• The fix on Databricks is Genie Ontology, a continuously learned context layer that maps your raw tables and columns to the concepts, metrics, and definitions your business actually uses, and ranks knowledge by authority so AI resolves to the definition your organization trusts.
• Model business context once in Unity Catalog: metric views (governed KPIs), domains (business-aligned curation), and glossary pages (shared terms), with certifications marking trusted assets, so Genie resolves definitions instead of guessing.
• Because that context is governed and permission-aware, every agent, app, and BI tool, including over MCP, answers from the same trusted definitions, so numbers stay accurate and consistent across teams.

Why does AI give the wrong answers when I ask about company data?

When an AI assistant returns a wrong number for a business question, the problem is usually not the model's reasoning, it's the context. Out of the box an agent sees your data as raw tables and columns with no business meaning: it cannot tell which of several revenue tables is the trusted one, or whether an "active customer" means someone who logged in this month or someone who paid. The business context needed to answer correctly is scattered across dashboards, queries, pipelines, wikis, tickets, and chat threads, so when the AI can't find it, it fills the gap with inference and returns an answer that is generic at best and wrong at worst. The way to solve this on Databricks is to give AI a governed model of your business with Genie Ontology.

Why AI gets company-data questions wrong

• Missing business context. The context needed to use data lives across dashboards, queries, pipelines, wikis, tickets, and documents. When an agent can't find it, it has to guess from raw tables and columns, which leads to hallucinations and incorrect answers. Most weak or inconsistent agent answers come down to missing context, not the prompt.
• Ambiguous metric definitions. When multiple definitions of a metric exist, for example "net sales" calculated one way by Finance and another by Operations, the agent tends to pick the first table it finds rather than the one the business actually maintains. Without a single authoritative definition, teams end up arguing about what the numbers mean instead of acting on them.
• Ungoverned data. Without semantic governance, an agent sees only raw tables, columns, and keys with no idea what they represent in business terms. Business users think in concepts like "customer," "order," and "yield," not in primary and foreign keys, and that gap is exactly where accuracy is lost.
• Slow, costly guessing. Lacking explicit definitions, a generic agent resorts to slow, costly iterative probing to reconstruct context on every request, which compromises both quality and speed.

Why Databricks Genie Ontology gives AI the right answers

• A continuously learned context layer. Genie Ontology (currently in Public Preview) automatically extracts knowledge from the tables, queries, dashboards, pipelines, and connected apps already in your workspace and builds a permission-aware context graph, mapping raw tables and columns to the concepts, metrics, and definitions your business uses. It ranks that knowledge by authority, so when multiple definitions of a metric exist, Genie resolves to the one your organization trusts most.
• Metric views govern your KPIs once. Metric views are reusable, governed Unity Catalog objects that define how a measure is calculated one time. They compile into logical queries at runtime, so when someone asks about "revenue" or "active users," Genie retrieves the authoritative definition instead of re-deriving logic that differs across teams. Genie resolves the metric from a single source of truth rather than hallucinating it, and metric views are generally available.
• Domains and glossary pages make meaning explicit. Domains (preview) group assets by business function so an agent gets scoped, relevant context instead of the whole catalog, and glossary pages (private preview) document business concepts, synonyms, and definitions, capturing the tribal knowledge and jargon specific to your company. The same term can legitimately mean different things in Finance versus Sales, and domains and glossary pages make that explicit instead of leaving it contested.
• Certifications mark what to trust. Assets you certify signal trustworthiness to Genie, so it prioritizes governed definitions over inferred ones and steers away from stale or deprecated assets.
• Grounded answers, consistent everywhere. Business context is modeled once and reused everywhere: Genie, dashboards, agents, and BI or custom apps all inherit the same definitions, and any agent or client can reach them over MCP. The same permission model, lineage, and audit path govern every surface, so answers stay accurate and consistent and each response respects the asking user's permissions.

Getting started

Giving AI reliable answers on company data is a modeling exercise in Unity Catalog, followed by connecting Genie and your agents on top.

• Define metric views for your key KPIs, such as revenue, active users, and net sales, as governed Unity Catalog objects so every tool computes them the same way.
• Organize assets into domains by business function (Sales, Finance, Operations) so agents get scoped, relevant context.
• Author glossary pages that document business concepts, synonyms, and the assets they map to, capturing definitions that live only in people's heads today.
• Certify trusted assets so Genie prioritizes them, and deprecate the ones that are out of date.
• Build Genie Agents on your metric views, and connect other agents and clients over MCP so they inherit the same governed definitions.
• Follow the docs on Genie Ontology and Unity Catalog semantics, and read Introducing Genie, Redefining the semantics data layer for the future of BI and AI, and Designing effective Genie agents for the full picture.

FAQs

Why does AI give wrong answers about company data?

Usually because it lacks business context. It sees raw tables and columns and has to guess which metric definition or which source table is correct, so it fills the gap with inference. Modeling that context, with metrics, domains, glossary pages, and certifications, lets AI answer from your organization's trusted definitions instead of guessing.

What is Genie Ontology?

Genie Ontology is Databricks' continuously learned enterprise-context layer. It automatically extracts knowledge from the tables, queries, dashboards, and apps already in your workspace, ranks it by authority, and gives AI a business-aware map of your organization so answers are grounded in your data rather than guessed.

How do metric views make AI answers more accurate?

Metric views define each KPI once as a governed Unity Catalog object and compile into logical queries at runtime. When someone asks for "revenue," Genie resolves the certified definition from a single source of truth instead of re-deriving logic that differs across teams, so the number is consistent everywhere.

Does giving AI business context keep answers secure?

Yes. The ontology builds a permission-aware context graph, and every response respects the asking user's permissions. The same permission model, lineage, and audit path govern SQL, dashboards, Genie, and MCP-connected agents, so accuracy and governance hold across every surface.