What does it mean for AI to understand your health system's business context?

Summary

• Business context means an AI system understands the meaning behind your data — which payer, contract, service line, or department a number comes from — not just how to retrieve the number.
• On Databricks, that context lives in the governed data layer: a semantic layer of metric views, a Unity Catalog business glossary, and the Genie Ontology give AI a business-aware map of your health system.
• Because definitions are set once and reused everywhere, dashboards, SQL, and AI agents all answer from the same trusted definitions, which reduces confident-but-wrong answers.
• Answers stay grounded and governed: every figure traces back to its source, and the permissions the asking user holds are enforced when AI answers on their behalf.
• The result: analysts, executives, and AI coworkers work from one governed source of truth for questions about cost of care, reimbursement, claims, and receivables.

What does it mean for AI to understand your health system's business context?

An answer can be perfectly accurate and still not be correct, because it rests on a partial or dated picture of how the business actually works. For a health system, business context means the AI knows which payer, which contract, and which service line produced a figure — and how each is changing as the organization evolves. This is a context problem, not a raw-intelligence problem: without shared business meaning, an AI system can answer with false confidence. On the Databricks Data Intelligence Platform, business context lives in the governed data layer so answers are rooted in your definitions and your permissions. See redefining semantics for the data layer and what enterprise intelligence means.

Why the Databricks Data Intelligence Platform gives AI your business context

• A governed semantic layer (metric views). Metric views centralize business definitions — for example net revenue, denial rate, or cost per case — in the data platform itself, so every consumer (dashboards, notebooks, SQL, and AI) uses the same definition rather than a private copy. See how business semantics power the data layer.
• A business glossary and domains. The Unity Catalog business semantics layer lets you define concepts explicitly and group them into domains, so the same term — such as "encounter" or "member" — carries the right meaning for each team instead of being left ambiguous.
• The Genie Ontology: a business-aware map. Genie combines governed Unity Catalog semantics with context inferred from your existing dashboards, queries, and metric views, ranks the most relevant context for each question, and answers using only sources the asking user is permitted to see. See the Genie Ontology documentation and Genie agent concepts.
• Grounded, sourced answers. Because every figure traces back to its source and stays governed under one model, teams can trust an answer enough to act on it — for questions such as where the cost of delivering care is outrunning reimbursement, where earned revenue is slipping to denials and underpayments, and where cash is tied up in aging receivables. See protecting margin with governed, sourced finance answers.
• Consistent context across people and agents. Define business meaning once and reuse it, so business users, analysts, dashboards, and AI agents do not have to guess what a term means or which number to trust.

Getting started

• Read how business semantics power the data layer for BI and AI.
• Define metrics and glossary terms once with Unity Catalog business semantics.
• See how the Genie Ontology gives AI a governed, business-aware map of your data.
• For a healthcare-finance perspective, read how a quality-care mission and finance protect margin with governed answers.

FAQs

What is the difference between an accurate answer and a correct one?

An answer is accurate when the number is right; it is correct when that same number is rooted in the right business context — the service line, payer, and contract it came from. Business context is what turns accurate retrieval into a correct, trustworthy answer.

Where does business context live on Databricks?

In the governed data layer: metric views define shared metrics, the Unity Catalog business glossary defines concepts and domains, and the Genie Ontology assembles these into a business-aware map that AI uses to answer questions.

How does Databricks keep AI answers grounded and permitted?

Answers are generated from governed sources, every figure traces to its origin, and the AI honors the asking user's permissions — so people only see the data they are allowed to see.

Do dashboards and AI use the same business definitions?

Yes. Because metrics and semantics are defined once in Unity Catalog and reused, dashboards, SQL, notebooks, and AI agents all answer from the same definitions, keeping results consistent.