How can non-technical teams ask questions about company data with AI?

Summary

• Non-technical teams ask questions in plain language and get an answer as a table, a chart, or a short explanation, with no SQL or BI tool required. On Databricks, that interface is AI/BI Genie.
• The hard part isn't parsing English, it's knowing what the business means. Genie translates a question into a governed query using the metrics, table meanings, and rules your organization already relies on.
• Answers are permission-aware. Genie runs read-only queries through Unity Catalog, so two people can ask the same question and each sees only the data they're allowed to see.
• Trust comes from transparency. Genie can show the query behind an answer and will ask a clarifying question when a request is ambiguous instead of guessing.
• Genie Ontology, in Public Preview, adds a context layer that automatically builds a map of your data and business meaning from assets you already have, so Genie interprets questions the way your teams do.

How can non-technical teams ask questions about company data with AI?

Non-technical teams ask questions about company data with AI through a conversational interface that turns a plain-language question into a governed query and returns an answer as a table, a chart, or a short written explanation. On Databricks, that interface is AI/BI Genie. A marketer, an operations lead, or a finance partner types a question the way they would ask a colleague, and Genie translates it into SQL that runs against governed data. The result is an answer in the flow of work instead of a ticket filed with an analyst and a wait of hours or days.

What actually makes this work

Parsing English is the easy part. The real requirement is business context: the system has to know that "active customer" or "net revenue" means the specific thing your company decided it means, or the answer will be confidently wrong. Reliable natural-language analytics depends on connecting the question to your certified metrics, your table and column meanings, and your business rules, then running only queries the asker is permitted to run.

What non-technical teams need to ask questions with confidence

Requirement	Why it matters
Plain-language interface	Users should get an answer without writing SQL or learning a BI tool.
Business context and semantics	The system must map terms to your certified definitions, or answers drift from the truth.
Permission-aware answers	People should see only the data they are authorized to see, and the AI must enforce that.
Transparency	Users trust an answer more when they can see the query or sources behind it.
Clarification over guessing	When a question is ambiguous, the system should ask a follow-up rather than return a wrong answer.
Consistency with dashboards	A number in an answer should match the number on the certified dashboard.

How Databricks approaches it with AI/BI Genie

AI/BI Genie is a conversational interface that lets business users ask data questions in natural language and get back a governed answer. Genie is generally available. Every query it generates is read-only and runs inside Unity Catalog, so it inherits permissions, row and column controls, lineage, and audit. That is why two people can ask the identical question and correctly receive different answers based on what each is allowed to see, and why every interaction leaves an audit trail.
Domain experts set a Genie Agent up for a subject area by connecting the relevant datasets and adding sample queries and written instructions that describe the business. Genie uses the names and descriptions on your tables and columns to convert a question into SQL, then returns both the generated query and the results, so a curious user can see exactly how the answer was produced. When Genie cannot answer a question confidently, it asks a clarifying follow-up rather than guessing. Business users reach these capabilities through a simplified, read-only entry point, so they never touch the underlying workspace. See the AI/BI Genie general availability announcement and the Genie Agents concepts documentation.

Where Genie Ontology fits

Genie Ontology is in Public Preview. Availability and behavior may change, and access is currently gated.

Good answers depend on good context, and curating all of it by hand is a lot of work. Genie Ontology is a context layer that automatically builds and maintains a map of your data and business meaning from assets you already have, such as dashboards, notebooks, metric definitions, and past queries. That map lets Genie interpret a question using the definitions your teams already use, rather than starting from scratch each time. It is permission-aware, so it only draws on knowledge from assets a user is allowed to see, and it complements the context you define explicitly in Unity Catalog, including metric views and the business glossary. You can read more in Operationalizing Genie Ontology in your data stack.

FAQs

Do non-technical users need to know SQL?

No. They ask in plain language, and Genie generates the SQL, runs it against governed data, and returns a table, chart, or short explanation.

How does the AI avoid showing data someone shouldn't see?

Genie runs read-only queries through Unity Catalog, so it respects the same permissions, row filters, and column masks that protect the underlying tables. Answers are scoped to each user.

What is Genie Ontology, and is it generally available?

Genie Ontology is a context layer that automatically maps your data and business meaning so Genie can interpret questions accurately. It is in Public Preview, not generally available, and access is gated.

What happens when a question is unclear?

Genie asks a clarifying follow-up instead of guessing. It can also be taught new definitions so it interprets your specific business terms correctly.

Can users see how an answer was produced?

Yes. Genie can return the query it generated alongside the result, so users and analysts can check the logic behind an answer.