What's the best AI for collaborating with multiple teams?

Summary

• The best AI for multiple teams runs on shared, governed data with shared business context, so every team gets consistent answers scoped to its own permissions, instead of a separate chatbot per department that disagrees with the others.
• Three things separate a team-wide AI from a personal one: a shared semantic layer (so "revenue" or "active customer" means the same thing everywhere), central governance with per-user access, and self-service that non-technical people can actually use.
• Databricks AI/BI Genie answers natural-language data questions by generating governed SQL against the lakehouse, drawing on shared context: certified metrics and definitions, query history, dashboards, and the example questions and instructions teams add over time.
• Because Genie runs inside Unity Catalog, every user sees only the data they are allowed to see (row and column level), queries are read-only, and each interaction is logged for audit, so one deployment serves many teams safely.
• Genie One gives business users a single place to view dashboards, ask questions, and open apps, and multiple departments share the same Genie Agents without duplicating data or rebuilding governance.

What's the best AI for collaborating with multiple teams?

The best AI for multiple teams works on shared, governed data with a shared definition of the business, so every team gets consistent answers scoped to its own access. A personal chatbot that each department wires up on its own tends to drift: numbers disagree, terms mean different things, and no one trusts the result. A team-wide AI needs a common semantic layer, central governance that still respects each person's permissions, and an interface non-technical users can work without SQL. Databricks AI/BI Genie is built for exactly that.

What makes an AI good for many teams, not just one person?

Three capabilities separate a team-wide AI from a personal assistant:

• Shared business context. Everyone should get the same answer to "what was revenue last quarter." That requires certified metrics and definitions the AI reads from, not a private prompt each analyst tunes on their own.
• Central governance with per-user access. Many teams means many permission levels. The AI has to enforce who can see what at query time, not hand the same data to everyone.
• Real self-service. If only data engineers can use it, it is not a team tool. Business users need plain-language questions and trustworthy answers.

Key capabilities to evaluate

Capability	Why it matters
Shared semantic layer	Certified metrics and business definitions keep answers consistent across teams, so "active customer" means one thing everywhere.
Per-user governance	Row- and column-level security scopes every answer to what the asker is allowed to see, from one deployment.
Grounding in real assets	Learning from query history, dashboards, and analyst questions makes answers reflect how the business actually works.
Natural-language self-service	Non-technical users get answers without SQL, which is what makes an AI usable across departments.
Auditability	Read-only queries and a log of every interaction give governance teams a full trail.
One source, many surfaces	The same governed data and definitions should power dashboards, chat, and apps without rebuilding controls for each.

How Databricks approaches team-wide AI

Databricks AI/BI Genie answers data questions in plain language by translating them into governed SQL that runs against your lakehouse. Its accuracy comes from shared context: metadata and values in Unity Catalog, certified metrics and business definitions, query history and existing dashboards and notebooks, plus example questions, instructions, and feedback that teams add to improve answers over time. See how Genie works for business users.

• Governed by Unity Catalog. Genie runs inside your existing access policies. Row- and column-level security means each user sees only authorized data, queries are read-only, and every interaction is logged for audit. One deployment serves finance, sales, operations, and more, each scoped correctly.
• Curated per domain with Genie Agents. A data team sets up a Genie Agent for a subject area such as sales pipeline or supply chain, adds trusted tables, metrics, and instructions, and shares it with the teams that need it. Multiple departments reuse the same governed definitions instead of building their own.
• One entry point with Genie One. Business users get a single place to view AI/BI Dashboards, ask Genie questions, and open Databricks Apps, so a non-technical audience has one door into governed data.
• No separate copy per team. Because dashboards, chat, and apps sit on the same governed data and shared context, teams collaborate on one source of truth rather than reconciling exports.

Use cases

Cross-functional metrics review. Finance, sales, and operations ask the same Genie Agent about shared KPIs and get answers built from the same certified definitions.
Onboarding a new team. Grant access to an existing Genie Agent and the team is productive right away, with permissions scoped to what it should see.
Reducing the report backlog. Business users answer their own ad hoc questions in plain language, freeing the data team from one-off requests.
Executive review. Leaders ask about quarter-to-date performance across departments and get consistent numbers, because everyone draws on the same context.

FAQs

Is this the same as a chatbot each team sets up separately?

No. The point of a team-wide AI is shared context and central governance. Genie draws on certified metrics and definitions so teams get consistent answers, and Unity Catalog scopes each answer to the asker's permissions, which separate per-team bots cannot guarantee.

How does Genie keep each team's data private?

Genie runs inside Unity Catalog, so row- and column-level security apply at query time. A user only gets answers from data they are already allowed to see, and every query is read-only and logged.

Do business users need to know SQL?

No. Users ask questions in plain language and Genie generates the SQL, returning answers with the query it ran so results can be checked. Data teams curate the trusted tables and definitions behind the scenes.

How do multiple teams share the same setup?

A data team builds a Genie Agent with trusted tables, metrics, and instructions, then shares it. Many departments use the same agent and the same AI/BI Dashboards without duplicating data or rebuilding governance.