# Asana AI Teammates + Databricks Genie One Integration | Asana > Asana AI Teammates now integrates with Databricks Genie One, letting teams query governed data in natural language and turn insights into project updates and tasks. Source: https://asana.com/inside-asana/asana-ai-teammates-databricks-genie-one ## Introducing the Asana AI Teammates integration with Databricks Genie One Companies have invested heavily in bringing their most important data together. But for many employees, getting an answer still requires specialized tools, technical expertise, and help from an analyst.  And getting the answer is only part of the job: teams still have to update plans and reports, create follow-up work, and assign it to the right people. With the new integration between [Asana AI Teammates](https://asana.com/product/ai/ai-teammates) and [Databricks Genie One](https://www.databricks.com/product/genie/one), teams can ask natural-language questions about governed data in their Databricks environments without ever writing code. AI Teammates can then use the answers to update projects, prepare reports, and create follow-up work in Asana. The integration connects Asana and Databricks through Model Context Protocol (MCP), while existing Databricks permissions remain enforced through Unity Catalog. By bringing Genie One's data intelligence into Asana's AI-powered workflows, organizations can turn trusted enterprise data into actionable insights and automate work across teams. We built Genie One to make it simple for everyone to use data to do their jobs. Connecting Genie with Asana AI Teammates will make it effortless for teams to make decisions using data and act on them all within the same flow - all with the governance and security of the Databricks Data + AI Platform." ### **Turn analysis into follow-up work** A marketer preparing a campaign retrospective may already manage the campaign brief, creative work, owners, and deadlines in Asana. But the performance data needed to evaluate the campaign lives in Databricks. Comparing marketing-qualified leads and conversion rates by region can still mean submitting a request to an analyst, waiting for the answer, and manually copying the findings into the retrospective. With the new integration, the marketer could ask the prebuilt Insights Analyst AI Teammate: _Compare MQLs and conversion rates by region for Campaign X with the previous campaign. Update the retrospective with the findings, and create a follow-up subtask for each region where conversion declined._ The Insights Analyst sends the data question to[Databricks Genie One through its managed MCP server](https://docs.databricks.com/aws/en/agents/mcp/genie-mcp). Genie One uses the organization’s governed data, business context, and business definitions to interpret the question and return an answer grounded in current company data, helping the AI Teammate produce more accurate, context-aware insights.  The Insights Analyst then updates the retrospective and creates the requested subtasks with the relevant findings for each region. The team can review the analysis, assign owners, and decide what to change before the next campaign. ### **Use with prebuilt AI Teammates across the business** Beyond marketing, teams in operations, IT, revenue, and more can use Genie One with prebuilt AI Teammates designed for their specific industries and roles. For example: - An **Operations Leader** could ask the **Program Ops Manager** to compare actual demand with forecasts by region, flag where demand exceeds available capacity, and create follow-up tasks to review resourcing plans. - An **IT Leader** could ask the**IT Request Agent** to identify service categories with rising resolution times, summarize recurring issues, and create investigation tasks for the team to address the causes. - A **Revenue Leader** could ask the **Executive Reporter**to analyze pipeline and net retention trends by product and geography, draft a QBR with the findings, and create follow-up tasks for areas that need attention. The resulting reports, project updates, and tasks stay in Asana, where teams can review them, assign owners, and track progress. ### **Preserve existing data access controls** Every user authorizes the connection using their own OAuth grant, and credentials are not shared between users.[Unity Catalog permissions](https://docs.databricks.com/gcp/en/agents/mcp/managed-mcp) are enforced on every request, so an AI Teammate can only access data that the person assigning the question is already allowed to see.  Answers are also traceable. Genie One can provide links to the relevant Databricks sources, helping users understand where an answer came from and review the supporting data when needed. This brings the governed data and business context in the Databricks Data + AI Platform into Asana workflows without changing the controls that determine who can access it. ### **Access Asana work from Databricks, too** In addition to the new Genie One integration, the[Asana MCP Server is now available in Databricks Marketplace](https://marketplace.databricks.com/details/a685f81f-3afc-4f22-aa99-5386b0d12ab5/Asana_Asana-MCP), enabling Databricks users and agents to access and act on work in Asana directly from Databricks.  To get started, follow our [setup guide](https://help.asana.com/s/article/connect-databricks-custom-apps-to-asana-ai-teammates?language=en_US) to connect Genie One to Asana AI Teammates. - [Introducing AI Meetings and Tables: Every meeting moves work forward, every Table keeps it current](/inside-asana/introducing-ai-meetings-and-tables) Artificial Intelligence (AI) #### Content writer Campaign kickoffs and program reviews are where teams make their most important decisions. 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