Forum Discussion
Building AI-Powered Features Around Power BI Embedded Analytics
Hi everyone,
I am exploring ways developers are extending Power BI solutions with AI-powered capabilities.
Many applications today need more than dashboards — they need intelligent features that can help users understand data, generate insights, and automate decision-making processes.
I am interested in understanding how developers are approaching this architecture.
Some questions:
- How are you integrating AI capabilities with Power BI Embedded applications?
- Are you using Power BI APIs together with external AI services for generating insights?
- What are the recommended approaches for allowing AI systems to query and understand Power BI datasets securely?
- How do you handle permissions and data access when adding AI features on top of business intelligence platforms?
Would love to hear about real-world implementations and best practices from developers working with Power BI integrations.
Thanks!
done
4 Replies
v-kathullacCommunity Support
Hi codeautomation ,
As we haven’t heard back from you, we wanted to kindly follow up to check if the solution provided for the issue worked? or Let us know if you need any further assistance?
Regards,
Chaithanyadone
- Natarajan_M
Super User
Hi codeautomation ,
Just sharing from my own experience. If you're planning to build an application that combines different features an embedded Power BI report, AI agents, and so on there are two paths worth looking at.
Option 1 (available now / GA): The Fabric Workload option is already generally available and supports the full set of features you'd want, including building a custom web app around your embedded reports.
Option 2 (GA expected end of Oct / Nov): A newer approach for building a fully customized app on top of your semantic model — where you can embed reports, add a Fabric Data Agent, bring in an agent from Databricks Genie, and wire up custom workflows, UDFs, and similar extensions.
If you can share a bit more about what your app needs to do, I'm happy to point you toward whichever of the two fits best.
Thanks
Natarajan Manivasagan
AI-assisted drafting: AI was used to help structure and phrase this response. I reviewed and validated the technical content before posting.For more Power BI tips and discussions, let's connect on LinkedIn.
- tharunkumarRTK
Below are the few usecases where I implemented AI capabilities in report embed applications
- Embed for Organization scenarios (User owns the data): The easiest route is to use the native power bi copilot option.
Now available: two new Copilot experiences | Microsoft Fabric Community - Embed for Customers outside the organization: You would need to train your LLM about your semantic model schema and its knowledge, to retrieve this information from your semantic model either you can use
- Native Power BI Consumption MCP server: https://learn.microsoft.com/en-us/power-bi/developer/mcp/remote-mcp-server-get-started
- You can build a Fabric data agent on top of an existing semantic model and use its MCP endpoint, to integrate it with your LLM:
https://learn.microsoft.com/en-us/fabric/data-science/data-agent-mcp-server - You can build a custom solution to retrieve the metadata from your model and report. Feed it into your LLM.
One more thing to mention here for those who does not want to build all of these applications from scratch, I have seen few tools called Reporting Hub which can do report embedding and can enable AI capabilities along with it. Do note its a paid service.
Hope this helps.
checkout my blogs here: https://www.techietips.co.in/
- Embed for Organization scenarios (User owns the data): The easiest route is to use the native power bi copilot option.
