Power BI + AI: Are we moving beyond dashboards toward conversational analytics?
I’ve been experimenting with combining AI tools (specifically Claude) with Power BI semantic models, and it’s starting to feel like a meaningful shift in how analytics might be consumed.
Traditionally, the workflow looks something like this:
- Build reports and dashboards
- Add visuals to answer anticipated questions
- Users click, filter, and explore
That model works well, but it’s always limited by what was pre-built into the report.
What’s interesting now is the ability to connect an LLM directly to a semantic model and let users ask questions in natural language. Instead of navigating visuals, they can just ask things like:
- Why did June outperform March?
- What are the main drivers behind this trend?
- How do two entities compare and what should we do about it?
The responses aren’t just filtered views of existing visuals either. The model can:
- Pull in dimensions not surfaced in the report
- Provide summaries across multiple metrics
- Offer explanations and even recommendations
It starts to feel less like “report consumption” and more like interacting with the data itself.
A few things that stood out to me while testing this:
- The semantic model becomes way more important than the report layer
- You can’t realistically pre-build all the insights users might want
- AI fills the gap between high-level dashboards and deep analysis
- There’s real potential for scaling analytics to less technical users
That said, there are still some open questions:
- How do you manage trust and validation of AI-generated insights?
- What does governance look like when users can query anything?
- Does this reduce the need for complex report design, or just shift it?
- How do organizations handle access and security at scale?
Curious how others are thinking about this.
Are you exploring AI + Power BI integrations yet?
Do you see this replacing parts of the dashboard experience, or just augmenting it?