# From Financial Data to AI-Ready Decisions: Power BI, Microsoft Fabric, Semantic Models & AI Agents with Rishi Sapra [MVP]

Page: https://stenobird.com/podcast/m365-fm-modern-work-security-and-productivity-with-microsoft-365-7311214/from-financial-data-to-ai-ready-decisions-power-bi-microsoft-fabric-semantic-models-ai-agents-with-rishi-sapra-mvp
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Podcast: [M365.FM a Microsoft MVP Podcast by Mirko Peters](https://stenobird.com/podcast/m365-fm-modern-work-security-and-productivity-with-microsoft-365-7311214)
Published: 2026-09-16T17:53:27+00:00
Episode link: https://www.spreaker.com/episode/from-financial-data-to-ai-ready-decisions-power-bi-microsoft-fabric-semantic-models-ai-agents-with-rishi-sapra-mvp--75172317
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Duration seconds: 3981

## Resource

What happens when AI agents start making sense of financial and business data — not just displaying it?In this episode of M365.FM, Mirko Peters talks with Rishi Sapra Microsoft MVP about the architecture required to move beyond traditional dashboards and toward AI-powered, context-aware analytics with Microsoft Fabric, Power BI, Copilot Studio, semantic models, ontologies, and data agents.Organizations already have enormous amounts of information spread across ERP systems, Excel workbooks, Power BI reports, Microsoft Fabric, SharePoint, financial systems, budgets, forecasts, and operational applications.Adding AI on top of that data does not automatically mean the AI understands the business.What does “revenue” actually mean? Which definition of margin should an AI agent use? Which KPIs represent the official version of the truth? And how does an agent understand relationships between customers, products, regions, cost centers, contracts, and business processes?The answer increasingly lies in the context layer between raw data and AI. FROM SELF-SERVICE BI TO SELF-SERVICE AI Rishi explains his journey from financial modeling and Excel through Power Query and the early days of Power BI to today's Microsoft Fabric and AI ecosystem.Power BI helped bring business intelligence out of centralized IT departments and into the hands of business users.But the platform has also become significantly more sophisticated.Semantic models, DAX, lakehouses, OneLake, Direct Lake, Copilot Studio, data agents, ontologies, MCP-based tools, governance, and AI now create an architecture that can quickly become difficult for individual business users to understand.AI agents could change that relationship.Instead of requiring every business user to become a data engineer, BI developer, and AI en…

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