Microsoft Community Insights Podcast

1) How can my business migrate to AI & ERP?
2) How can we save time and money with this migration?
3) How can my business become stronger on AI with S/4?
As such, these firms have the great fortune of helping SAP-run companies achieve "Faster, stronger, more stable company" status.
✔️ Scale your SAP landscape while reducing complex code and staff
✔️ Generate a S/4 upgrade scenario with a team of experts
✔️ Increase productivity while reducing cost and complexity

https://demystify-ai-book.lovable.app/

https://ai-lead-link.lovable.app/

amazon.com/author/christophermcarter 

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We talk with Tiago Costa about what “secure by design” looks like on Azure when teams want AI fast but lack the basics like data governance, landing zones, and repeatable deployment. We share lessons on cost shocks from token-based billing, why click ops undermines security, and how to guide teams towards safer habits without slowing delivery.    

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Jordan shares the thinking behind “Easy AI”: add agents and agentic workflows to what you already run, even when the people operating the system don’t know the codebase inside out. We talk through the new Azure portal experience for enabling MCP, using  App Service managed instance 

See here for information 

https://www.youtube.com/watch?v=QRKgQCQvSjk

https://devblogs.microsoft.com/microsoft365dev/mcp-apps-now-available-in-copilot-chat/

https://techcommunity.microsoft.com/blog/appsonazureblog/whats-new-in-azure-app-service-at-msbuild-2026/4526569

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If you are letting people query Microsoft Fabric with T-SQL, you are already making a security decision, whether you meant to or not. We sit down with data platform MVP John Minor to unpack what it really takes to secure the Fabric SQL analytic endpoint, from the basic shape of the Fabric ecosystem to the small settings that can derail a rollout.

We start with the fundamentals: which Fabric objects actually expose a SQL analytic endpoint (lakehouse, warehouse, mirrored database, SQL database), what “read-only endpoint” means in practice, and why the SQL database has both OLTP and OLAP representations. John explains the purpose of the SQL analytic endpoint, what you can build with T-SQL (views, functions, object-level controls), and why case sensitivity is a gotcha worth fixing before you deploy anything widely. We also cover connectivity, including the TDS protocol and how SSMS fits into real-world management and testing.

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Deployment is where motivation goes to die: the app works locally, the deadline is close, and suddenly you’re buried in portals, docs, and mysterious errors. We sit down with Microsoft MVP Mohammed Sami to talk about a more practical approach: using GitHub Copilot in VS Code to keep your focus, ask better questions, and move from “it runs on my machine” to “it’s live” with less stress.

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You will learn how small knowledge graph, implement a GraphRAG workflow, and connect it to an AI agent powered by the Microsoft Agent Framework and Microsoft Foundry. You will learn how the agent traverses a graph database, interprets relationships, and answers queries over connected data.

https://www.linkedin.com/in/christian-glessner/

https://ilovemesh.com

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Tech does not just need more women, it needs more women who feel free to show up as themselves. We sit down with Samantha St Louis, VP of AI App Innovation and a Microsoft MVP, to talk about what it really looks like to build an AI career, lead AI strategy, and navigate a male-dominated industry without shrinking your personality to fit the room.

Her career journey is the proof point. She starts as an  nurse then moves into early health tech, builds her own company, and later levels up through Microsoft Learn and certifications like Power Platform Fundamentals before going deeper into AI engineering and AI architecture. Along the way, she shares candid stories about bias, the quiet gatekeeping that keeps people out, and why soft skills are a real competitive advantage in modern tech.

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Choosing the right AI model shouldn’t feel like roulette. We sit down with Kate Catlin, the product manager from GitHub. Kate shares how model strengths and explains how auto mode aims to pick the right model for the job so developers can focus on outcomes.

We dig into practical tactics that cut through hype: start with a golden dataset, run evaluations early, and keep refining with real user prompts once your AI is live. If you’re overwhelmed by weekly model releases, you’re not alone—Kate outlines how to compare new options  with  scoring and selective manual review. 

She also tackles the enterprise challenge: slow model approvals that leave teams on outdated systems. With a disciplined eval pipeline, organisations can safely adopt newer, faster, and often cheaper variants that deliver better results.

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Build an agent that understands intent, finds the right data, and takes action across your CRM—without wiring a maze of connectors. We sit down with Microsoft MVP and functional architect Nathan Rose to explore how Dataverse and Model Context Protocol in Copilot Studio turn instructions into outcomes, shrinking complexity and unlocking reliable, low‑code automation.

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We walk through a plan-first workflow that asks Copilot to survey the codebase, propose a step-by-step solution, and only then switch to agent mode to implement. That simple change lifts code quality, reduces rework, and keeps diffs aligned with architecture. We also dig into custom agents with scoped permissions and roles: a testing agent for unit coverage, a docs agent for READMEs, a refactor agent limited to certain directories. Prompt craft matters, but stable configuration, coding standards, and CI guardrails matter more. Think of it as turning best practices into reusable instructions that AI can follow every time.

Johan shares field notes from consulting and charity projects: where AI saves hours, where it still stumbles, and how code generation quality has improved over the past year. We explore SpecKit’s promise for scaffolding and simpler apps, acknowledge its preview status and quota costs, and outline how to adopt incrementally in legacy systems. Along the way, we cover open source tracking via changelogs and issues, integrating agents into CI/CD, and designing a workflow that is auditable, secure, and team-friendly.

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