Enterprise AI Architecture Learning Path
Learn how data, identity, security, model choices, and operating processes fit together in an enterprise AI architecture.
What you will learn
Data foundations, semantic layers, identity, security, integration, observability, governance, and ROI measurement.
Architecture, security, and governance
Define outcomes before pipelines. Create trustworthy data and access foundations before adding agents, and measure value as a product capability.
🎧 Recommended podcast episodes
- Building Trusted Enterprise AI with Microsoft AI Foundry — an enterprise architecture perspective.
- Microsoft Graph Connectors — Simply Explained — understand how enterprise data can support AI experiences.
Frequently asked question
What is the best next step?
Start with the recommended episode, document the business problem you are solving, and use the parent hub to choose the next adjacent learning path.
Continue learning
Return to the Microsoft AI and Agents Learning Hub, then continue through the M365.fm Learning Hub.
Last reviewed: July 2026.
Enterprise AI architecture learning objectives
Learn how data, identity, integration, governance, and operating boundaries support AI that can scale beyond a prototype.
FAQ
Why do AI pilots fail to scale?
They often lack the data foundations, governance, operating ownership, and integration architecture needed outside a controlled pilot.
Continue learning: return to the Microsoft AI and Agents Learning Hub and choose the adjacent module that matches your role.
Last reviewed: July 2026.