This module develops architectural judgment for two of the most consequential early decisions in an agentic AI engagement: which model or models to use, and whether to build, buy, or extend an agent solution. You will design model routers, evaluate SLM use cases, and apply structured decision frameworks to real enterprise scenarios.

This module develops the diagnostic skills needed at the start of an agentic AI engagement, identifying and prioritizing high-value agent use cases across enterprise workflow types before any data-readiness or platform decisions are made. You'll apply a value assessment framework to a realistic retail scenario and practice categorizing and prioritizing use cases the way a practicing architect would in an early-stage client conversation.

This module develops the conceptual and procedural foundation needed to evaluate data readiness, understanding the four core data readiness concepts, connecting them to personal experience through a reflection activity, and applying a four-step audit procedure to a realistic healthcare scenario.

This module develops the strategic planning and execution skills needed to structure an enterprise AI transformation using the Cloud Adoption Framework for Azure. Learners understand why unstructured AI adoption fails, map the four critical components to the correct CAF phases, and apply a four-step execution playbook to a realistic finance department scenario, building the architectural judgment needed to lead a controlled AI rollout from intake to production.

This module translates the CAF adoption framework into an actionable enterprise agent strategy. Learners understand why governance structure is an architectural decision, map the precise platform architecture and accountability matrix of a governed Microsoft AI ecosystem, and apply a four-phase implementation blueprint to a realistic HR department scenario, building the end-to-end design judgment needed to deploy and govern a multi-platform agent strategy in practice.

Learners step into the role of Principal AI Strategy Consultant and Lead Architect for Apex Premium Finance—a mid-sized wealth management and retail banking institution facing uncoordinated AI requests from three business units. Rather than producing a generic strategy document, learners apply every LC 1 skill to a realistic enterprise scenario with real stakes: preventing shadow AI chaos, classifying project risk, remediating data gaps, selecting the right platform for each use case, designing a phased adoption roadmap, and establishing a Center of Excellence accountability model. The result is a portfolio-ready Enterprise AI Strategy and Governance Playbook that mirrors the deliverables a practicing AI Solutions Architect produces in a client engagement.