This module develops your ability to translate vague business goals into well-defined AI requirements that teams can act on. You'll learn to structure problem statements around outcomes rather than solutions, define measurable success criteria, and surface constraints that affect feasibility. By the end of this module, you'll be able to guide stakeholder conversations from broad intent to actionable requirements, setting AI initiatives up for clarity and accountability from the start.

This module focuses on the critical transition from exploration to commitment in AI projects. You'll learn to recognize when a project has achieved sufficient clarity to proceed responsibly, how to facilitate go/no-go discussions that surface uncertainty rather than suppress it, and how to document decisions in ways that create accountability and enable future course correction. By the end of this module, you'll be able to assess project readiness, guide stakeholders through commitment decisions, and create defensible records of why projects were approved, paused, or stopped.

This module focuses on how managers reason about governance decisions before and during the use of Microsoft Foundry. The emphasis is on understanding what decisions need to be made around access, oversight, risk, and cost, not on performing technical configuration. Learners develop judgment around how governance requirements vary based on project context, team structure, and risk exposure, and how those decisions are reflected in workspace design at a conceptual level.

This module focuses on how managers reason about model deployment and early optimization decisions after an AI solution goes live. Rather than teaching how to deploy or tune models, the module emphasizes how teams evaluate what is running, interpret early performance and cost signals, and decide what actions, if any, should be taken next. You will develop judgment around post-deployment oversight, expectation management, and decision timing.