Overview
Practical AI Strategy and Azure Service Selection introduces the structured decision-making required before launching an AI initiative. AI projects often fail due to unclear problem framing or misaligned technology choices.
This course helps you build the judgment needed to assess when AI is appropriate and how to align solutions with business objectives. You’ll examine how to map business challenges to AI use cases and evaluate feasibility, risks, and expected value.
The course explores the Microsoft Azure AI ecosystem, including Microsoft Foundry, Azure OpenAI Service, and Azure Machine Learning, focusing on capabilities, constraints, and appropriate use-case alignment. By the end of this course, you’ll be able to assess AI opportunities with clarity, support informed service selection decisions, and establish a structured foundation for AI delivery within enterprise environments.
Syllabus
- Avoiding costly AI mistakes early
This module builds your ability to critically evaluate whether AI is appropriate for a given business situation before any commitment is made. You'll learn to distinguish between AI approaches at a conceptual level, recognize early warning signs that suggest AI may not be the right fit, and apply structured evaluation techniques that experienced project leaders use to avoid costly missteps. By the end of this module, you'll be able to assess AI opportunities with confidence and articulate your reasoning to stakeholders.
- Turning business needs into clear AI requirements
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.
- Defining what an AI project will and will not do
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.
- Microsoft Foundry Governance decisions and workspace design
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.
- Model deployment and performance optimization
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.
Taught by
Microsoft
Subjects
Programming
