Generative AI reached the boardroom a while ago. Most companies now have a slide about it, a handful of pilots, and no agreed answer to the obvious follow-up question: which of these is worth funding properly next year, and who signs for it once it touches a live system? This course is written for the people who have to answer that. You will take one business process from your own organization, whichever one is genuinely painful, from vague opportunity to funded plan with controls attached.
Module 1 is about choosing. You will assess where your organization actually sits on the Agentic AI Adoption Maturity Model, and use the Microsoft Cloud Adoption Framework to check whether the foundations exist before anything gets built on them. You will also cost a workflow properly, which most business cases never do. If a supplier intake process handles 10,000 requests a month for $18,000 all in, and 9,000 come out at the quality you need, you are paying $2.00 for every usable result. Compare that with the $8.00 of staff time each one saves and you have a figure that survives a finance review.
Module 2 moves to design. Adding a model to an unchanged process usually produces a faster version of the same bottleneck, so the work here is redesigning the process around a mix of human and machine steps. You will define what an agent may do without asking, where it has to stop, and what information travels with a case when it escalates to a person. Exception handling gets sustained attention, because it is where these systems fail once they are live and the first thing security and compliance will ask you about.
Module 3 covers governance, starting from a problem that is easy to miss: a deployment that passed review in January can behave quite differently by September without anybody deciding to change it. Prompts get edited, models get updated, the data grows categories of record the original design never anticipated. The course calls this trust drift. You will write policy that is enforced by configuration rather than goodwill, working with Azure Policy, Microsoft Purview, and agent identity in Microsoft Entra, then set up the evaluation and monitoring that would have caught the drift in month one. The module closes with a full impact assessment for a high-risk autonomous deployment.
Module 4 is about the organization. A central AI team is usually right early on, when expertise is scarce and mistakes are expensive, and usually wrong later, once it becomes the queue everyone waits in. You will plan that transition deliberately: what the Center of Excellence keeps, what moves out to platform and product teams, and roughly when. The rest is adoption, where these programs tend to stall in practice. Communication, role-based training for people whose job is now supervising an agent rather than doing the task, local champions, and a few adoption and value measures you can report without inventing them.
Each module produces one section of a single document: a use case feasibility matrix, a human-agent process map, a governance policy with its impact assessment, and a change roadmap with the CoE operating model. By the end you have an Enterprise AI Implementation Blueprint for a real process in your own organization, with a recommendation at the front, proceed, revise, delay, or stop, and ninety days of plan behind it.
