AI Project Leadership: Definition to Delivery
About this course
In this Specialization, you’ll learn how to lead AI projects from early definition to organizational delivery. You’ll start by building a clear approach to disruption and emerging tech, using structured foresight and probabilistic reasoning to think through uncertainty and communicate innovation choices. Next, you’ll turn business goals into AI project clarity. You’ll frame problems using SMART objectives, connect outcomes to measurable success metrics (including precision and recall when relevant), check data readiness, estimate labeling needs, and surface early risks like imbalance, poor quality, or limited resources. You’ll then scope AI initiatives so they stay finishable and valuable by defining requirements, writing scope statements, and building a WBS that reflects time, budget, compliance, and trade-offs. Finally, you’ll plan, track, and deliver using practical delivery methods (including agile and CRISP-DM), and guide implementation with QA, acceptance testing, and stakeholder communication that keeps teams aligned.
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- 35/35
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What you'll learn
- transform business goals into clear AI project definitions
- frame problems using SMART objectives
- connect outcomes to measurable success metrics
- identify and assess early risks in AI projects
- scope AI initiatives considering time, budget, and compliance
- apply delivery methods like agile and CRISP-DM
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