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.
Applied Learning Project
You’ll complete scenario-based activities that mirror real AI project leadership. Learners will evaluate emerging technologies, apply foresight methods, and translate business goals into AI-ready problem statements with clear objectives and success metrics. The program also covers project scoping, data readiness, risk identification, and defining project requirements within time, budget, and compliance constraints. Using agile and CRISP-DM frameworks, learners will plan and manage AI project delivery while preparing implementation strategies and communicating recommendations to stakeholders.
