Full course description
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AI is transforming how organizations make decisions, serve customers, and compete — but knowing what AI can do is not the same as knowing when and how to use it well. This course helps leaders develop the strategic judgment needed to evaluate where data, analytics, and AI create real value, where they fall short, and why the difference matters.
Rather than teaching you to build models or select tools, this course focuses on the strategic decisions AI is meant to support. You will examine how AI reshapes workflows and organizational capabilities and explore topics such as prediction versus judgment, human-machine collaboration, data as a competitive asset, and the limits and risks of analytics-driven initiatives. Through cases and applied frameworks, you will assess when decisions should be automated, augmented, or left to human expertise — and how data availability, bias, explainability, and scale affect the strategic choices you face.
This course develops the diagnostic and evaluative thinking leaders need to engage responsibly with AI-related decisions. By the end, you will be able to:
- Diagnose where data, analytics, and AI can (and cannot) create competitive advantage within an organizational context
- Design a high-level, practical job aid for AI-supported decision making
- Evaluate the strategic, organizational, and ethical trade-offs involved in scaling analytics and AI initiatives
- Reflect on and articulate your role in shaping how analytics, AI, and machine learning are used within your organization
- Demonstrate a commitment to building AI and digital capabilities responsibly, valuing the short-term and long-term consequences of substituting computation for human expertise
- Develop a personal approach for continuously evaluating, learning about, and adapting to evolving AI and digital capabilities.