AI is transforming industries at an unprecedented pace, creating new opportunities for startups and established businesses alike. Yet, while AI’s potential is widely discussed, far less attention is given to how to actually build and scale AI products and companies. This course fills that gap.
Designed for future founders, product managers and investors, the course aims to provide students with a deep and practical understanding of what it takes to create and grow an AI-driven product.
The course will teach students to think about AI like a strategist and move in the AI space like a builder. For that purpose it is organized around three core questions: where should you use AI? How do you build successful AI products? How can companies create and sustain competitive advantage with AI?
The course will focus on the following learning goals:
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Diagnose whether a problem is well suited to an AI solution and craft a coherent AI product strategy.
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Design AI product architectures and experiences that are technically feasible and effective from a human-AI collaboration perspective.
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Understand how AI can generate competitive advantage and change industry dynamics.
Students will integrate these learnings by building an MVP to test the most important technical, behavioral and economic uncertainties in their product.
To help students achieve these learning goals, the course will blend relevant research, real-world case studies, and hands-on application. We will review foundational insights from economics, behavioral science, and computer science that are shaping the frameworks AI practitioners use when making practical decisions. We will have case discussions, analyzing how AI companies navigate critical decisions and trade-offs. Finally, students will apply these concepts and techniques through practical assignments, progressively designing an AI product by the end of the course.
The first half of the course will be primarily focused on understanding the relevant frameworks to make decisions on AI products and illustrate them through case discussions, while the second half will be more focused on the practical aspects of building an AI product through hands-on application in class.
This is not a course on machine learning or generative AI—it’s a course on building AI products and businesses, not models or systems.
The course will cover some technical fundamentals as they relate to building and evaluating AI products, but it is not designed to be an introduction to AI or machine learning. Students will benefit from prior exposure to concepts such as model training and evaluation, different model architectures, performance metrics, etc. Those without this background can still succeed, but should expect to spend additional time learning foundational concepts. Recommended readings and resources will be provided for those who need to catch up.
You should NOT take this class if:
- Your main interest is to learn how to build AI models and systems. There are other technical classes you can take, not this one.
- You want a lab class that will teach you to use different AI tools for product managers and founders. You don’t need a full term class to learn those tools, they are easy to learn on your own. While this class may provide a good opportunity to use some of those tools as part of your group project, we will not spend time teaching them.
- You are not interested in frameworks and cases, you just want to build something. There are other pure lab courses you can take for that. This course takes the view that you can’t build great AI products without a rigorous decision-making framework, so a good part of the course is dedicated to teaching it.
