AI is transforming industries at an unprecedented pace, creating new opportunities for startups and established businesses alike. It represents a fundamental shift in how businesses operate. Yet, while AI’s potential is widely discussed, far less attention is given to how to actually build and scale AI products and companies. AI products have different dynamics from traditional software -from the cold start problem and to ethical risks and usability constraints. Successfully navigating these challenges requires a different strategic approach and operational playbook. This course fills that gap.
Designed for future founders, executives 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 business. Students will learn about key principles and tradeoffs when developing effective product strategies, the ethical and technical considerations that have to go into AI business decisions, and lessons from early failures and successes of companies that have scaled.
The course is built around two core themes: achieving product-market fit in AI, and the challenges AI companies face when trying to build, scale and sustain competitive advantage.
It follows a structured approach that blends 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.
However, AI products are highly technical, so students are expected to have at least a basic understanding of how AI works under the hood. The course will not cover fundamentals like how to train models and measure their performance, students are expected to be at least familiar with these topics. If terms like AUC, recall, or embeddings sound completely foreign, students should be aware that the workload to be successful in the course will be much higher. While recommended readings and resources will be provided for those who need to catch up, these concepts will not be explained in class.
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.
