This course focuses on the latest developments in AI/ML as applied to product development. The curriculum will prepare students for current, relevant, and industry-applicable topics.
Identify AI product opportunities: Assess needs, constraints, and value in AI integration
Select and apply AI models/systems: Choose models, source data, and design prompts
Develop strategic AI roadmaps: Build roadmaps balancing short-term and scalable goals
Manage AI-specific constraints: Address data, privacy, and feasibility challenges in AI products
Lead AI-focused teams: Guide cross-functional teams and communicate with stakeholders
Anticipate AI trends: Prepare for impacts on security, scalability, and ethics
About this course:
AI for Product Management is designed to equip professionals in managerial and leadership roles in technology, whether product managers, CTOs, and strategists, with the essential tools and strategies for effectively integrating AI technologies into the product lifecycle. This course provides a deep dive into the practical and strategic aspects of building and managing AI-driven products, including model selection, data sourcing, prompt design, performance evaluation, and more.
Students will learn how to select AI solutions by identifying key opportunities for AI application, analyzing customer needs, and understanding technical constraints. The course focuses on how to build a product roadmap around AI-based capabilities that balances immediate results with long-term scalability. Through practical examples, such as developing conversational interfaces, students will strategize and iterate over several weeks, focusing on the nuances of iterative development with this technology.
Students will define and present an AI-based product, using a comprehensive set of knowledge that includes understanding constraints such as data quality, privacy concerns, and technical feasibility, providing practical frameworks to manage these challenges in real-world business contexts.
Additionally, the course covers team management, including how to form cross-functional AI teams, training non-technical stakeholders, and management of ongoing product development. By the end of the course, students will have a comprehensive understanding of the entire product lifecycle — from concept to launch — enabling them to successfully lead AI-driven products to market. Students will also gain insights into emerging AI trends, with a focus on rapidly evolving security, infrastructure, and scalability challenges.
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