AI Product Foundations: Planning Strategies for Data Scientists

The rise of generative AI has elevated product management from a niche competency to an essential discipline for data scientists. Those who can wield technical expertise with strategic execution will shape the future of AI-driven industries. In this course, Matthew Blasa distills the principles of AI product planning, offering structured frameworks and pragmatic strategies for those operating at the intersection of data science, product management, and leadership.

Learn to define AI products with precision, construct a mental framework for execution, and assess your career trajectory with clarity. The course also navigates the complexities of data governance, quality, and project delivery—elements that determine whether AI initiatives succeed or collapse under their own weight. By the end of this course, you’ll be equipped with the communication skills you need to command a room, translate data into strategic insight, and align AI initiatives with business imperatives.

Learning objectives
Explain the strategic importance of AI product development and identify key players, essential skills, and opportunities for creating AI products that provide competitive and profitable advantages for businesses.
Analyze the components of AI products, their lifecycle, and various types, while understanding the crucial role of data and user experience in developing successful AI solutions.
Apply top-down and bottom-up discovery methods to identify AI opportunities, break down business requirements, and create effective road maps for AI product success.
Evaluate and implement strategies for data curation, labeling, and model building to address the "first mile problem" in AI product development, ensuring data quality and relevance.
Assess the value of AI products using intrinsic and extrinsic valuation methods, develop effective monetization strategies, and measure ROI to maximize the business impact of AI solutions.