Foundations of AI Product Thinking

This unit introduces core distinctions between AI and machine learning, the data science workflow (training, validation, testing), and the complementary responsibilities of product teams and engineering in shaping AI experiences and foundations. Students will practice uncovering product-focused AI use cases and connecting model performance metrics to measurable business impact to prepare for hands-on development in subsequent units.

Learning Objectives

• Analyze differences between AI and machine learning and classify common algorithms (e.g., supervised, unsupervised, reinforcement) for a given product scenario
• Apply the training, validation, and testing workflow to design a reproducible data-splitting plan and justify chosen proportions for a provided dataset and use case
• Evaluate roles and responsibilities that shape the AI user experience and the AI foundation by proposing at least three UX safeguards or governance controls for a sample product
• Demonstrate the ability to identify and prioritize at least three AI use cases within a product and map relevant model performance metrics to business impact by selecting appropriate KPIs for one prioritized use case