Product Thinking & Problem Framing for AI

This course contains the use of artificial intelligence journey beginner to pro.

Duration: 5 Months · 21 Weeks · 105 Teaching Days
Audience: AI Product Owners, PMs, Business Leaders
Outcome:  Consistently identify high-value, feasible, responsible AI problems—and avoid costly AI mistakes.

Product Thinking & Problem Framing for AI is a comprehensive 5-month course designed for AI Product Owners, Product Managers, Business Leaders, and emerging AI strategy professionals who want to identify the right problems for AI before investing in solutions. Across 105 teaching days, students learn how to move beyond hype, vague ideas, and solution-first thinking to frame high-value, feasible, and responsible AI opportunities.

This course begins with the foundations of product thinking, including outcomes over outputs, customer value vs business value, Jobs-To-Be-Done, and writing strong problem statements. Students then learn when AI is the wrong tool, how to avoid unnecessary complexity, and how to recognize situations where simple rules, process redesign, or human judgment outperform AI.

As the course progresses, learners deconstruct problems into AI-sized components, evaluate prediction, generation, and decision-making use cases, and analyze workflows through signals, inputs, outputs, and actions. They explore critical product dimensions such as data readiness, risk, harm mapping, explainability, error tolerance, human-in-the-loop design, and go/no-go decision frameworks.

A major focus of the course is helping leaders validate AI ideas before building models. Students practice problem discovery, assumption testing, non-AI prototypes, Wizard-of-Oz experiments, manual-first validation, and MVPs without models. They also learn how to define success criteria, distinguish learning metrics vs business metrics, maintain decision logs, and gracefully kill weak AI ideas.

The course also covers modern AI-specific framing for Generative AI, LLMs, and Agentic AI systems. Students learn when to use GenAI, when not to use LLMs, how to think about context windows, hallucination tolerance, grounded vs open-ended problems, trust calibration, agent autonomy, feedback loops, and accountability mapping.

By the end, students apply everything through real-world framing studios for consumer AI products, enterprise workflows, internal tools, customer-facing AI, regulated industries, and high-risk domains. They complete an end-to-end capstone, defend their framing through peer critique, conduct a risk and ethics review, make a final go/no-go decision, and build their own Product Leader Problem-Framing Playbook.

This course is ideal for leaders who want to reduce AI waste, avoid costly mistakes, challenge bad ideas confidently, and lead AI initiatives with judgment, clarity, and business impact—not hype.