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What You'll Learn

Learn how to identify high-value AI problems worth solving in real business environments
Develop strong product thinking skills for AI products
workflows
and intelligent systems
Evaluate when AI should — and should not — be used for a problem or workflow
Break complex business challenges into AI-ready components and decision flows
Analyze risk
ethics
trust
explainability
and failure modes in AI systems
Validate AI opportunities using prototypes
experiments
and MVPs before building models
Design smarter Generative AI and Agentic AI workflows with appropriate guardrails and autonomy levels
Learn to frame AI initiatives for executives
boards
and cross-functional stakeholders
Build practical frameworks for go/no-go decisions
risk reviews
and AI governance
Create a reusable AI Product Problem-Framing Playbook for future leadership and product decisions

Requirements

No prior AI or machine learning experience is required
Basic understanding of business workflows or product thinking is helpful
Curiosity about AI products
Generative AI
and Agentic AI systems
Willingness to think critically about problems
workflows
and decision-making
Comfort using basic digital tools such as web browsers
documents
and presentations
Helpful for Product Managers
Business Leaders
Founders
Analysts
Engineers
and Consultants
No coding is required for most of the course concepts and exercises
Access to a computer and internet connection for exercises and assignments
Optional: Familiarity with tools like ChatGPT or AI assistants can enhance learning
Most importantly
bring an open mindset and readiness to challenge AI hype with structured thinking

Description

This course contains the use of artificial intelligence.

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.

Who this course is for:

Product Managers and Product Owners who want to lead successful AI initiatives with stronger decision-making and problem-framing skills
Business Leaders and Executives looking to evaluate AI opportunities strategically instead of following hype
AI Product Leaders who want to reduce costly AI mistakes
failed pilots
and weak automation projects
Founders and Startup Teams building AI-powered products
copilots
or agentic systems
Consultants and Digital Transformation Professionals helping organizations adopt AI responsibly and effectively
Engineers and Technical Professionals who want to improve their business thinking and product judgment around AI
Innovation Teams exploring Generative AI
LLMs
automation
and AI agents in enterprise environments
Operations and Workflow Leaders seeking practical ways to improve productivity and decision-making using AI
Professionals working in regulated industries such as healthcare
finance
insurance
and government
Anyone who wants to learn how to identify valuable AI opportunities
frame problems correctly
and lead AI projects with confidence