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Product Thinking & Problem Framing for AI
Product Thinking & Problem Framing for AI
Published 5/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 10h 32m | Size: 6.8 GB
Master AI problem framing, validation, risk, and product judgment before building costly AI solutions.
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 identifyhigh-value, feasible, responsible AI problems —and avoid costly AI mistakes.
Product Thinking & Problem Framing for AI is a comprehensive 5-month course designed forAI Product Owners,Product Managers,Business Leaders, and emergingAI strategy professionals who want to identify the right problems for AI before investing in solutions. Across105 teaching days, students learn how to move beyond hype, vague ideas, and solution-first thinking to framehigh-value,feasible, andresponsible AI opportunities.
This course begins with the foundations ofproduct thinking, includingoutcomes over outputs,customer value vs business value,Jobs-To-Be-Done, and writing strongproblem statements. Students then learn whenAI is the wrong tool, how to avoid unnecessary complexity, and how to recognize situations wheresimple rules,process redesign, orhuman judgment outperform AI.
As the course progresses, learners deconstruct problems intoAI-sized components, evaluateprediction,generation, anddecision-making use cases, and analyze workflows throughsignals, inputs, outputs, and actions. They explore critical product dimensions such asdata readiness,risk,harm mapping,explainability,error tolerance,human-in-the-loop design, andgo/no-go decision frameworks.
A major focus of the course is helping leaders validate AI ideas before building models. Students practiceproblem discovery,assumption testing,non-AI prototypes,Wizard-of-Oz experiments,manual-first validation, andMVPs without models. They also learn how to definesuccess criteria, distinguishlearning metrics vs business metrics, maintaindecision logs, and gracefully kill weak AI ideas.
The course also covers modern AI-specific framing forGenerative AI,LLMs, andAgentic AI systems. Students learn when to useGenAI, when not to useLLMs, how to think aboutcontext windows,hallucination tolerance,grounded vs open-ended problems,trust calibration,agent autonomy,feedback loops, andaccountability mapping.
By the end, students apply everything through real-world framing studios forconsumer AI products,enterprise workflows,internal tools,customer-facing AI,regulated industries, andhigh-risk domains. They complete anend-to-end capstone, defend their framing through peer critique, conduct arisk and ethics review, make a finalgo/no-go decision, and build their ownProduct Leader Problem-Framing Playbook.
This course is ideal for leaders who want to reduceAI waste, avoid costly mistakes, challenge bad ideas confidently, and lead AI initiatives withjudgment,clarity, andbusiness 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
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