Product Thinking & Problem Framing for AIPublished 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.
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
RequirementsNo 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
DescriptionThis 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 forProduct 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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