AI Product Innovation and Design

Duration: 6 weeks

Modules: 00 AI fundamentals for product decision-makers Build a clear, practical understanding of what modern AI can and cannot do. This module cuts through hype to help you assess capabilities, limitations, and real-world applications with confidence. Topics include:

• The foundations of AI in machine learning

• How large language models (LLMs) are developed and function through tokenization

• Training and deploying LLMs

• Real-world applications across products and industries

01 Finding your AI edge: What AI will (and won’t) do for your product and strategy Apply your understanding of AI to real product and strategic decisions. Learn how to evaluate where AI can be used reliably, where it introduces risk, and how to make informed choices about its role in your product. This module introduces the sharp-edged vs. rough-edged framework to help leaders assess error tolerance, trust dynamics, and investment readiness. Learners explore how to reframe high stakes problems into safer, more adaptable workflows and forecast how AI capabilities may evolve over time. Topics include:

• Understanding the challenge of predicting AI progress

• Defining sharp-edged and rough-edged problems

• Understanding why AI performs differently on each type

• Using the framework to predict what comes next

• Turning sharp problems into rough ones

02 Replacing vs. augmenting people with AI Examine why many AI products succeed or fail based on how well they augment human capability. This module introduces intelligence augmentation as a guiding principle using real-world examples to understand where AI enhances capabilities, and where it undermines trust, usability, or decision-making. Topics include:

• People: where AI lives or dies

• Intelligence augmentation

• Achieving intelligence augmentation

03 Creating differentiated AI products Analyze why users default to general-purpose AI tools even when specialized products exist. Learn how habit, friction, and channel factors shape adoption, and identify what drives users to switch or stay. Develop strategies to shape AI products that deliver differentiated value and are consistently chosen in real world use. Topics include:

• Why so many AI products fail

• Lessons from past technologies

• The ChatGPT gravity well

• The psychology behind the pull

• How to escape the gravity well

• Designing AI products users will adopt

04 Responsible and ethical AI product development Explore how ethical and societal considerations influence the success and integrity of AI products. This module introduces structured techniques, such as Ethics and Societal Review processes, to identify risks early, articulate mitigation principles, and embed responsible design decisions into product development. Topics include:

• Why do AI products need to be concerned with ethical issues? (Why not “I’m just an engineer?”)

• Two techniques for identifying ethical issues: Tarot Cards of Tech and Black Mirror Writers Room

• Ethics and societal review as a structured process for early-stage projects

05 Chatbot interaction: How to get it right, and what goes wrong Investigate people's psychological responses to AI systems as social actors. Learn key principles behind chatbot design, including the Media Equation, Uncanny Valley, and Replicant Effect, and examine how design choices influence trust, transparency, and user perception. Topics include:

• The AI chatbot rogues' gallery

• How AIs integrate as social actors

• How AI influences our social interactions with each other

06 AI agent simulation of your users and customers Learn how generative agents can simulate user and customer behavior to inform product decisions. This module explores how these agents are built, where they are most useful, and how to assess their limitations and risks when used for design exploration and decision-making. Topics include:

• Motivations for human behavior simulations

• Generative agents

• High-level architecture for generative agents

• Agent believability and long-term behavior

• Applications for generative agents

Capstone project: Evaluating AI opportunities from concept to validation

In the capstone project, you will apply the course’s core frameworks to evaluate an AI opportunity from concept through validation. You will identify and prioritize intelligence augmenting product ideas, assess their feasibility and potential value, and examine associated risks, including ethical and societal considerations.

Using generative AI simulations, you will test how different users respond to your concept and refine your approach based on those insights. Through this capstone, you will strengthen your ability to make informed product decisions while critically assessing the strengths and limitations of AI in real-world applications.

The AI-Powered Product Innovation and Design multi-course series combines two complementary Stanford Online courses into a single enrollment. Together, they provide expertise in AI-powered product innovation and human-centered AI design while offering savings of 15%.