Leading with AI: Strategy and Product Transformation

Duration: 60 Minutes (live session)

This session connects the strategic mindset developed in Course 1 with the innovation focus of Course 2. It is designed to help participants transition from the leadership perspective introduced in the first course to the applied innovation lens of the second, highlighting how the two parts of the program fit together as a cohesive learning journey.

Duration: 6 weeks

Module 0: 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:

1. The foundations of AI in machine learning

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

3. Training and deploying LLMs

4. Real-world applications across products and industries

Module 1: 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. You will also explore how to reframe high-stakes problems into safer, more adaptable workflows and forecast how AI capabilities may evolve over time.

Topics include:

1. Understanding the challenge of predicting AI progress

2. Defining sharp-edged and rough-edged problems

3. Understanding why AI performs differently on each type

4. Using the framework to predict what comes next

5. Turning sharp problems into rough ones

Module 2: 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:

1. People: where AI lives or dies

2. Intelligence augmentation

3. Achieving intelligence augmentation

Module 3: 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:

1. Why so many AI products fail

2. Lessons from past technologies

3. The ChatGPT gravity well

4. The psychology behind the pull

5. How to escape the gravity well

6. Designing AI products users will adopt

Module 4: 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:

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

2. Two techniques for identifying ethical issues: Tarot Cards of Tech and Black Mirror Writers room

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

Module 5: 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:

1. The AI chatbot rogues' gallery

2. How AIs integrate as social actors

3. How AI influences our social interactions with each other

Module 6: 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:

1. Motivations for human behavior simulations

2. Generative agents

3. High-level architecture for generative agents

4. Agent believability and long-term behavior

5. Applications for generative agents

Live webinars with the faculty: Participate in two interactive, faculty-led live webinars designed to enhance your understanding, address real-world challenges, and connect leadership concepts to practical AI implementation.

Capstone project 2: Evaluating AI opportunities from concept to validation

Participants apply core frameworks to evaluate an AI opportunity from concept through validation, assessing feasibility, value, and associated risks. Using generative AI simulations, they test user responses and refine their approach based on these insights.

This final reflection activity challenges participants to integrate their experience implementing generative AI in high-value workflows with their experience developing and evaluating an AI-powered product concept. Drawing on frameworks from both courses, learners will examine how leaders navigate implementation decisions, product idea generation, ethical review, and simulation-based feedback, articulating key insights and leadership implications across both contexts.

The program consists of two six-week courses. Learners begin with AI-Driven Leadership: Strategies for the Future, followed by AI-Powered Product Innovation. Across both courses, learning includes faculty-led instruction, structured discussion forums, and applied projects.

This program is designed for professionals responsible for leading, shaping, or supporting AI initiatives across organizations and products. Typical participants include executives, product leaders, innovation and strategy professionals, technology and data leaders, and entrepreneurs working with AI-enabled offerings.

No prior technical AI experience is required. Learners typically bring experience in leadership, product development, innovation strategy, or organizational decision-making.

Learners who successfully complete the program will earn three Stanford Online credentials: two digital badges for completing the individual courses and one digital certificate for completing the integrated learning journey. Eligible learners may also earn Continuing Education Units (CEUs) upon completing the required accreditation confirmation.

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