Leading with AI: Strategy and Product Transformation
From AI readiness to real-world product impact
Benefit from up to 14% off on this integrated learning journey
Leading with AI: Strategy and Product Transformation brings together two complementary Stanford Online courses into one integrated learning experience. This journey combines AI leadership frameworks with human-centered product innovation, enabling professionals to lead AI adoption while designing AI-powered products that deliver real-world value.
Lead with confidence at the intersection of strategy, product innovation, and AI
Leading with AI: Strategy and Product Transformation from Stanford Online helps professionals move beyond AI experimentation toward scalable organizational and product impact. Over 12 weeks, this integrated learning journey combines AI-Driven Leadership: Strategies for the Future and AI-Powered Product Innovation to build the leadership perspective and product design frameworks required in the AI era.
Led by Stanford faculty and designed for professionals leading AI initiatives, this program develops the judgment required to adopt AI strategically by aligning data, workflows, people, and product decisions, enabling leaders to drive responsible AI from organizational strategy through human-centered product solutions.
Industry insights
$407 billion
is the projected global spend on AI systems by 2027, to drive leadership decisions and product innovation.
Source: IDC Worldwide AI Spending Guide
95%
of leaders see AI improving leadership decision-making, highlighting growing reliance on AI.
Source: IBM Survey
70%
of AI initiatives fail to scale due to design and alignment gaps, signaling gaps in leadership and design.
Source: McKinsey
What will you learn?
Apply AI-driven decision-making across real-world organizational and industry contexts.
Align AI initiatives with business objectives to drive efficiency and innovation.
Evaluate algorithmic capabilities across the AI lifecycle.
Assess organizational readiness for AI adoption, including data, governance, and change management.
Guide the development of trustworthy, human-centered AI products while translating AI strategy into scalable workflows.
Who is this program for?
Mid- to senior-level executives including CEOs, CTOs, and senior leaders with decision-making authority who are responsible for guiding AI adoption, organizational strategy, and transformation
Product leaders and product managers building or overseeing AI-powered products and services who want to identify high-value AI opportunities and design products that are trustworthy, usable, and human-centered
Digital transformation, innovation, and strategy leaders driving enterprise-wide digital initiatives who need to align AI capabilities with business objectives, operational priorities, and long-term organizational impact
Technology and data leaders leading data, analytics, or AI teams who seek to improve AI readiness, configure scalable workflows, and integrate generative and predictive AI into organizational processes
Entrepreneurs, founders, and business owners looking to implement AI strategies responsibly within their organizations and translate AI capabilities into sustainable product and business value
Learners typically bring experience in leadership, product development, innovation strategy, or organizational decision-making. No prior technical AI experience is required.
Program outline
Duration: 6 weeks
1. How leaders create conditions for effective integration of predictive analytics
Enable effective ML adoption through leadership practices. Evaluate current structures and propose new frameworks for implementation and measurement.
2. How leaders create conditions for effective integration of generative AI and agentic AI
Understand the strategic differences of GenAI. Design leadership approaches for framing, structuring, and evaluating GenAI initiatives.
3. Creating a culture of data excellence
Assess data quality, governance, and AI readiness. Build a framework for sustainable, integrated data practices.
4. Configuring workflows and decisions for machine learning (ML)
Design scalable ML workflows. Anticipate resistance and plan for change across teams and systems.
5. Configuring workflows for generative AI and agentic AI
Explore real-world GenAI use cases and evaluation strategies. Design a GenAI workflow that fits your context.
6. How the work of managers will change in the age of AI
Examine how AI is reshaping managerial roles. Conceptualize supportive tools for algorithmic decision-making and coordination.
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
Implementing generative AI in high-value workflows - Identify a high-impact workflow, evaluate tool fit, build an implementation roadmap, and reflect on key outcomes.
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
1. Sharp edges and rough edges
Learn how to evaluate which problems AI can and cannot solve reliably using the sharp-edged vs. rough-edged framework. Assess error tolerance, trust dynamics, and investment readiness.
2. Centering the human in human–AI interaction
Examine how AI products succeed or fail based on their ability to augment human capability. Apply intelligence augmentation principles to product design.
3. Avoiding the gravitational pull of ChatGPT: building AI products that last
Analyze why users default to general-purpose AI tools and how to design differentiated AI products that achieve durable adoption.
4. Ethics and the societal impact of your AI designs
Identify ethical and societal risks in AI product development. Apply structured techniques to surface risks early and embed responsible design decisions.
5. Designing social AIs: chatbots
Explore how users perceive AI systems as social actors. Learn design principles that influence trust, transparency, and user perception.
6. Generative agents: simulating human behavior
Learn how generative agents simulate user behavior to inform product decisions. Examine their architecture, applications, and limitations.
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
Creating a simple AI-powered design - Complete a hands-on capstone project where you develop and evaluate a real-world AI product concept using strategic frameworks, ethical assessment, and simulated user feedback. Apply course concepts from idea generation through validation and iteration to strengthen your ability to make confident AI product decisions.
The journey concludes with an integrated reflection activity, where you synthesize insights from strategic AI implementation and AI-powered product design. You examine how AI operates across both organizational workflows and product innovation, integrating lessons on implementation, product concept development, ethical assessment, and user feedback to strengthen holistic decision-making about AI adoption.
Program walkthrough
Complete two complementary Stanford Online courses over 12 weeks.
Participate in five live sessions with Stanford faculty on AI strategy and product design.
Apply concepts through hands-on assignments and guided peer discussions.
Work with modern large language models, including ChatGPT and other LLMs.
Earn three Stanford Online credentials, with Continuing Education Units upon completion.
Instructors
Melissa Valentine
Associate Professor of Management Science and Engineering, Stanford University
Melissa Valentine is an Associate Professor at Stanford University in the Department of Management Science & Engineering. Professor Valentine studies how technology is transfo...
Michael Bernstein
Professor of Computer Science, Stanford University
Michael Bernstein is a Professor of Computer Science at Stanford University, where he is a Bass University Fellow and STMicroelectronics Faculty Scholar. His research in human...
Certificate of Achievement from Stanford Online
All learners who successfully complete this integrated learning journey will earn three Stanford Online credentials: one each for completing AI-Driven Leadership: Strategies for the Future and AI-Powered Product Innovation (delivered as digital badges) and one for completing the Leading with AI: Strategy and Product Transformation program (delivered as a digital certificate). In addition, they will earn 8.5 Continuing Education Units (CEUs)* for completing the program.
*To obtain CEUs, complete the accreditation confirmation, which is available at the end of the course. CEUs are calculated for each course based on the number of learning hours. Note: All certificate images are for illustrative purposes only and may be subject to change at the discretion of Stanford Online.
Frequently asked questions
This integrated learning journey is delivered fully online and combines faculty-led instruction, structured discussion forums, and applied assignments across both courses. The format allows learners to engage with Stanford faculty and course content while applying concepts to real-world leadership and product challenges.
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.
Applicable taxes will be calculated and added at checkout in accordance with country/state regulations.
Connect with a Program Advisor for a 1:1 session
Didn't find what you were looking for? Schedule a call with one of our Global Alumni Program Advisors or call us at +1 315 871 5140
Register now and boost your professional trajectory.
Enroll by