Applied Generative AI for Digital Transformation

Inquiring For
Work Experience

Live Online Learning Experience

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Empowering Live Sessions

Immerse yourself in interactive sessions and meaningful conversations led by MIT experts.

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In-depth Q&A Engagement

Elevate your understanding through insightful one-hour Q&A sessions after each live session.

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Rich Supplementary Resources

Access a trove of additional content and resources, offering a comprehensive learning experience.

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Harness groundbreaking technology

To unlock your organization's full potential.

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Revolutionize Workflow Efficiency

Play an essential role in optimizing workflows in your organization.

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Tap into the future of productivity

Through emerging generative AI techonology.

 What will you learn?

What will you learn?

  • Understand generative AI deeply, including its historical development

  •  Discover how diverse domains like art, biology, emotional support, and learning apply Generative AI

  •  Comprehend and implement prompt engineering to enhance productivity

  •  Learn strategies for automating organizational workflows using Generative AI

  •  Understand the dynamics of reinforcement learning and the power of data search in Generative AI

  •  Navigate the ethical, compliance, and risk aspects associated with Generative AI

  • Harness the power of Gen AI in addressing real-world business challenges

All the participants who successfully complete their program will receive an MIT Professional Education Certificate of Completion, as well as Continuing Education Units (CEUs)*.

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.

*The Continuing Education Unit (CEU) is defined as 10 contact hours of ongoing learning to indicate the amount of time they have devoted to a non-credit/non-degree professional development program.

To understand whether or not these CEUs may be applied toward professional certification, licensing requirements, or other required training or continuing education hours, please consult your training department or licensing authority directly.

Course Outline

Applied Generative AI for Digital Transformation program is a focused three-week live virtual course designed to explore generative AI technologies in depth. This course seamlessly combines technical knowledge with managerial perspectives, ethical considerations, and human factors, providing a comprehensive understanding of digital transformation strategies leveraging AI as a driving force for change.

Who is This Virtual Course For?

  • Senior leaders grappling with the potential opportunities and challenges of generative AI for their businesses

  • Technology leaders who want to learn current best practices for adopting and optimizing generative AI systems to boost business outcomes

  • Senior managers and mid-career executives who want to gain insights into the potential applications of generative AI within their organizations

  • Innovation managers, sales and product managers, and marketing and customer experience professionals who want to learn how to leverage generative AI to create new products, new content, and personalized customer experiences

  • Investors in venture capital, private equity, or hedge funds looking to understand investment opportunities created by generative AI

  • Professionals from all industries and sectors are welcome to create a dynamic learning ecosystem

Prerequisites: No prior background in analytics, computer science, coding or machine learning is required.


MPE - Faculty - John R. Williams
Prof. John R. Williams

Professor of Information Engineering, Civil and Environmental Engineering and Director of MIT Geospatial Data Center, and a faculty member in the Center for Computational Science and Engineering part of the Schwarzman School of Computer Science

MPE - Faculty - Abel Sanchez
Dr. Abel Sanchez

Executive Director of MIT’s Geospatial Data Center, Research Scientist; Center for Complex Engineering Systems, Sociotechnical Systems Research Center, under the Schwarzman School of Computer Science

Faculty Contributor

MPE - Faculty - Armando Solar Lezama
Prof. Armando Solar-Lezama

Professor in MIT's Department of Electrical Engineering and Computer Science; Head of Computer-Assisted Programming Group and Associate Director and COO of MIT Computer Science and Artificial Intelligence Laboratory

MIT Research Contributor

MPE - Faculty - Katie M Lewis
Katie M. Lewis

MIT Research Contributor

Industry Contributors

MPE - Faculty - Susan Doniz
Susan Doniz

Chief Information Officer of The Boeing Company and Senior Vice President of Information Technology and Data Analytics

MPE - Faculty - Mark Schwartz
Mark Schwartz

CIO and Enterprise Strategist at Amazon

MPE - Faculty - Jacob Depriest
Jacob Depriest

Deputy Chief Security Officer at GitHub

This course opened up new frontiers in my professional experience and it also, most especially, provided multiple entry points to learning and applying what I learned. This allowed me to create multiple exit points while expanding the initial ask for every assignment, discussion, or reflection. I cannot thank the MIT Professional Education team enough for the time they took to plan and execute on this course. This is being offered at such a pivotal and marked time in our history. I am extremely satisfied with this course and it offered what I was looking for. I am especially impressed with how Professors Sanchez and Williams found ways to translate brilliance into everyday terms for non data scientist like me. I believe that most of the folks in this cohort felt like we needed this 'launch pad' to feel better informed and to learn about which questions to ask as we continue on our curious journey to peel the layers of where GAI is and where it is going. Thank you, I will certainly be back.
Nina Araujo
Director of Learning Solutions
University of Maryland Global Campus

Register now and boost your professional trajectory.

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