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Driving Digital Transformation with Applied Generative AI
Artificial Intelligence is revolutionizing various sectors by automating processes, analyzing large volumes of data, and providing valuable insights. Among the many branches of AI, generative AI stands out for its ability to create new content, from text and images to music and software code, based on patterns learned from existing data.
Generative AI now enables the automation of complex tasks that were previously exclusive to humans. Companies in sectors such as marketing, healthcare, finance, and entertainment are using this technology to innovate and improve operational efficiency. As generative AI continues to evolve, its potential to drive innovation and efficiency across various sectors is only expected to grow, redefining the future of work and business.
$7.9 trillion
Generative AI could have an economic impact of up to $7.9 trillion per year.
Source: McKinsey
94%
of business executives believe that AI is a key to success in the future.
Source: Deloitte
78%
of executive leaders believe that the benefits of generative AI outweigh the risks.
Source: Gartner
An online course designed to enhance your generative AI expertise
MIT Professional Education’s online course Applied Generative AI for Digital Transformation explores the complexities of generative AI, providing participants with advanced knowledge and tools to drive digital transformation, improve efficiency, and create new pathways for innovation.
The skills you will develop
This 8-week course helps participants understand generative AI and its evolution, equipping them with the knowledge and confidence to make effective decisions, while driving innovation and digital transformation within businesses. This AI course provides key theoretical and practical tools to optimize productivity and business growth in an efficient and automated way.
1.
Understand and explain the historical context behind artificial intelligence, including its origins and foundational concepts.
2.
Harness the latest generative AI tools to automate workflows and increase productivity in the workplace.
3.
Leverage prompt engineering principles to enhance organizational performance and drive results.
4.
Employ generative AI for domain-specific tasks such as video summarization, automated research, and conversational data understanding.
5.
Identify opportunities, ethical considerations, and potential challenges in digital transformation through the application of generative AI.
6.
Minimize risks, protect data privacy, and enhance the customer experience by utilizing Gen AI technologies and tools.
In addition, you will receive a Certificate of Completion
All the participants who successfully complete the online course Applied Generative AI for Digital Transformation will receive an MIT Professional Education Certificate of Completion. Furthermore, participants will receive * MIT 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.
This course is aimed at
Senior leaders responsible for guiding generative AI initiatives and shaping key decisions for their organizations.
Technology leaders seeking to master the latest best practices for implementing and optimizing generative AI systems to drive organizational success.
Senior managers and mid-career executives looking to discover potential opportunities for generative AI in their organization.
Innovation managers, sales and product managers, and marketing and customer experience professionals who want to learn to harness generative AI to develop new products, craft compelling content, and deliver personalized customer experiences.
Investors in venture capital, private equity, or hedge funds seeking to uncover new investment opportunities fueled by generative AI advancements.
Meet the instructors of this course

He is the author and co-author of more than 250 articles in journals and conferences. Professor Williams teaches courses on the basics of programming, modern software development, and the architecture of Web, cloud, and blockchain systems.

Dr. Sanchez holds a PhD from the Massachusetts Institute of Technology (MIT) and teaches MIT courses in cybersecurity, engineering, blockchain, and data science. He has been involved in developing educational software for Microsoft and establishing the Accenture Technology Academy. He has produced over 150 educational videos, has 10 years of experience with learning management systems, and has made deployments in the Americas, Asia, and Europe.
Faculty Contributor

He is currently the principal investigator of the NSF-funded project “Expeditions: Understanding the World through Code” and is also the founder of playskript.com, an online platform for creating interactive presentations. Solar-Lezama earned his PhD from the University of California, Berkeley.
Affiliations:
- CSAIL: Vertical AI Community of Research; Applied Machine Learning Community of Research; Computation Structures Group; Center for Deployable Machine Learning (CDML)
- Associate Director and COO, Computer Science and Artificial Intelligence Laboratory
- LEAD: Computer-Aided Programming Group
Areas of Interest and Expertise:
- Programming systems focused on software synthesis
- Programming tools for computation
- High performance
- Cybersecurity
MIT Research Contributor

Some of her generative machine learning publications include At the Intersection of Conceptual Art and Deep Learning: The End of Signature, Generating Image-Specific Text for Fine-grained Object Classification, and Machine Learning for Healthcare (ML4H) at NeurIPS 2018.
She recently defended her dissertation under the supervision of John Guttag and Frédo Durand and has interned twice with Ira Kemelmacher-Shlizerman's team at Google.
Industry Contributors

DePriest has over 15 years of experience as an engineer in cybersecurity, cloud architecture, and organizing technical teams.

Mark Schwartz is also an accomplished author and his most recent work titled Adaptive Ethics for Digital Transformation touches on how the act of digital transformation requires a change in the moral outlook and ethical assumptions of a business.
In 2010, Mark Schwartz was named one of the Premier 100 IT Leaders by Computerworld Magazine for his contribution in technology leadership, innovative ideas, and effectively managing IT strategies.

Before joining Boeing in 2020, Doniz was the Group CIO of Qantas Airways, where she expanded the airline’s digital ecosystem and adopted new technology to support the needs of the business and its customers.
Previously, during a 17-year career at Procter & Gamble, she led IT and Analytics programs in support of sales, research and development, the supply chain, as well as a cross-functional program to digitize the company. Doniz also worked at SAP, where she was a strategic adviser to the global chief executive officer on transformation and technology issues in support of customers, and Aimia.
Doniz has been a board member of multiple nonprofit organizations, including The Women’s College Hospital Foundation, Salvation Army and Engineers Without Borders. She serves as an adviser to the Center for Digital Transformation at the University of California, Irvine, Paul Merage School of Business and was previously the vice chair of the Digital Transformation Advisory Council of the International Air Transport Association."