Course is closed
Lead Instructor(s)
On Campus
Course Length
5 Days
Course Fee
3.2 CEUs
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Self-driving cars. Advanced medical diagnostics. Predictive financial algorithms. Artificial intelligence (AI) is revolutionizing nearly every industry from energy and consumer products to transportation and finance. Given the pace of change, it’s essential that leaders know how to capitalize on the latest developments in AI.

That’s why MIT Professional Education is pleased to introduce Engineering Leadership in the Age of AI. Designed for accomplished technical professionals, this dynamic, five-day course will give you the tools and knowledge you need to lead, develop, and deploy AI systems in ways that augment human capabilities. Covering everything from the history of AI to modern opportunities and challenges, this course applies an AI system architecture approach to engineering products and services.

In the spirit of MIT's motto, “Mens et Manus,” or mind and hand, Engineering Leadership in the Age of AI offers collaborative, project-based learning experiences. You’ll also collaborate with a group of fellow participants to formulate a strategic roadmap of an innovative AI application.

Through this immersive experience, you’ll implement the key leadership tenets taught in the course: identifying and developing a strategic vision, formulating a competitive value proposition, convening and developing a high performing team, and building a culture that supports new AI applications. At the end of the course, you’ll present your roadmap to a panel of industry and academic experts and receive real-time feedback. Equipped with the knowledge you gain in Engineering Leadership in the Age of AI, you’ll be well prepared to lead AI teams and harness the most transformational technology of our time.

Participant Takeaways

  • Understand end-to-end AI architecture at the systems engineering level
  • Demonstrate a machine learning algorithm using a hand-held low power computer
  • Assess the current capabilities of AI subsystem components
  • Effectively leverage systems engineering tools and techniques
  • Verify individual subcomponents and validate an end-to-end AI system
  • Secure an AI system both physically and against cyber threats
  • Understand the ethical issues and principles of AI
  • Apply engineering leadership principles to an AI team and innovation
  • Leverage power and influence to lead the implementation of AI innovations
  • Create a strategic vision and development plan focused on technology-based AI products and/or service—and effectively communicate that vision
  • Identify measurable metrics, validate an end-to-end AI system, and secure it 

Who Should Attend

Engineering Leadership in the Age of AI is designed for senior managers who want to enhance their ability to lead, develop, and deploy AI systems. In particular, this course will benefit individuals who are responsible for defining technology strategy; for example, those with titles such as Chief Technology Officer, Chief Information Officer, Technical Manager, Project Manager, and AI Design Engineer.

This course is appropriate for individuals at any company—from global corporations to small start-ups—that develops AI system capabilities or is a user/buyer of AI systems. Participants are welcome from a wide range of industries including energy, consumer AI products and services, aerospace, transportation, robotics systems, finance, national security, and health. 


Participants should have an undergraduate education, management responsibilities, and be interested in gaining AI leadership skills. In addition, participants must bring a laptop (or tablet with an ethernet connector) that has Mac OS or Microsoft OS. Participants will need to download open source AI software (e.g., Anaconda Navigator). 

Program Outline

Day One: 9:00am – 5:30pm

  • Introduction to course framework on engineering leadership and AI
  • AI system architecture lectures and discussions on data conditioning, machine learning taxonomy, and modern computing
  • Exploration of individual interests and groups for project-based learning
  • Evening reading assignment on human-machine teaming and robust AI

Day Two: 8:30am – 5:30pm, 6:00pm – 8:30pm (group reception)

  • Lectures and discussions on human-machine teaming, robust AI, ethical challenges and opportunities for AI, and leading an organizational culture around AI
  • In-class project using the Raspberry Pi computer and machine learning tools
  • Formation of groups for project-based learning
  • Evening group reception with fellow participants

Day Three: 8:30am – 5:30pm

  • Exploration of strategic development model, including:
    • Creating a vision and direction around an AI innovation and communicating that strategy
    • Convening and developing new teams charged with AI innovation
    • Formulating a competitive value proposition, developing a SWOT analysis, and identifying specific goals and actions
  • In-class project to develop a strategic roadmap for an AI product or service
  • Evening reading assignment on networking in the organization and mentoring AI teams

Day Four: 8:30am – 5:30pm

  • Exploration of engineering leadership principles
  • Lecture and discussion on ways to strengthen your ability to implement new innovations, mentor AI teams, and lead with power and influence
  • Summary of main takeaways on engineering leadership in the age of AI
  • Presentations by class teams on their AI strategic roadmap for a product or service

Day Five: 8:30am – 12:00pm (team presentations), 12:00pm-12:30pm (issuing of course certificates)

  • Continuation of presentations by class teams on their AI strategic roadmap for a product or service
  • Issuing of course certificates