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Artificial Intelligence Management Concentration

Carnegie Mellon University is a pioneer and a hub for artificial intelligence. At Heinz College, we have embedded our AI expertise into your master’s education, regardless of whether you are studying information security, public policy, the arts or health care.

At Carnegie Mellon, we view AI as intertwined with everyday life. In entertainment, health care and cybersecurity, in Silicon Valley and on Capitol Hill, change makers are using AI to address the problems that have stymied our efforts to solve them. 

To maximize the impact AI can have while minimizing its risk of harm, the world needs leaders who understand the entire AI life cycle and steer the technology toward the greater good. This is where Heinz College lives, and we are proud to offer a concentration in AI Management. 

This concentration will be a differentiator in the job market and in your career.

The purpose of this concentration, which is open to current and incoming students in our MSPPM two-year and Data Analytics tracks, as well as our MAM, MSHCA and MSISPM programs, is to build aptitude in operationalizing and governing AI systems. You will complete a semester-long AI Capstone project, during which you’ll work with a real-world client, to reinforce your knowledge and build relevant experience.

As a Heinz master's student, you can opt into the AI Management concentration simply by taking relevant elective coursework at Heinz College or across CMU. Students opting to pursue an AI Management concentration must submit the Concentration Declaration Form and may request a letter of concentration completion from the Heinz College Office of Academic Services.

Current Students: Declare Concentration

Incoming Students: Learn More

Coursework Overview 

To obtain the concentration in AI Management, students are required to take at least 42 units of coursework, including 18 units of foundational courses; 18 units out of approved electives; and 12 units of an AI capstone project. 

Required Foundational Courses (18 Units): 

  1. Fundamentals of Operationalizing AI: Mastering AI Lifecycle from Theory to Practice (94-879): Understand the AI lifecycle from business scoping, procurement, data management, and engineering to model development, deployment and stewardship. The course introduces the concept of Operationalizing AI, a critical aspect of AI implementation. It will cover its significance, the challenges it presents, its benefits, and the roles involved, focusing on the emerging best practices, roles, skills, capabilities, and governance across the AI lifecycle.

  2. Responsible AI: Principles, Policies, Practices (94-885): Understand the risks and harms traditional and generative AI can pose, the principles guiding ethical use of AI, and the intricacies of how these harms manifest themselves in the AI lifecycle. This course places a strong emphasis on bias, fairness, transparency, explainability, safety, security, privacy, and accountability, demystifying these foundational concepts and highlighting their relevance in the end-to-end AI life cycle.

  3. Critical AI Studies for Public Policy (90-769): Instead of studying AI as a purely technical subject, in this course you will critically examine the most recent developments and deployments of AI from a social, cultural and policy perspective. Drawing upon real-world cases, this course will introduce you to the basic concepts and main topics to think about AI socially, help you understand the potential benefits and pitfalls of various contemporary AI applications, and think toward future AI systems that can deliver greater social good.

Approved Electives (Select 18 units from the list below):

Course #


Course title



Applications of NL(X) and LLM



Generative AI Lab



Disruptive Technologies in Arts Enterprises



AI Security



Managing Analytics Projects



Introduction to Artificial Intelligence



Machine Learning for Public Policy Lab



Machine Learning for Problem Solving



Societal Consequences of Tech Change: Generative AI & Societal Implications



Agent-Based Modeling and Digital Twins



Tech Strategy



Making Products Count: Data Science for Product Managers



Systems Thinking & Discrete Event Simulation



Advanced AI and Business Strategy



Design Thinking