Carnegie Mellon University

Managing AI Systems

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Practical AI Within Reach

A curriculum designed for non-technical leaders to gain AI expertise and drive organizational change.

AI is rapidly creating new solutions (and new problems) across every industry. As a manager without a technical background, you need to quickly gain AI knowledge in a way that is specifically designed for your needs - starting with the basics of AI, through knowing when and how to implement AI solutions, the ethical and risk considerations of those solutions and, most importantly, how to manage AI adoption across the organization. 

Our curriculum tackles all of this using a systems-thinking approach that allows you to think beyond the technical considerations of an AI implementation more broadly across the entire organization. As a leader, you don’t need to learn programming or complex math algorithms to lead an AI project - but you do need to understand the context and important considerations to create a successful AI solution that sticks.    

Finally, you are facing these issues today and need practical application, not theory. All of our courses are taught by CMU faculty who have a mix of both subject matter expertise and extensive industry experience, bringing first-hand knowledge of the problems you may face and how to solve them. In addition, each course offers a hands-on project that allows you to apply what you have learned and create a portfolio of materials that you can refer to and rely on in the future.

Curriculum Overview

The Graduate Certificate in Managing AI Systems includes four graduate-level, credit-bearing courses taught by expert CMU faculty and features the following course progression: 

For SPring 2026 Start:

Semester

Spring 2026

Summer 2026

Fall 2026
Course

AI Foundations

Operationalizing AI Systems

Responsible AI

 Building the AI Organization

All students begin the program with two, concurrent courses: one 7-week course called AI Foundations that provides fundamental AI knowledge, and one semester-long (or 14-week) course that explores one of three topics: operationalizing AI systems, responsible AI, or building an AI organization. This means students will take two courses per week (on different nights), every other week for the first half of the semester and then one course per week, every other week for the second half of the semester.

All courses meet bi-weekly in live-online classes with CMU faculty. Additional content is offered on-demand so you can finish the work on your own time when your schedule allows. Each course will appear on your Carnegie Mellon transcript with the grade earned as evidence of completion. Once you successfully complete all four courses, you will earn a certificate of completion.

Course Descriptions

Number of Units: 3 units (~1 credit)

All students start with AI Foundations to provide baseline knowledge that supports the rest of the program. The course provides an overview of AI technologies, addresses AI misconceptions, and clarifies the capabilities and limitations of AI. It is taken first by all students and meets bi-weekly over the first seven weeks of the term concurrent with the first full course being offered.

By the end of this course, students should know how to answer questions like:

  • What is AI technology and how is it used?  What does AI look like?
  • How do you develop a systems thinking mindset?  
  • What are the components of an AI system?
  • How can AI help you reach organizational goals? What is the ROI?

Course Project
Students will create a systems map outlining the elements of their organization and the ways in which an AI solution could impact the company.

Number of Units: 7 units (~2 credits)

An introduction to the practical aspects of managing AI throughout its lifecycle, from scoping to deployment, all while developing a systems-thinking mindset to navigate complexity effectively. 

By the end of this course, students should know how to answer questions like:

  • What are the key components of the AI system lifecycle?
  • What types of business problems are suited for AI and how do we scope them?
  • Should you build or buy?  
  • What are the core data challenges to be aware of in AI?
  • What are the most frequently used AI models?
  • How do you scale and sustain your AI solution?  

Course Project
Students will demonstrate their management approach for an organizational challenge based on a hypothetical, industry-based scenario.

Number of Units: 7 units (~2 credits)

This course introduces students to the ethical considerations of AI implementation, covering risk mitigation strategies, test and evaluation methods, and governance frameworks to ensure responsible AI deployment. 

By the end of this course, students should know how to answer questions like:

  • How do you design and implement effective AI governance frameworks to ensure responsible and ethical AI adoption? 
  • How do you test AI models for fairness, bias, accuracy and precisions? 
  • How do you mitigate harms incurred and communicate after incidents occur?
  • How do you train responsible AI champions?

Course Project
In this course, students will develop a governance plan for AI implementation based on an industry-based case study.

Number of Units: 7 units (~2 credits)

AI systems are complex and require robust planning to implement successfully. In this course, students will learn to formulate AI strategies that align with organizational goals, consider talent acquisition, follow change management best practices, and foster trust between AI systems and stakeholders. 

By the end of this course, students should know how to answer questions like:

  • How can AI support organizational value propositions?
  • What people and skills should be on your AI team?
  • How do you build AI literacy in your organization and dispel common AI myths?
  • How do you organize AI efforts in your organization?
  • How do you prioritize AI projects across business functions?
  • How do you stay current on AI trends and build a learning organization?

Course Project
In this course, students will develop an organizational roadmap and experimentation plan that articulates their strategy for AI implementation.

At a Glance

Next Start Date
January 2026

Application Deadlines
Priority*: September 17, 2025
Final: December 3, 2025

*All applicants who submit by the priority deadline will receive a partial scholarship award.

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Questions? There are two ways to contact us. Call 412-501-2686 or send an email to apply@online.cmu.edu with your inquiries.

Meet Our World-Class Faculty

Dr. Rachel DzombakDr. Rachel Dzombak

Distinguished Service Professor of Design and Innovation & Senior Innovation Advisor

Education: Ph.D., University of California, Berkeley

Dr. Dzombak is a researcher, educator, and AI consultant for a range of sectors including technology, defense, construction, and government. She partners with organizations to help them understand the implications of AI adoption and navigate complexity to drive system change. Dr. Dzombak frequently consults and provides training on: design and systems thinking, the Future of Education, digital transformation and workforce development, responsible AI and AI system evaluation, innovation ecosystems, teaming with diversity, and life design.

Dr. Anand RaoDr. Anand Rao

Distinguished Service Professor of Applied Data Science and AI

Education: Ph.D., University of Sydney, Australia

Previously the Global Artificial Intelligence Leader for PwC, Dr. Anand Rao has focused on research, innovation, applications, and the business and societal adoption of data, analytics, and artificial intelligence over his 35-year consulting, industry, and academic career. With his academic and professional experience, Dr. Rao merges business domain knowledge, software engineering expertise, statistical expertise, and modeling expertise to generate unique insights into the practice of ‘data science’ and artificial intelligence.

The Graduate Certificate in Managing AI Systems is offered by distinguished faculty in CMU’s Heinz College of Information Systems & Public Policy. As AI visionaries in their field, they often advise policymakers on complex topics and collaborate on high-profile AI projects. Check out some of their work below.

CMU graduate students in Professor Chris Goranson’s Policy Innovation Lab: Public Interest Technology developed a GenAI application for researchers.

Dr. Rema Padman, Professor of Management Science and Healthcare Informatics at Heinz, has researched the opportunities and challenges of AI in healthcare.

Heinz College Dean Ramayya Krishnan testified before a Senate subcommittee on the need for transparency in AI to spur its responsible adoption and use.

The Building Blocks of Our Curriculum

AI, Made Possible

The Managing AI Systems Graduate Certificate is designed for non-technical professionals with a passion for identifying and solving organizational problems. Students in this certificate are innovators - they recognize the power of AI and have an idea of what they could do with it, but they might not know how to make it happen on the job. By the end of this certificate, students will understand the ins and outs of systems thinking and have a robust toolkit they can leverage to effectively implement and manage the AI lifecycle.

Practical Coursework

Businesses don’t just want AI - they want better processes to help them improve products, drive customer satisfaction, and increase revenue. Sometimes, AI is the answer. And sometimes, it's not. The coursework in this certificate will help students navigate the inevitable question, "Well, what about AI?" when posed with a new organizational problem. They'll learn which business problems can be solved with AI, whether to build or buy an AI solution, how to deploy and scale AI systems, and how to manage the successful adoption of AI across departments. With this newfound knowledge, students will be ready to make an immediate impact in their organization.

Hands-On Projects

Each course in this certificate culminates in a final project for students to apply what they’ve learned in the live-online classes. Imagine creating a systems map based on the intricacies of your own organization or demonstrating the management approach you would take to tackle an organizational problem for your boss. These projects will serve as proof points for your newfound expertise in AI management. When you complete this certificate, you'll leave with a robust portfolio that showcases your understanding of AI and your ability to think strategically about enterprise-scale AI implementation.