AI concentrations and programs are popping up at top MBA programs around the world – including Ross School of Business.
Recently, we talked with S. Sriram, Associate Dean for Graduate Programs at Ross, who is leading the development of the school's new AI concentration. The curriculum was built around a framework that extends well beyond prompt engineering.
We talked about how the AI initiative got started, its core principles, and where it’s headed.
Identifying a strategic opportunity
A few years ago, when Ross students began using AI tools like ChatGPT, the initial reaction from faculty focused on how AI usage should be stopped.
However, that conversation soon evolved.
"Very quickly, we determined that's probably not the right way to think about it," Sriram explains. "We'd be doing a disservice to our students if we don't embrace AI and get them to thrive using this technology."
That pivot set the stage for how Ross approached building the AI concentration. The school reframed AI as a competitive necessity for graduates entering the current job market.
Three buckets: AI and me, AI and my organization, AI and society
Ross structured its AI concentration around three distinct categories of learning, which Sriram frames as concentric circles of impact:
AI and me: This means developing personal proficiency with AI tools and understanding what sits behind them. It includes data science courses covering machine learning algorithms and courses using AI for complex data analysis. The goal isn't making every MBA a prompt engineer. Instead, it's building sufficient technical literacy to use AI effectively and understand its limitations.
AI and my organization: This requires strategic thinking about how AI transforms business models across industries. Specifically, it’s where Ross believes MBA graduates can differentiate themselves. As Sriram puts it, "People who graduate from these programs are going to be future business leaders, and they need to be able to see around the corner and say, this is what is coming and this is how it's going to impact our business."
AI and society: This considers the broader implications of AI, including its environmental impact, the future of work, and ethical / legal considerations of AI technologies. This facet recognizes that business decisions about AI deployment have consequences far beyond quarterly earnings.
The framework ensures that Ross students don't just learn tools – they develop the strategic lens to identify opportunities, assess risks, and lead through technological disruption.
Developing a breadth and depth of skills
Ross intentionally structured the concentration to offer breadth and depth. Specifically, the school created two tiers of courses:
Fully AI-focused foundational courses and courses with substantial AI components. Courses include: Customer-AI Value Creation, AI and Strategy: Lead in the Age of Intelligent Machines, and AI for Business, among others.
Depth in specific areas – the classic T-shaped skill profile. Courses include: Healthcare Startups: Understanding Needs and Creating Ventures in Healthcare, Digital Marketing: Applications and Analytics, Advanced Big Data Analytics, and more.
To develop the curriculum, Ross asked faculty to identify which courses fit into the three buckets and explain how much AI content they included. "It's not just they put one case on AI and then you're done," Sriram notes. "It has to be a bit deeper."
What does this look like in practice?
Consider digital marketing, where AI has a tremendous impact. Search has changed, and people are asking ChatGPT questions, rather than going to Google. This results in “no click” searches and is disrupting online monetization models.
Additionally, content creation is being completely transformed by generative AI, which threatens traditional agency businesses. Also, personalization, which is already prevalent on social platforms, is " on steroids" according to Sriram.
A digital marketing course in Ross’s AI concentration doesn't just teach students how to use the tools. Instead, it forces them to grapple with how AI changes the game altogether.
Another example is product management. Sriram identifies three phases where AI creates opportunities and challenges:
Idea generation: AI makes it very easy to generate product ideas. However, your competitors have the same ideas. The differentiator becomes developing frameworks to extract better, more unique insights from AI than others can.
Testing and refinement: AI helps use synthetic data to simulate customer segments and test different product offerings, dramatically accelerating customer acquisition.
Go-to-market planning: AI can help scenario plan barriers to product adoption and develop strategies to address them before launch.
The constant thread is that AI doesn't replace strategic thinking or domain expertise. Instead, it amplifies both – if you have them.
Leveraging the University of Michigan’s strengths
One of Ross's differentiators is access to the greater University of Michigan ecosystem. For example, Ross students pursuing the AI concentration can take courses from computer science, information, and statistics departments. This campus-wide integration means that students aren't limited to business school perspectives on AI. They can develop more technical depth if their career path demands it.
As the concentration develops, Sriram expects it will continue expanding course offerings both within Ross and across the university.
Demand is strong
The concentration launched mid-fall semester for the current academic year – recent enough that Ross didn't expect significant second-year interest. However, MBA2s signed up anyway.
Multiple courses hit wait lists, forcing Ross to open new sections. MBA1 interest has also been strong, validating that the school identified real demand.
How is Ross measuring success?
Sriram shared two key metrics to evaluate the success of Ross’s AI concentration:
Employer demand: Are students developing skills that recruiters are looking for? This is the ultimate validation, but it will take time to measure as cohorts graduate and enter the job market.
Student satisfaction: Do students feel that they're learning skills that matter? This includes course evaluations and qualitative feedback about whether classes deliver on the outcomes they promise.
The concentration that might not be a concentration in the future
With AI adoption across industries and functions, is there really a need for an AI concentration?
"Every course is going to have an AI component," Sriram explains, “so it's going to be very hard for us to call it a concentration anymore, because all courses could actually do this."
He draws parallels to other technologies: "Thirty, forty years ago, knowing how to type something in Word was a very special skill. Now, it's not a point of differentiation. It's going to happen very quickly" with AI.
AI fluency is rapidly moving from a differentiator to table stakes. The concentration exists because we're in a transition period… but within a few years, expect every course to meaningfully integrate AI.
What does this mean for you?
If you're shortlisting MBA programs and trying to understand what actual AI integration looks like, Ross's approach offers useful benchmarks:
Look for a transparent development process: Did faculty systematically evaluate and restructure courses or just add "AI" to existing course titles?
Check for structural requirements: Does the program require breadth and depth, or can students develop basic AI exposure across disparate courses?
Understand the philosophy: Is the program teaching tools, or is it teaching strategic thinking about how AI transforms industries and business models?
Evaluate university-wide resources: Can you access technical courses from computer science or engineering departments if your career path requires these skills?
Ask about faculty commitment: Are professors actively updating courses to stay current with rapid AI developments or treating this as a one-time revision?
As you navigate MBA program selection and course planning, remember: the goal isn't learning to use ChatGPT. Anyone can do that. The goal is developing the strategic thinking, domain expertise, and critical evaluation skills that let you use AI to create value others can't.
S. Sriram is Associate Dean for Graduate Programs and Professor of Marketing at Michigan Ross School of Business. He teaches product management and advises on development of the school's AI concentration.