Northwestern Kellogg has launched new MBA courses in AI and machine learning for Full-Time MBA and Evening & Weekend MBA students that the school says “will offer a practical foundation in emerging technologies through a cross-disciplinary lens.” Kellogg photos
Professor Sebastien Martin likens the new AI initiative at Northwestern University’s Kellogg School of Management to a professional kitchen during rush hour.
“AI arrives and then for a second you’re like, ‘Oh my God, what’s happening?’” he says. “But at the same time, it feels like a huge opportunity. Everything is moving so fast.”
That urgency — and opportunity — is what drove Kellogg to launch its first coordinated AI and machine learning curriculum this fall. The centerpiece is AIML 901, a new foundational course offered across five departments — operations, finance, marketing, strategy, and management — each presenting a unique lens on the AI revolution in business.
The result? One course code, five distinct versions — and every section, Martin says, is already “oversubscribed.”
A CROSS-DEPARTMENTAL RESPONSE TO A CROSS-FUNCTIONAL PROBLEM
Kellogg’s Sebastien Martin: “There’s something even more important than making the perfect thing — and that’s doing it fast. Because our students have a craving need to use this technology”
The idea began, Martin recalls in an interview with Poets&Quants, with a realization: no one department could claim AI. “You’re like, okay, maybe we should teach it. But exactly, what do we teach? And who teaches? Like, what is it?” he says. “Is AI analytics? Maybe not. Is it marketing? Maybe. Maybe it’s everything. All of the above.”
That uncertainty — and the need to act before perfect answers emerged — drove Kellogg to do something new: let each department teach AI its own way, under the umbrella course AIML 901. Students could choose among sections led by professors in operations (Martin), finance (Bryan Seegmiller), marketing, strategy, or management & organizations — with shared foundational pillars but different applications and assignments.
“When something moves very fast, you sort of want to operate like a startup,” says Martin, associate professor of operations at the school since 2020. “Instead of waiting three years for getting a course, just do it. And understand this: actually, it’s even more opportunity for them.”
AI FOR OPERATIONS — AND FOR EVERYONE ELSE
In Martin’s operations section, students build AI agents that do real tasks, then step back to reflect on what those agents mean for organizations, labor, and governance. “My angle from operations is AI agents — so AI that does work,” he explains.
“As they’re building agents, we have recitations where it’s all hands on deck… and in the lecture, we take a step back: okay, you tried this — but what does it mean?”
That mix of hands-on design and high-level discussion is a hallmark of AIML 901 — and it’s designed for everyone. One of the course’s core design principles was accessibility, regardless of technical background.
“The AI courses, we actually believe that they should be for everyone,” Martin says. “Before, machine learning and analytics courses could have requirements… now, even building agents doesn’t require coding anymore. AI is doing that for you.”
FINANCE MEETS AI: FOUNDATIONS FIRST, THEN APPLICATION
Kellogg’s Bryan Seegmiller: “The goal is not to train engineers, but to help students think critically about which AI tools fit which problems”
In the finance section, Assistant Professor Bryan Seegmiller takes a methodical approach. Students begin by learning key AI and ML concepts — including supervised vs. unsupervised learning, regression vs. classification, and various modeling techniques ranging from logistic regression to gradient boosting and deep learning.
“This foundation is crucial: without basic knowledge of how these technologies work, it’s impossible to critically analyze outputs or choose the right tool for a given problem,” Seegmiller tells P&Q. “From there, we’ll turn to finance applications, where domain-specific challenges emerge. For example, asset managers deal with very different signal-to-noise dynamics than fraud detection teams.”
He continues:
“In the finance industry fiduciary duties can make model transparency (explainable AI) especially important in certain settings. Benchmarks and evaluative approaches for identifying ‘success’ must be adjusted accordingly, and students will learn why.
“The goal is not to train engineers, but to help students think critically about which AI tools fit which problems.”
The course culminates in a project that asks students to identify an AI-driven opportunity in the finance industry and propose a solution — with a clear, defensible rationale.
COGNITIVE OFFLOADING & HUMAN JUDGMENT
Seegmiller, who joined Kellogg in 2022 after earning his Ph.D. in financial economics from MIT, also addresses a growing problem in MBA classrooms: over-reliance on AI tools to do the thinking.
“One of my concerns is that students sometimes let AI do their thinking for them, a tendency known as ‘cognitive offloading,’” he says. “While tempting, it prevents the development of skills needed to complement AI.”
He sees case studies as a key tool to fight that tendency.
“Students could easily turn them over to AI,” he says, “but I will encourage them to wrestle with the material themselves because I want them to learn insights from these cases to help build analytical tools that they can use to generate their own ideas.”
STRUCTURED COLLABORATION — AND THE BIRTH OF A CURRICULUM?
Though each version of AIML 901 has its own content and angle, faculty from the five departments plan to meet regularly, share notes, and discuss what’s working, Martin says. That cross-pollination could grow into something more formal.
“Maybe behind there is a sort of birth of a complete curriculum,” Martin says. “There’s no clear plan yet, but people are completely open, if this works well, to build it up.” Follow-up electives are already emerging, he notes, with some courses being reclassified as AIML 901 extensions.
It’s not just internal momentum — student demand has been staggering.
“It was one of the courses with the most immense demand,” Martin says. “Completely oversubscribed.”
THE LONG GAME: AI-EMPOWERED MBAS
While some MBA students have expressed concern that AI may one day replace their skillsets, both Martin and Seegmiller emphasize that MBAs are positioned to lead in the new landscape — not be left behind.
“AI automates things,” Martin says. “But it automates technical things. The things it can’t do — human interaction, leadership, structure, creation — are exactly what MBAs are trained for.”
Seegmiller, too, underscores the importance of human oversight in an AI-driven world. The ultimate goal? Complement AI — don’t surrender to it.
“AI can be a productivity accelerator,” says Seegmiller, “but its outputs should always be interpreted and evaluated by someone with domain expertise. My rule of thumb: if you can’t reconstruct and critique the reasoning behind an AI-generated result, you’re relying on it too heavily.”
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