by Meredith Carroll | Nov 12, 2025 | Faculty Focus
When Clinical Professor of Economics Thomas Walker worked in industry just three years ago, AI was barely a topic of conversation.
Today, discussions about AI are everywhere. Businesses are incorporating AI into their offerings and their workflows. Entry-level analyst positions, once a mainstay for fresh business school grads, are at risk.
At the same time, uncertainty abounds. How much of the conversation around AI is signal and how much is noise? If this much has changed in less time than it takes to finish a bachelor’s degree, what might the world look like three years from now?
“Trying to stay in touch with something that’s changing so fast is a tricky thing to do,” said Walker. “But we’re focused on building a framework that helps students adapt and apply what they learn as the technology evolves — that helps them be resilient no matter what comes next.”
In a changing world, AI helps meet students where they’re at
As AI became a household term, work was already underway to reimagine DePaul’s MBA program. Aligning the program with the cutting edge in industry was one guiding precept; each core class now includes a hands-on project in partnership with a real-world company or nonprofit.
Aligning the program with the expectations of today’s students was another.
Some students arrive straight from undergrad; others, after several years in the workforce. Many are career switchers.
Incorporating AI makes it possible to tailor a business education to students’ increasingly varied experience levels and interests.
Walker teaches a foundational course in business analytics tools. The concepts he teaches can be abstract, but AI helps students connect these concepts to specific use cases.
Professor of Economics Rafael Tenorio does something similar in his Strategic Management Foundations class, another core course in the MBA program. When he teaches different business models, he tasks students with using AI to find real-world examples of those models.
“It empowers the students to participate,” he said. “It helps me illustrate the concept; it fills in gaps.”
Program pairs new skills with time-tested fundamentals
Like many of the core courses in DePaul’s MBA, the version of Walker’s business analytics tools class that he’s teaching this quarter is new. AI wasn’t the reason for curricular changes across the MBA. But it has shifted Walker’s thinking about what business education looks like in 2025, and how to deliver it.
A previous version of the class centered on three exams that Walker used to break down tricky, technical skills. Now, like other core courses across the MBA, the class is project-based. The emphasis isn’t just on practicing skills in isolation. Instead, it’s on understanding how to apply skills in complicated, real-world contexts.
“The more I think about AI, the more I think of it as a framework for discovery — for trying to answer big questions,” Walker said. “We really try to belabor the point that step number one of business analytics isn’t learning some program or software. It’s asking the right questions. It’s knowing where to start.”
For Tenorio, like Walker, grappling with AI has led him to return to the fundamentals — in Tenorio’s case, of the discipline of economics itself.
“What I want is for students to treat AI as a complement, not a substitute,” Tenorio said. “It can do boilerplate research for you in a fraction of a second. It can remind you of the fundamental things you should consider when trying to solve a strategic problem. But you don’t get brilliant, quirky solutions with AI. We still need to augment it with human insight.”
Experiences offer insights for future-proofing education and industry alike
How do you future-proof a business education, whether you’re in academia or industry? How do you brace for further changes in an already-changing world?
Tenorio’s and Walker’s experiences with reenvisioning the MBA classroom in the age of AI suggest that it goes something like this: You leverage new tools, whatever they are, to expand the range of material that is available to you. You focus on equipping students with frameworks that help them adapt to – or even get ahead of – change, whether that’s today’s new tools or innovations we can’t yet picture.
“That looks like working together on teams, asking good questions, finding the real problem that a business has — even finding problems they don’t know they had,” Walker said. “It looks like pushing the boundaries, moving the company forward, and setting better goals and objectives. Our MBA students get hands-on experience using AI to do just that. And our program is designed to continue doing that, even as technology evolves.”
by Meredith Carroll | Jun 24, 2024 | Scholarly Pursuits
By Jamie Merchant
The hype has been extraordinary.
Over the past two years, news outlets have blanketed the public with stories about the impact of large-language models (LLMs), or “artificial intelligence,” and their profound implications for human civilization. The CEO of Tesla and billionaire investor, Elon Musk, warns that the technology represents one of “humanity’s biggest threats.” Other commentators predict a more benign future in which AI liberates us from toil, taking over the mundane tasks of office work.
With conflicting reports like these, one could be forgiven for feeling confused.
Putting the hype aside, what are the facts on the ground? How is this emerging technology actually used by businesses and organizations?
At DePaul’s Driehaus College of Business, new faculty in the Department of Marketing are cutting through the headlines to investigate the promise, and the limits, of LLMs for modern businesses.
One innovative use of LLM’s is for producing “synthetic data”, AI-generated responses that simulate humans in order to inform business intelligence. “How good is AI at really representing human variety?” asked Ignacio Luri, an Assistant Professor of Marketing at Driehaus. “That’s something I’m skeptical about. But it’s happening, so I’m studying it.”
Luri, at right, in the Beta Hub
Luri, whose background is in marketing and linguistics, also studies what he calls the “market conversation”: the dialogue that unfolds around companies, consumers, and the brands that connect them. His current research focuses on the uses of AI analytics for studying that conversation. “I mostly study big data,” he said, referring to the modern study of human behavior based on very large data sets. “But I also have a qualitative toolkit.
“The market conversation is very cultural at heart,” he explained. “It happens in a cultural context. It can be really tempting to take a dataset and just crunch the numbers. But we’re talking about people. When we’re talking about the consumer conversation, things happen in a context – always. Who said that? When? Why? In what context? To whom?”
In other words: how consumers see brands, and conversely, how companies understand their customers, are both products of an ongoing dialogue between them. And, like any dialogue, the market conversation unfolds in the assumptions, habits, and beliefs that characterize particular people in a particular community at a particular point in time – that is, in all the messiness of human communication.
How apt are large language models to capture the subtle nuances of human speech, or the unique meanings that attach to specific words for a given community? Amidst all the enthusiasm for AI, Luri insists on the importance of not losing sight of the human element – the inherently contextual nature of communication.
“All that matters. It’s important not to abstract away from all that reality.”
As with generative AI, buzzwords swirl around the emerging field of neuromarketing. According to the Harvard Business Review, this new field “studies the brain to predict and potentially even influence consumer behavior and decision making.” One can easily imagine the value of this technology for businesses.
Such is the potential, but how does it work in practice? Most importantly, what are the real advantages and limits of the technology?
“With neuromarketing, I always say it’s a supplement, not a replacement, for traditional marketing research,” said Jennifer Tatara, Assistant Professor of Marketing at Driehaus.
A computer in the Beta Hub equipped with neuromarketing research software
Tatara works at the cutting edge of the neuromarketing field, which mines insights from psychology, marketing, and economics to look at the science behind consumer decision-making. Neuromarketing introduces biometric data into the study of consumer behavior, assisting researchers with an age-old question: what motivates people to make the decisions they do?
Marketing researchers and professionals, of course, are interested in a specific subset of people: consumers.
“We can use these tools to see into the decision-making process in a different way,” said Tatara, “to get into the black box of decision-making. We need a wide range of tools to get the full picture. But without biometric tools, you’re missing a piece of that picture.”
Some of these tools might be familiar. Electroencephalograms, for instance, gauge mental activity by tracking the small electrical impulses given off by the brain in response to stimuli. But some are more exotic: eye-tracking software yields insights into where consumers’ focus is drawn. Galvanic skin response, a measure of minuscule amounts of sweat, correlates with subjects’ emotional arousal.
Tatara emphasizes the potential of these tools to help both consumers and businesses make better, more satisfying decisions. But she is also quick to deflate the exaggerated claims sometimes made on its behalf, and to point to the ethical dimensions of this new field. As she puts it, “there’s no magical ‘buy’ button in the brain – this isn’t mind control.”
“Like with any new tool, there are ways to use it positively and ways to use it negatively. As marketers with access to these tools, it’s our job to make sure we’re not only selling, but we’re also helping. With a better understanding of how people make decisions, we can help them make better decisions.
“That’s why I’m happy that DePaul is taking an active role in teaching these tools. Here, faculty and students study and apply these tools; we’re doing it ethically, and we’re doing it to help consumers, at the end of the day.”