Teaching AI during the AI revolution
By Andy Fano
Anthropic CEO Dario Amodei has warned that AI could wipe out 50 percent of entry-level jobs within five years. If that's the case, what's the point in preparing for a career in knowledge work? What's the point of an AI-focused MBA program?
In 2022, ChatGPT didn't exist and “prompt engineer” just meant a coder who showed up on time. That was the year I became director of Northwestern's MBAi program, a joint-degree program offered between the Kellogg School of Management and the McCormick School of Engineering.
At that time, applied AI typically meant applications like churn prediction, fraud detection and marketing optimization. In just a few short years, the public’s view of AI has changed dramatically. Our students are now entering a labor market full of genuine uncertainty. What can anchor a program in that environment?
I wrestled with versions of this question for most of my career before joining Northwestern. At Accenture Labs, we focused on applying emerging technology to real client problems. The technologies kept changing. The problems didn’t: How do you satisfy customers? How do you improve quality? How do you improve efficiency?
Technology was never the solution itself. It was a tool that changed the economics of a problem and forced us to rethink not just the technical approach, but the business process, the incentives, the metrics and the expectations of workers and customers.
The best work was always grounded in client realities, not extrapolations of tech trends. The same holds for MBAi students today.
“You'll gain the technical knowledge and business acumen to stand out in the marketplace. You'll also get the experience and confidence to lead within and among organizations looking to utilize AI to make a difference in the world.”
There’s no shortage of predictions and I don't know which will prove right. The goal of the MBAi program is to train business leaders with the technical proficiency to engage both the C-suite and AI engineering teams. What the program offers is hands-on experience with the theory and practice of solving real problems. We do that with clinical faculty who work in industry, executives who share their business challenges and direct engagement with partner companies through our capstone program.
It's access to first-hand problem-solving like this that prepares students for what they'll actually face. Questions like:
- How do you ensure a customer gets answers based on the right product information when hundreds of products look similar?
- How do you optimize a sugarcane harvester's performance across different climate conditions?
- How do you let employees use LLMs without putting your IP at risk?
These are real questions MBAi students have tackled with partner companies.
Ethics questions come up, too. We had one student discover that a company’s supposedly anonymized dataset might contain personally identifiable information. The student had to muster the courage to raise the concern with the company — a potential future employer. What he gained from the experience wasn’t just technical insight. It was the opportunity to experience and address an ethical challenge as it occurred, not after the fact, while debating abstract hypotheticals from a safe distance.
The company was grateful when the student raised the issue, although it turned out to be a false alarm. Still, the importance of creating a culture that is welcoming of such warnings is timeless, even if the risks we need to be mindful of are being redefined daily by the latest AI developments.
That mindset is what students carry forward.
A former MBAi student, who is now a senior product manager, recently shared how AI coding tools hadn’t eliminated her role — it had instead helped reframe her position. Fewer over-specifying requirements, more clarifying intent. Tighter feedback loops. More focus on ecosystem integration.
That reframing is not something she read about in a book. But she was comfortable making the shift because of her knowledge and past experiences.