“If you don’t understand how to use AI effectively, you risk being replaced by colleagues who do,” says Professor Andreas Park.
Professor Andreas is a professor of finance at the University of Toronto’s Rotman School of Management and research director of its Financial Innovation Lab. He has advised Canada’s securities regulators and co-founded the University of Toronto’s blockchain research lab.
We spoke to him to understand what this shift means for MBA students preparing to enter the field.
How to prepare for an AI-driven finance career
An MBA is a rare chance to pause and reflect on your career. For students interested in finance, it’s also the right moment to build confidence in a field that’s evolving fast.
While tools such as ChatGPT have captured public attention, many of the biggest changes in finance are happening behind the scenes—in the way institutions analyze data, assess risk, and make decisions.
“You can use machine learning tools to analyze data to get a better sense in real time of how the economy is moving and make predictions about where it’s going. This has been evolving over the last decade,” Professor Andreas (pictured) explains.
Rotman students are encouraged to engage with these developments both in theory and in practice. Based in Toronto, Canada’s financial capital, Rotman offers nearby access to major financial institutions—including the headquarters of all of Canada’s Big Five banks. North American banks currently lead the way in AI adoption.
To help students translate that exposure into career growth, Rotman offers personalized career support, including one-on-one coaching, technical interview prep, student clubs, and industry-specific networking events.
Why human judgement still matters
AI tools are fast and efficient, but they’re less effective in unfamiliar situations that require context, or human judgement.
“When something genuinely new comes along, there’s no data to predict what the outcome of that will be. You still need a person to consider possible scenarios and judge what’s most likely,” says Professor Andreas.
In practice, using LLMs to automate a task often comes down to three steps: understanding the problem, generating the code or solution, and then debugging it. While LLMs can handle much of the second and third steps, the first—defining the problem and breaking it down—still depends on the human behind the tool.
“You spend 10% of the time thinking about what you want to write, another 10% writing the code, and 80% debugging it. The trick is: the hard part is actually the first 10%. That’s the key differentiator.”
Recognizing this, the Rotman MBA encourages students to gain hands-on experience with AI tools through assignments that mirror real-world cases.
Instead of treating tools such as ChatGPT as off-limits, students are given the chance to explore how they can be used to support analysis or speed up workflows—just as financial institutions are now experimenting with their own internal LLMs.
“Anyone can use ChatGPT to get information, but when you actually have to do an analysis and solve a problem using these tools, that’s quite challenging,” says Professor Andreas.
“Even when the problems are complex or math-heavy, we want students to be able to process the challenge, choose the right tools, and apply them.”
What skills do professionals need for the future of finance?
These changes mean finance professionals increasingly need to be comfortable using tools such as Python, ChatGPT, and machine learning applications—not to build AI models themselves, but to frame problems, understand outputs, and apply these tools effectively in context.
It’s this kind of practical fluency that the Rotman MBA aims to build.
Ranked Canada’s best MBA by the Financial Times, the program gives students practical exposure to AI tools in finance, including real-world use cases such as risk modeling and data analysis.
One such option is in Machine Learning and Financial Innovation, an elective that covers the fundamentals of machine learning, with applications across risk modeling and investment analysis.
Even students without a technical background build foundational skills in Python—a language widely used across the finance sector for tasks such as risk modeling, data analysis, and machine learning—and learn how to apply it in real-world business contexts.
“Demystifying AI is very important to us. We’re working on how we can train students in AI without being full stack developers,” says Professor Andreas.
This kind of applied knowledge can be particularly valuable for smaller or mid-sized firms, he adds, where MBAs could help build practical internal tools—without the cost of hiring specialist developers.
While some areas of finance—such as client-facing roles—are slower to adopt AI due to regulatory and data sensitivity concerns, demand for professionals who can apply these tools responsibly and effectively is growing fast.
For students planning for finance careers, the takeaway is simple. The technology will keep evolving, but the demand for people who can think clearly, work hands-on, and solve problems is not going away.
“What MBA students learn is that, in the workplace, you need to be somebody who stays useful—and that means being able to solve problems,” Professor Andreas concludes.
