Coffee Chat #30: From Class to Industry: How to Better Support Finance Students enter the Workforce?

‍Q: How has your professional experience shaped the way you design learning environments and guide students in the classroom?

A: What has shaped my career more than anything was starting out at NICE, a large global software organization. Early in my career, I had the opportunity to work with teams across the U.S., Europe, Asia, and South America, and that experience fundamentally changed the way I think about collaboration. Working closely with colleagues from different cultures taught me the value of listening deeply, staying curious, and appreciating how diverse perspectives strengthen products, teams, and ultimately organizations. It also showed me how powerful it is when people with different backgrounds come together to solve complex problems. 

After eight years at NICE, I wanted to continue expanding my global perspective, which led me to pursue my MBA at Harvard Business School. Learning alongside classmates from different industries, countries, and professional experiences reinforced the idea that diversity isn’t simply something to acknowledge, it’s something to intentionally leverage, especially in learning environments. 

Those experiences directly shape how I design my classroom today. I strive to create an environment where every student feels comfortable participating, asking questions, and contributing their unique perspective. I remind students that everyone enters the room with different experiences, strengths, and ways of thinking, and that diversity is one of the greatest assets any project team can have. 

Whether students are learning financial modeling for the first time, exploring product management, or applying business frameworks in a new context, I encourage them to learn not only from me but from one another. My goal is to help them build confidence while creating an environment where diverse ideas are welcomed and challenged constructively. 

One way instructors can help students begin developing this mindset, even before they enter the workforce, is by encouraging them to collaborate with classmates who approach problems differently, whether because of another academic discipline, professional background, or culture. Those experiences expose students to different ways of thinking and prepare them for the collaborative nature of today’s workplace. 

I also encourage students to follow global companies and observe how they adapt to different markets. Starbucks, for example, offers different menus across countries, while Netflix tailors its content and user experience by region. Spotify recommends different playlists and features based on local listening habits in countries such as Brazil, India, and Germany. These examples help students recognize that business decisions rarely exist in isolation, they’re shaped by customers, culture, and context. 

Even simple habits like asking thoughtful questions, staying curious about others’ experiences, and actively listening during group discussions help students build the adaptability and collaboration skills employers increasingly value. 

Q: Finance can be too theoretical sometimes – what teaching approaches help students apply concepts to real business decision-making?  

A: I’m a big believer that students learn best when they can clearly see how academic concepts appear in real business situations. Finance can feel abstract when it’s taught only through theory, so I make a deliberate effort to connect every concept to practical examples. 

Whenever I introduce a new topic, I pair it with a story from my own experience, whether from NICE, RapidSOS, BCG, Laguna Health, or Zafran Security. I share the challenges we faced, the decisions we made, what worked, and what I would approach differently today. Those stories help students understand not just what happened, but also why those decisions were made. 

I also believe instructors should give students opportunities to experiment with practical tools rather than simply discussing them. For example, when we explore how to evaluate a new product opportunity before making a significant investment, I introduce students to Base44, a generative AI platform that helps teams quickly generate, refine, and test early product ideas. During class, students experiment with the tool in real time, evaluate its outputs, and discuss practical ways to validate those ideas with potential customers. 

This hands-on approach helps students connect theory to practice, build confidence, and produce work that feels meaningful. Rather than viewing themselves as students completing an assignment, they begin thinking like business professionals solving real problems. 

One of the clearest indicators that students have moved beyond theory is the quality of the questions they begin asking. Instead of focusing on formulas alone, they start challenging assumptions behind their TAM calculations – which customer segments should be included, what penetration rates are realistic, or how to define a serviceable market. 

Another clear sign is when students return after reviewing a company’s 10-K with thoughtful questions about revenue recognition, product segmentation, competitive positioning, or business risks. Those conversations demonstrate that they’ve begun thinking like business leaders rather than simply completing an academic exercise. 

Q: What challenges do students typically face when applying finance concepts, and how can instructors better support them through these obstacles?‍

A: Students often struggle when they’re asked to move from understanding financial concepts to applying them in practical business situations. One of the biggest challenges is building financial models that reflect real business dynamics rather than simply completing spreadsheet calculations. 

For many students, it’s difficult to estimate software development costs, understand operational expenses, or translate different pricing strategies into realistic revenue projections. 

When we work through these challenges together, we step back and examine the complete business picture. We discuss cloud infrastructure costs, AI tokens costs, engineering salaries, software tools, ongoing maintenance, and other operational expenses that influence product economics. On the revenue side, we explore how subscription models, tiered pricing, feature bundling, and other monetization strategies affect customer behavior and long-term growth. 

One of the most valuable things instructors can do is help students break complex problems into smaller, manageable decisions instead of trying to build a complete financial model all at once. 

When students become overwhelmed, I encourage them to start simple and gradually add complexity. We begin by identifying the target customer, major cost drivers, and primary revenue streams before introducing additional assumptions. From there, we stress-test those assumptions, refine the model, and evaluate whether the overall logic makes sense. 

This structured, iterative approach helps students build confidence while reinforcing an important lesson: strong financial models are developed through thoughtful analysis and continuous refinement, not by getting everything right on the first attempt. 

By working through realistic business examples together, instructors can help students better understand uncertainty, strengthen their judgment, and develop models that reflect how financial decisions are actually made in industry. 

‍Q: GenAI is transforming the finance landscape—based on what you’re seeing employers expect, how should instructors and professors address this topic in the classroom?  

A: Generative AI is one of the most transformative trends shaping the future of finance, and I believe students should begin developing familiarity with these tools early. The finance industry is becoming increasingly data-driven, and GenAI is already helping professionals analyze large datasets, identify trends, automate repetitive work, and generate draft financial analyses or market research summaries in seconds. These tools don’t replace critical thinking, but they significantly expand what one person can accomplish. 

Students don’t need to become AI engineers, but they do need AI fluency. They should understand what these tools do well, where they’re reliable, and where human judgment remains essential. 

I encourage students to begin with practical, low-risk applications such as summarizing financial reports, developing TAM methodologies, comparing pricing strategies, or brainstorming assumptions for financial models. As they become more comfortable, they can use GenAI to challenge their own thinking, for example, asking it to critique revenue assumptions or propose alternative cost structures. 

At the same time, instructors should emphasize that AI outputs should never be accepted at face value. Students need to develop the habit of validating information, questioning assumptions, and using GenAI as a thought partner rather than a replacement for critical thinking. 

Ultimately, the students who will stand out are those who learn to combine these technologies with strong business judgment. The real advantage isn’t using GenAI to get answers, it’s using GenAI to ask better questions and make more thoughtful, data-informed decisions.