As artificial intelligence continues to reshape how organizations interact with data, industry leaders are increasingly focused on bridging technical innovation with real-world application. Sarah Nagy, founder and CEO of Seek AI, has spent her career at the intersection of data science, machine learning, and entrepreneurship—most recently through Seek AI’s acquisition by IBM in 2025.
Nagy now works closely with IBM on initiatives that connect industry practice with education and early-stage innovation. As part of this work, she recently attended an IBM-affiliated student pitch competition hosted at Vanderbilt University, where students presented AI-driven startup ideas developed through a Data Science Institute capstone experience. In a conversation with Vanderbilt’s Data Science Minor Communications Intern Rosie Feng (’26), Nagy reflected on her career path, evolving perspectives on data practice, and what she believes sets future AI leaders apart.
Learning to Think Like Builders
For Nagy, one of the most exciting aspects of the IBM pitch competition was seeing how quickly students adapted to business-oriented thinking. This area can feel unfamiliar to those with primarily technical backgrounds.
“Most of the apps and products students use are consumer-facing,” she explained. “Concepts like B2B sales aren’t always intuitive at first.” Despite that, Nagy was impressed by how effectively students absorbed new ideas around market fit, pitching, and customer needs.
Working alongside IBM Ventures, students presented mock startups in a pitch format similar to Shark Tank. According to Nagy, the Ventures team noted that many of the student companies closely resembled real startups they encounter in industry—an encouraging sign of how well the course translated theory into practice.
A Career Shaped by Interdisciplinary Thinking
Nagy’s path into AI reflects the interdisciplinary nature of the field itself. Beginning in physics, she transitioned into quantitative finance—a mathematically rigorous discipline that later converged with data science and machine learning. As these tools evolved, so did her work.
“In 2015 and 2016, machine learning and data science were still relatively new terms,” she said. “By 2020 and 2021, AI had come to mean large language models.” Today, her focus has shifted again toward AI agents, which she sees as the next major stage of development.
Despite rapid changes in terminology and applications, Nagy emphasized that the underlying technical foundation remains approachable. “At the end of the day, it’s all coding in Python,” she noted. “That makes it doable to stay on top of the field as it evolves.”
Rethinking What Good Data Practice Means
Founding Seek AI also reshaped Nagy’s perspective on data quality. While clean, accurate data is a well-known ideal, she pointed out how difficult it can be to achieve in real organizational settings.
In many businesses, data entry depends on busy employees—such as sales teams—who may not prioritize detailed documentation amid packed schedules. This can introduce inconsistencies early in the data lifecycle.
Nagy sees AI as a powerful tool for addressing this challenge. AI systems that assist with tasks such as meeting monitoring and automated data capture can reduce human error at the source, leading to cleaner, more reliable datasets over time.
What Makes a Strong AI Pitch
When evaluating startups—or student projects—Nagy looks for what she calls the “three Ts”: team, technology, and traction.
Team refers to why a group is uniquely positioned to solve a particular problem. Technology asks whether the solution is defensible, novel, or newly possible. Traction considers market size and early customer validation. During the capstone course, Nagy noted that several student teams demonstrated real traction, including engagement with actual customers—an uncommon but impressive achievement at the undergraduate level.
Challenging Misconceptions About AI Accuracy
One of the most common misunderstandings Nagy encounters is the expectation that AI tools should be perfectly accurate. She finds this standard inconsistent with how human analysts are evaluated.
“In my years as a quant and data scientist, no one ever asked if I was 100 percent accurate,” she said. Instead of seeking perfection, she encourages organizations to assess AI based on benchmarks and transparency—particularly whether the system can clearly communicate the steps it took to conclude.
Advice for Aspiring AI Entrepreneurs
For students considering entrepreneurship, Nagy’s advice is simple: start now.
“This is the easiest time in history to become an entrepreneur,” she said. With accessible tools, open-source models, and rapidly expanding AI infrastructure, the barriers to entry are lower than ever. Whether or not success comes immediately, Nagy believes the experience itself is invaluable.
“Why not take the shot?” she added.