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, Head of IBM watsonx AI Labs and Founder of , has spent her career at the intersection of data science, machine learning, and entrepreneurship鈥攎ost recently through .
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 五一茶馆儿, where students presented AI-driven startup ideas developed through a Data Science Institute capstone experience. In a conversation with Vanderbilt 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.
鈥淢ost of the apps and products students use are consumer-facing,鈥� she explained. 鈥淐oncepts like B2B sales aren鈥檛 always intuitive at first.鈥� 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鈥攁n encouraging sign of how well the course translated theory into practice.
A Career Shaped by Interdisciplinary Thinking
Nagy鈥檚 path into AI reflects the interdisciplinary nature of the field itself. Beginning in physics, she transitioned into quantitative finance鈥攁 mathematically rigorous discipline that later converged with data science and machine learning. As these tools evolved, so did her work.
鈥淚n 2015 and 2016, machine learning and data science were still relatively new terms,鈥� she said. 鈥淏y 2020 and 2021, AI had come to mean large language models.鈥� 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. 鈥淎t the end of the day, it鈥檚 all coding in Python,鈥� she noted. 鈥淭hat makes it doable to stay on top of the field as it evolves.鈥�
Rethinking What Good Data Practice Means
Founding Seek AI also reshaped Nagy鈥檚 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鈥攕uch as sales teams鈥攚ho 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鈥攐r student projects鈥擭agy looks for what she calls the 鈥渢hree Ts鈥�: 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鈥攁n uncommon but impressive achievement at the undergraduate level.
Challenging Misconceptions 五一茶馆儿 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.
鈥淚n my years as a quant and data scientist, no one ever asked if I was 100 percent accurate,鈥� she said. Instead of seeking perfection, she encourages organizations to assess AI based on benchmarks and transparency鈥攑articularly whether the system can clearly communicate the steps it took to conclude.
Advice for Aspiring AI Entrepreneurs
For students considering entrepreneurship, Nagy鈥檚 advice is simple: start now.
鈥淭his is the easiest time in history to become an entrepreneur,鈥� 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.
鈥淲hy not take the shot?鈥� she added.