Flywire Innovation, Technology & Agility

What’s your professional or academic background? How did you break into the tech industry?
I grew up fascinated by how things work, always wanting to take things apart and put them back together. That intellectual curiosity would follow me throughout my career. In college I chose a degree in finance that helped land my first job at a financial services company. This was during the dot-com boom and I took a QA role working on a new pricing platform replacing a legacy COBOL solution. I loved understanding software principles while finding ways to break the software to help developers build better solutions. At that point, I knew I wanted to focus my career on technology.

From 1998 to 2000, I learned Java, HTML and JavaScript through continuing education. I transitioned to web development but often led QA initiatives due to my expertise. This sparked my automation-first principles — I wrote routines to automate test cases across different servers and browsers, building rule engines that generated reports with developer recommendations. This foundation in automated solutions became my launching pad for AI and process optimization.

How did you learn how to use AI, and how do you apply it to your work?
My AI learning journey really began around 2010 through working with data science teams and online training via Udemy and Coursera. I’ve learned extensively through peers and their personal innovations, attended meetups and conferences to expand my learning — developer conferences for art of the possible, business conferences for pressure-testing AI use cases. While I’ve benefited through formal training, I believe in learning by doing and that is usually done best on the job. In 2014, our focus at my former employer was shifting toward modern data architectures leveraging technologies like Hadoop and Spark where we could start to perform machine learning at scale and in real time for use cases like streaming analytics and sentiment analysis. Then in 2017, augmented analytics emerged with NLG, NLQ and intelligent insights, so it was at that time that we started producing insight narratives leveraging AI for clients.

ChatGPT’s 2023 arrival felt like AI finally arrived in full force. Today at Flywire, I personally use gen AI throughout my day for communication, presentations, documentation, brainstorming and research, always verifying outputs while adding my personal touch.

What do you consider the greatest benefit of leveraging AI? How has it positively impacted the work you produce as a whole or your career?
AI’s greatest gift is leverage — automating repetitive tasks to free human creativity. In our always-on world, we need more time for priorities. AI delivers that promise, creating workforce productivity equivalent to entire FTEs.

Time liberation: AI handles non-value-add manual activities. A colleague’s philosophy resonates: “I ask myself what I don’t want to do today, then build AI to do it.” This reframes AI as liberation, not replacement, freeing us for strategic thinking and creative problem-solving.
Rapid prototyping: Teams can build functioning demos in hours versus weeks. Product managers create powerful prototypes without developer resources, transforming stakeholder conversations from theoretical to practical evaluations.

Scalable architecture: Embedding AI directly into the data management process  —  intelligent data quality, auto-scaling, automated modeling are a few examples. At Flywire, we’re modernizing capabilities to create leverage through AI agents, enabling smaller teams to manage complex systems while delivering better stakeholder outcomes.