Nan P’s journey into AI and product management is an inspiring example of how non-technical backgrounds can successfully transition into the technology sector, especially in AI-driven roles. Originally studying international studies and education, Nan’s career began with an education startup in Vietnam that focused on international exchange programs. Her role there was multifaceted, involving program measurement, business analysis, and eventually product management. Despite her non-technical origin, Nan’s passion for sustainability and youth empowerment motivated her to pursue a master’s degree in Transition Innovation and Sustainability Management across multiple European universities, including University College Dublin (UCD).
During her master’s, she was introduced to AI and data analysis, learning foundational Python and machine learning concepts through patient, supportive professors. This exposure sparked her interest in AI, particularly responsible AI and diversity issues within the field. Nan completed an internship in Health Tech product management, which merged her diverse skills—business, marketing, design, data analysis—and deepened her AI knowledge.
Currently, she works as a senior product owner at a healthcare technology company, focusing on AI challenges and bridging business needs with engineering solutions. Nan acknowledges the challenges she faced as a woman in a predominantly male, technical environment, especially coming from a non-technical background. She highlights the importance of role models, support systems, and a growth mindset to overcome these barriers.
Nan notes that the education system in Ireland is comparatively supportive of female integration into STEM and ICT roles, featuring more gender-balanced classrooms and initiatives encouraging female participation. She emphasizes the necessity of fundamental technical skills while recognizing the rapidly evolving tech landscape means continuous learning is essential for everyone, regardless of their background.
Support structures such as diversity committees within companies and grassroots organizations like Women AI and Python Ladies provide mentorship, networking, and advocacy, which are invaluable for women entering tech. Nan advises young women interested in AI to seek role models, leverage online resources, build confidence, and embrace their unique backgrounds as assets in developing responsible and inclusive AI products.
Highlights
- 🚀 Nan transitioned from international studies to AI product management, showcasing diverse career paths.
- 🎓 Her master’s program across Europe provided interdisciplinary education blending sustainability, innovation, and AI.
- 🤖 Nan learned AI fundamentals despite a non-technical background, highlighting accessible learning opportunities.
- 👩💻 She currently bridges business and engineering as a senior product owner in healthcare AI.
- 💪 Nan faced gender stereotypes but overcame them through mentorship and self-belief.
- 🇮🇪 Ireland’s education system supports female STEM integration with relatively balanced gender representation.
- 🌐 Women-focused tech communities and mentorship programs play a crucial role in female empowerment in AI.
Key Insights
- 🌱 Nan’s academic journey across sustainability, innovation, and AI shows that non-technical fields can provide a strong foundation for AI careers. This broad knowledge base supports the development of AI solutions that are ethically sound and sustainable. It suggests AI education benefits from integrating social sciences and ethics alongside technical training.
- 🤝 Nan’s story underscores how seeing and interacting with female role models in AI and tech can transform confidence and career trajectories. Having mentors who understand the unique challenges faced by women—and especially those from non-technical backgrounds—provides encouragement, reduces isolation, and fosters persistence. Organizations should prioritize mentorship programs to retain diverse talent in AI.
- 💻 Despite initial intimidation, Nan was able to learn programming and machine learning fundamentals through university modules and self-study via online platforms like YouTube. This highlights the democratization of tech education and the critical role of patient, inclusive teaching. It also points to the need for continued expansion of accessible, beginner-friendly AI education resources.
- ⚖️ Nan illustrates the subtle biases women face in AI and tech, such as assumptions about technical competence. However, she also notes growing awareness and active diversity efforts within companies and industries. This mixed reality calls for ongoing cultural shifts, transparency in hiring and promotion, and visible support networks to create truly inclusive workplaces.
- 🌍 Nan’s dual background in social sciences and tech positions her uniquely to address challenges like cultural diversity in generative AI and responsible AI development. The AI field benefits from diverse perspectives to build products that are user-centric, ethical, and socially responsible. Encouraging individuals with varied skill sets, including marketing, psychology, and sustainability, enriches AI innovation.
- 📚 Nan emphasizes that no one can master all aspects of technology due to its rapid evolution. Even engineers with strong computer science backgrounds must continuously upskill. This means aspiring AI professionals should focus on solid fundamentals while cultivating adaptability and a growth mindset to stay relevant in the fast-changing field.
- 🌐 While some companies have internal diversity committees, external organizations like Women AI and Python Ladies provide crucial industry engagement, workshops, and mentorship. These grassroots movements empower women by building community, sharing knowledge, and advocating for gender equality in tech. Expanding such networks can accelerate female participation in AI.