Priyanka Kuvalekar, a 31-year-old UX research lead at Microsoft in Redmond, Washington, has carved out a successful career in AI despite having no traditional tech background. Kuvalekar joined Microsoft in April 2025 as a senior UX researcher, leading research for Microsoft Teams Calling and related AI experiences.
Kuvalekar’s journey began with a five-year architecture degree in India, far removed from computer science or AI. After working as a junior architect, she made a pivotal decision to transition into the digital world. She enrolled in a three-month user experience course, which led to a master’s degree in user experience and interaction design in Philadelphia starting January 2018.
Her career progression included an internship at Korn Ferry that converted to a full-time role until 2021, followed by breaking into Big Tech at Cisco in October 2021. At Cisco, she spent over 3.5 years as a UX research lead, working on AI features for Webex meetings and messaging—her entry point into AI product work.
To build AI competency, Kuvalekar pursued certifications and training through her employer and independently, focusing on generative AI, agentic AI design patterns, large language models, and evaluating AI experiences. She utilized platforms like Google Skills, Microsoft Training, and DeepLearning.AI to understand how generative AI could be applied to her projects.
Kuvalekar shares three critical lessons from her journey: First, AI requires continuous evaluation—it’s never “done” and needs ongoing assessment to deliver trustworthy experiences. She designed qualitative studies examining how AI conversations perform across diverse user groups, uncovering inconsistencies in tone, meaning interpretation, and pacing. Second, AI can lower barriers or create new ones, particularly regarding accessibility. She learned to include people with disabilities in AI research and evaluate how AI integrates with assistive technologies like screen readers. Third, fluency matters more than technical depth—understanding AI well enough to bridge technical teams and user needs is more valuable than building the technology yourself.
Her advice for breaking into AI without a tech background: start where AI meets people, not code. Focus on shaping what “quality” means for AI features by asking questions about scope, interruption handling, and inclusivity across languages and dialects. She recommends building a portfolio around “AI-plus-people” that showcases frameworks, evaluation studies, and how insights influenced decisions.
Key Quotes
AI isn’t something you test once, and then it’s ‘done.’ It requires ongoing evaluation to ensure it continues to deliver trustworthy experiences.
Priyanka Kuvalekar explains her first major lesson about working in AI, emphasizing the continuous nature of AI evaluation. This insight is crucial as it challenges the traditional software development mindset and highlights why UX researchers are essential to AI product teams.
AI can make tasks easier and reduce barriers for people with disabilities — for example, by automating steps. It can also create new inequities if not designed with accessibility in mind.
Kuvalekar discusses the dual nature of AI’s impact on accessibility, demonstrating her understanding that AI systems require intentional inclusive design. This perspective is increasingly important as AI becomes embedded in everyday tools and services.
You don’t have to build the technology yourself to make an impact, but you do need to understand it well enough to engage with it.
This quote encapsulates Kuvalekar’s philosophy on breaking into AI without a technical background. It offers encouragement to professionals from non-traditional backgrounds while emphasizing the importance of developing AI fluency to contribute meaningfully to product development.
Start where AI meets people, not where AI meets code — focus on how AI shows up in products and how people experience it.
Kuvalekar’s advice for others looking to break into AI highlights a strategic entry point that leverages human-centered skills rather than requiring deep technical expertise. This approach positions UX and product professionals as essential contributors to AI development.
Our Take
Kuvalekar’s success story reveals an important truth about the AI industry: technical expertise alone cannot create successful AI products. As AI systems become more sophisticated, the gap between what’s technically possible and what’s actually useful or trustworthy widens. Her architecture background likely provided valuable skills in systems thinking and user-centered design that translate well to AI product development.
What’s particularly noteworthy is her emphasis on accessibility as a core AI consideration, not an afterthought. This perspective is often missing from AI development teams dominated by engineers and data scientists. Her approach to continuous evaluation also addresses a critical weakness in many AI deployments—the assumption that AI systems are static rather than dynamic.
The timing of her career trajectory is also significant. She entered AI work at Cisco just as generative AI was gaining momentum, positioning herself perfectly for the explosion of AI features across enterprise software. Her move to Microsoft in 2025 suggests strong demand for professionals who can humanize AI experiences.
Why This Matters
This story is significant for the AI industry because it demonstrates that diverse backgrounds can be assets rather than barriers in shaping responsible AI development. As AI becomes increasingly integrated into consumer and enterprise products, the need for professionals who can bridge the gap between technical capabilities and human experience becomes critical.
Kuvalekar’s journey highlights a crucial skills gap in the AI industry: the ability to evaluate AI systems through a user-centric lens. While many focus on building AI models, fewer professionals specialize in ensuring these systems are trustworthy, accessible, and inclusive. Her emphasis on continuous evaluation and accessibility addresses growing concerns about AI bias, reliability, and equitable access.
For businesses, this underscores the importance of multidisciplinary AI teams that include UX researchers, accessibility experts, and other non-technical roles. As companies like Microsoft invest heavily in AI features across products like Teams, the demand for professionals who can ensure these features meet real user needs will only grow. This story also provides a roadmap for career-changers looking to enter the AI field, suggesting that domain expertise in human-centered design may be more valuable than traditional computer science credentials in certain AI roles.