Microsoft UX Researcher Transitions to AI Leadership Role

The moment things shifted

Priyanka Kuvalekar's path into technology was not straightforward. Her formal education was in architecture, a discipline that taught her to think about space, design, and how people interact with built environments. That foundation would prove more relevant to her later work than she might have anticipated at the time. When she joined Microsoft, she did not arrive as a computer scientist or engineer. Instead, she brought with her the architectural mindset—an understanding of how design decisions shape human experience.

At Microsoft, Kuvalekar pivoted toward user experience research. This was a deliberate shift from her original training, but one that built naturally on her existing skills. UX research, like architecture, is fundamentally about understanding how people engage with systems and spaces. The transition marked the beginning of her focus on a question that would define her career: how do we ensure that technology actually serves the people who use it?

What they tried

As a UX research lead at Microsoft, Kuvalekar developed expertise in evaluating how users interact with products and systems. Her work centred on understanding user needs, pain points, and the practical realities of how technology functions in people's lives. This research-driven approach became her methodology—grounded in observation and data rather than assumption.

From this foundation in UX research, Kuvalekar took on a new challenge: leading AI research initiatives. Specifically, she led AI research for Microsoft Teams Calling, a product used by millions of people globally. This represented a significant transition from traditional UX research into the emerging field of AI systems research. The move required her to develop new expertise while maintaining the user-centred perspective that had defined her earlier work. She was now grappling with questions about how artificial intelligence could be developed and deployed responsibly within products that people depend on daily.

What worked, what didn't

Kuvalekar's approach to AI research has centred on continuous evaluation of AI systems. Rather than treating AI development as a one-time implementation, she emphasises the need for ongoing assessment—testing how these systems perform, how they affect users, and where improvements are needed. This reflects her background in research methodology: the importance of measurement, feedback, and iteration.

Accessibility has emerged as a core focus within her AI work. This commitment to ensuring that AI systems are usable and beneficial for people with diverse needs represents a continuation of values established during her UX research years, but applied to a new domain. The technical challenges of building accessible AI systems are substantial, and her work addresses both the technical and human dimensions of this problem.

Her focus on user experience, accessibility, and practical evaluation of AI systems provides a model for others looking to transition into AI-related roles. — Redmond Today, 2026

What they'd tell someone else

Kuvalekar's career trajectory offers a particular lesson about how diverse educational backgrounds can contribute meaningfully to AI development. She did not follow the conventional path of computer science degree leading to technology career. Instead, her architecture training provided her with a different lens—one focused on how humans inhabit and navigate systems. When she moved into UX research and later into AI research, she brought this perspective with her.

Her experience suggests that the field of AI benefits from people who come from different disciplines and bring different ways of thinking. Architecture taught her to consider design holistically. UX research taught her to listen to users. Both prepared her for the work of developing AI systems that function well in practice, not merely in theory. The adaptability required to move from architecture to UX research to AI leadership—learning new skills, acquiring new knowledge, remaining open to new challenges—appears to have been as valuable as any single specialisation. For others considering a transition into AI-related work, her path demonstrates that the route does not need to be direct, and that bringing a different background to the field can be a strength rather than a limitation.