How an architect became a senior UX researcher at Microsoft working with AI
UX Research has established itself as one of the most strategic disciplines inside Big Tech companies, and the most interesting part is that not everyone working in this field started out studying technology. Priyanka Kuvalekar’s career path is a perfect example. At 31, she holds the title of senior UX researcher at Microsoft, where she leads research projects focused on Microsoft Teams Calling and related Artificial Intelligence experiences. What makes her story even more compelling is that her original degree is in architecture, a five-year program she completed in India that seemingly had no direct connection to the tech world. However, it was precisely that trained eye for understanding spaces, flows, and human behavior that turned out to be a competitive advantage when designing people-centered digital experiences.
Before joining Microsoft in April 2025, Priyanka followed a path that included an intensive three-month course on user experience, a master’s degree in UX and Interaction Design that she started in January 2018 in Philadelphia, and stints at companies like Korn Ferry and Cisco. At each of these stages, she intentionally built a solid repertoire in user research. For anyone thinking about switching careers and breaking into the Artificial Intelligence space without a computer science degree, the lessons she has shared publicly are pretty straightforward and applicable in everyday work 👇
From architecture to the digital world: how it all started
Priyanka kicked off her professional career during her final year of architecture school, doing a full-time internship as a junior architect. After graduation came that moment of reflection a lot of people know all too well: keep going down the path already laid out, or take a risk and pivot to the digital world? She chose the second option. The first concrete step was enrolling in a three-month user experience course, which served as a gateway to understanding the fundamentals of the field. That bootcamp sparked such a strong interest that it led her to pursue a full master’s degree in UX and Interaction Design.
With her master’s program underway in Philadelphia, her first professional experience in the field came through an internship as a UX researcher at Korn Ferry. After roughly a year of interning, Priyanka received an offer for a full-time position, where she stayed until 2021. The transition to Big Tech happened in October 2021, when she joined Cisco as a UX research lead. At Cisco, she spent more than three and a half years before accepting her current role at Microsoft. This gradual and well-planned trajectory is one of the most instructive aspects of her story, because it shows that successful career transitions rarely happen overnight.
The role of curiosity and empathy in UX Research
One of the first things Priyanka highlights about her career transition is that the foundation of UX Research isn’t about knowing how to code or mastering algorithms. It’s about cultivating a genuine curiosity for how people interact with products and services. When she was studying architecture, the focus was on understanding how human beings move through physical spaces, what makes them feel comfortable or uncomfortable, and how the design of an environment influences behavior. That same logic applies perfectly to the digital world, where the goal is to understand how users navigate interfaces, what frustrates them, and what makes an experience truly functional. Empathy, in this context, stops being just a nice-sounding value in a mission statement and becomes an essential work tool for anyone conducting user research inside any Big Tech company.
Another point she emphasizes is the importance of knowing how to ask the right questions. In UX Research, the quality of insights depends directly on the quality of the questions formulated during interviews, usability tests, and observation sessions. Priyanka says her architecture training taught her to observe before proposing solutions, and that habit translated remarkably well into the context of technology research. At Microsoft, for example, when working with Artificial Intelligence features in Teams Calling, she needs to understand not just whether a feature works technically, but whether it makes sense within the real workflow of the people who use the tool every day.
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Empathy also connects directly with accessibility, a topic that’s becoming increasingly central in product decisions at major tech companies. When a UX researcher conducts studies with users who have different needs and abilities, they help ensure the final product is genuinely inclusive, not just on paper. Priyanka mentions that working with Artificial Intelligence brings an extra challenge in this regard, because AI-powered features need to be understandable and usable by people with different levels of tech familiarity, different cultural contexts, and different physical and cognitive abilities. Ignoring these aspects means creating products that only work for a narrow slice of users.
The entry point into AI: real product work at Cisco
Priyanka’s immersion in the world of Artificial Intelligence didn’t start with abstract theory. It happened in a hands-on way during her time at Cisco, where she led projects focused on AI features for Webex meetings and messaging. As research lead, she needed to understand in practice how artificial intelligence capabilities impacted user experience in real-world usage scenarios.
Knowing she needed to grasp the mechanisms behind AI to do more robust research work, Priyanka pursued certifications and training both offered by the company and independently. These studies covered topics like generative AI, design patterns for agentic AI, large language models, and frameworks for evaluating AI-based experiences. On top of that, she explored courses and online resources from platforms like Google Skills, Microsoft Training, and DeepLearning.AI to understand how generative AI could be applied to her projects. This investment in continuous learning was key to her ability to bridge the gap between technical teams and user needs, a skill that would become central to her work at Microsoft.
Three fundamental lessons about AI for people coming from outside tech
Drawing from her experience moving between architecture, interaction design, and UX research at major tech companies, Priyanka shared three core takeaways that helped her build a solid career in Artificial Intelligence even without a traditional technical background.
AI requires continuous evaluation, not one-time testing
The first big lesson is that Artificial Intelligence isn’t something you test once and call it done. Unlike traditional software features, where behavior is deterministic and predictable, AI-based systems need constant evaluation to make sure they keep delivering reliable experiences over time. In practice, this meant Priyanka had to design qualitative studies that examined how AI-mediated conversations behaved with diverse groups of users. These studies revealed issues like tone inconsistencies, misinterpreted meanings, and problems related to the pacing of interactions. By identifying these friction points in the real world, she learned how to refine AI systems so they operate in a reliable and inclusive way.
AI can break down barriers, but it can also create new ones
The second lesson came from an accessibility perspective. Artificial Intelligence has enormous potential to simplify tasks and reduce barriers for people with disabilities, for instance, by automating steps that previously required manual effort. But if these features aren’t designed with accessibility in mind from the start, they can end up creating new inequalities instead of eliminating them. Priyanka learned that accessibility and AI can’t be treated as separate topics. It’s essential to include people with disabilities in AI research studies and to evaluate how these systems integrate with assistive technologies like screen readers and keyboard navigation. This perspective prevents technological innovation from excluding the very people who could benefit from it the most.
Fluency matters more than technical depth
The third lesson is perhaps the most liberating for anyone who doesn’t come from a computer science background: you don’t need to build the technology to make an impact on it. What matters is understanding enough to have productive conversations with technical teams and ensuring that AI quality evaluation is rooted in real user experience. For Priyanka, gaining that fluency meant understanding the concepts behind how large language models work, their limitations, and how to design evaluation frameworks that account for those limitations. This knowledge base helped her ask more relevant questions of engineers, conduct studies that measure trust, consistency, and reliability across different user groups, and work closely with product managers to define what success looks like for AI-powered experiences.
Artificial Intelligence and Interaction Design at Big Tech companies
The current landscape at Big Tech companies shows that Artificial Intelligence is no longer a siloed area within engineering departments. It’s woven into virtually every product aimed at consumers and enterprise customers alike, which means UX Research and Interaction Design professionals need to understand at least the fundamentals of how these systems work. In Priyanka’s case, working with AI in Teams requires her to understand how language models generate responses, what the limitations of these technologies are, and how users perceive and react to interactions mediated by artificial intelligence. This understanding doesn’t need to be at the level of a machine learning engineer, but it does need to be deep enough for the research to produce insights that are relevant and actionable for product and development teams.
Interaction Design gains an additional layer of complexity when it involves Artificial Intelligence, because the system’s behavior isn’t always predictable in the same way a traditional interface is. In a dropdown menu or a signup form, the user knows exactly what to expect when they click each element. But in a conversational interface powered by AI, like the Copilot features integrated into Teams, the response can vary depending on the context, the conversation history, and how the question was phrased. This creates unique challenges for those designing these interactions and for those researching how users react to them. Priyanka notes that this is one of the most fascinating areas of UX research right now, precisely because there are still so many open questions about how to make these experiences more natural, trustworthy, and transparent.
On top of that, accessibility needs to be considered from the very beginning of the design process when we’re talking about AI-based products. There’s no point in creating a sophisticated automatic meeting summary feature if it doesn’t work properly with screen readers, or if the information generated by the AI is presented in a way that makes it hard for people with cognitive disabilities to understand. Big Tech companies like Microsoft, Google, and Apple have been investing more and more in accessibility guidelines for Artificial Intelligence products, and UX Research professionals play a crucial role in this process by making sure tests include participants with diverse profiles and that the results of this research inform concrete design and engineering decisions.
Why a non-traditional background can actually be an advantage
Priyanka makes a point of highlighting that entering the AI field without a traditional tech background can actually be an advantage. Her recommendation is to start where AI meets people, not where AI meets code. The focus should be on how artificial intelligence shows up in products and how people experience those interactions in their daily lives.
One of the most practical ways to add value without a technical degree is to help define what quality means for an AI feature. Working alongside product managers to ask questions like: Is the AI operating within its expected scope? Does it handle interruptions well? Is it inclusive when it comes to different languages and dialects? These aspects are often overlooked when AI is viewed purely as a technical system, but they’re absolutely critical to user trust. Anyone who can translate these questions into actionable terms becomes an indispensable part of any product team.
Building a portfolio focused on AI and people
Another valuable piece of advice Priyanka shares is about the importance of building a portfolio that demonstrates the intersection of AI and human experience. According to her, hiring managers don’t just want to see that a candidate knows AI exists. They want concrete evidence that this person can shape technology responsibly and make it usable.
In practice, this means documenting every framework, rubric, and evaluation study you’ve conducted, along with clear examples of how insights generated by research influenced product decisions. Even people who haven’t yet had access to projects at large companies can conduct small-scale studies with publicly available AI tools and use those materials to demonstrate their reasoning and analytical abilities.
Priyanka also suggests volunteering for projects where AI is being integrated into existing tools and trying to answer questions like: What does this AI feature need to do? How should it behave? Can the AI explain what it is and isn’t capable of? Does it recover gracefully when it makes a mistake? These are exactly the kinds of questions that researchers, product thinkers, and professionals with non-traditional backgrounds are uniquely positioned to answer.
Practical lessons for anyone looking to switch careers
Priyanka’s experience brings some really concrete takeaways for anyone considering a transition into UX Research, especially within the context of Artificial Intelligence. The first is that a traditional academic background in technology is not an absolute prerequisite. What matters is the ability to demonstrate analytical thinking, communication skills, and a deep understanding of human behavior. People coming from fields like psychology, anthropology, sociology, journalism, and even architecture often possess competencies that translate remarkably well into the world of user research. The key is knowing how to connect those skills with the specific demands of the tech market and presenting that clearly in portfolios and interviews.
The second takeaway is about the importance of building practical experience gradually and strategically. Priyanka didn’t jump straight from architecture to a senior position at Microsoft. She invested in a three-month UX course to pick up the fundamentals, then deepened her studies with a master’s in UX and Interaction Design, and after that went through positions at Korn Ferry and Cisco before landing at a Big Tech company the size of Microsoft. Each stage served as a stepping stone that added new skills to her repertoire and strengthened her ability to take on increasingly complex projects. For anyone just starting this journey, the tip is not to try to skip steps, but to make the most of every opportunity to learn, make mistakes, iterate, and build a portfolio that shows real growth.
Finally, Priyanka reinforces that staying up to date on Artificial Intelligence and its applications in digital products is essential for any UX Research professional who wants to stay relevant in the market. Big Tech companies are in an intense race to integrate AI across all their platforms, and that’s creating a growing demand for researchers who can evaluate the impact of these technologies on user experience. Understanding concepts like accessibility in AI contexts, knowing how to conduct studies with conversational interfaces, and being familiar with the ethical discussions surrounding the use of language models are differentiators that make a real difference when it comes to landing strategic positions. The field is wide open, and professionals with diverse backgrounds have a lot to contribute toward making technology products more human, inclusive, and functional 🚀
