I'm a senior UX researcher at Microsoft. Here's how I broke into AI without a tech background — and 3 lessons I learned.

• Priyanka Kuvalekar leads UX research for Microsoft Teams Calling and related AI experiences.
• She transitioned from architecture to AI by pursuing a UX design education and Big Tech roles.
• Her insights focus on AI evaluation, accessibility, and bridging user experience with tech.

This as-told-to essay is based on a conversation with Priyanka Kuvalekar, a 31-year-old UX research lead at Microsoft in Redmond, Washington. It's been edited for length and clarity.

I joined Microsoft in April 2025 as a senior UX researcher. I lead research for Microsoft Teams Calling and related AI experiences.

My path to this job wasn't typical. I spent five years studying architecture in college, not computer science or AI, and earned my degree in India.

I started my career with a full-time internship as a junior architect while completing the final year of my degree. After I graduated, I began thinking seriously about my next steps. Should I continue as an architect or pivot into the digital world?

I decided to make the pivot

I enrolled in a three-month user experience course, which led me to pursue a master's degree in user experience and interaction design.

I moved to Philadelphia and began my master's program in January 2018. My first industry step was an internship as a UX researcher at Korn Ferry. After a year of interning, I was offered a full-time role, which I held until 2021.

I broke into Big Tech at Cisco in October 2021 and worked as a UX research lead for over 3 1/2 years before taking my current position at Microsoft.

My entry point into AI began with real product work at Cisco

I led projects focused on AI features for Webex meetings and messaging as the lead researcher.

I knew I needed to understand AI's mechanics, so I pursued certifications and training, both through my employer and independently, on generative AI, agentic AI design patterns, large language models, and evaluating AI experiences as a researcher.

I also explored courses and online resources on UI design from platforms such as Google Skills, Microsoft Training, and DeepLearning.AI to understand how generative AI could be applied to my projects.

Here are the three biggest lessons I learned that helped me break into AI with a non-tech background.

1. AI requires continuous evaluation

I learned that you need to understand how to evaluate AI in practice. AI isn't something you test once, and then it's "done." It requires ongoing evaluation to ensure it continues to deliver trustworthy experiences.

This meant designing qualitative studies that examined how AI conversations hold up across diverse user groups. Inconsistencies in tone, misinterpretations of meaning, and pacing issues were revealed in this research.

By uncovering real-world friction points, I learned how to refine AI systems to work reliably and inclusively.

2. AI can lower barriers or unintentionally create new ones

Another key lesson came from approaching AI through an accessibility lens. 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.

Accessibility and AI can't be separated. I've learned to include people with disabilities in AI research and to evaluate how AI integrates with assistive technologies such as screen readers and keyboard navigation.

3. When upskilling on AI, fluency matters more than technical depth

Breaking into AI also taught me that 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.

It's about learning just enough to bridge the gap between technical teams and user needs, and to ensure that how you gauge AI quality is rooted in real user experience.

Gaining fluency meant understanding the concepts behind how large language models work, their limitations, and how to design evaluation frameworks that account for those limits.

This has helped me ask engineers the right questions and design studies that measure trust, reliability, and consistency across different user groups. It's also helped me work closely with product managers to define what success looks like for AI experiences.

Breaking into AI without a traditional tech background can be an advantage

I recommend starting where AI meets people, not where AI meets code — focus on how AI shows up in products and how people experience it.

One of the most practical ways to add value without a traditional background is by shaping what "quality" means for an AI feature. Work with product managers to ask questions like, Does the AI stay within scope? Does it handle interruptions gracefully? Is it inclusive across languages and dialects?

These things often get overlooked when you view AI only as a technical system, but they're crucial to user trust. If you can frame them in actionable terms, you can become indispensable.

It's important to build a portfolio around "AI-plus-people"

Hiring managers don't just want to see that you know AI exists — they want to see how you can shape it responsibly and make it usable.

Document all frameworks, rubrics, evaluation studies, and examples of how your insights influenced decisions. Even if you don't have access to big-company projects, you can run your own small-scale studies on publicly available AI tools and use those to showcase your thinking.

Try volunteering for projects where AI is being integrated into existing tools, and answer questions like, What does this AI feature need to do? How should it behave? Can the AI explain what it can or can't do? Does it recover gracefully when it makes a mistake?

These are the kinds of questions researchers, product thinkers, and others with non-traditional backgrounds are uniquely positioned to answer.