Hi, everybody. Good morning, good afternoon, anywhere you are in the world. I'm Priyanka.I'm very excited to get started with this session, and I wanna thank you all for joining this session. So as Paulina introduced me, I am a senior AI UX researcher at Microsoft. And today, I'm gonna be talking about how you can break into UX research and specifically also in the context of AI product development, which is where the industry is heading. So whether you're exploring a career transition, you're early in your journey, or you're just looking to understand more about this field, this session is gonna give you further insight into all of the wonderful things that us researchers do. Just a heads up, unfortunately, my hand is a little injured, so I'm using my left hand for everything pretty much.

So if I'm looking around, this is just me navigating those screens. But let's get started. Okay. So next slide. Awesome. So just a brief introduction about who I am. As I said, I'm a senior AI UX researcher at Microsoft where I'm leading research specifically for Teams calling and AI related AI collaboration experiences. I've been in the field of research for over eight years, and I've worked I'm working at Microsoft. But before that, I've worked across Cisco, Global Payments, Confetti. I also have a master's of in user experience interaction and design, and I'm an accessibility champion. So that means I also lead research with people with disabilities to ensure that all digital products are meeting accessibility standards from the ground up. And outside of my job, I am really active in the UX community.

So, I'm passionate about giving back and helping people break into tech, and I do that through content creation. So I am a content creator. My channel on Instagram is called u x r dot priyad I. I have a barcode if anybody is interested in following or reaching out. Always happy to, chat. I also do mentorship. So I am an active mentor on ADP list and LinkedIn where I connect with folks to help them break into UX, help with resume reviews, portfolio reviews, interview coaching, all of that helpful stuff. I am also interested in speaking engagements. So I've previously spoken at Women in Tech. In 2025, I was invited as a speaker at Grace Hopper Conference, and I also have a few upcoming conferences and podcasts this year. So really excited about that.

I think the whole point is I really enjoy giving back and helping folks break into text. So that's something that I pursue outside of my work. And I'm also a food blogger. I'm passionate about blogging, about, like, different recipes in my journey. So all of that's something that I pursue beyond work. I've also added a LinkedIn barcode if you're interested in connecting, chatting more. Always feel free to do that. So awesome. And so today, specifically, what we're gonna be touching on is I'm gonna be talking about what UX research is and defining what UX research means and what the research function is, especially in product development, where it fits within the product development and why it matters.

We'll be examining why UX research becomes even more critical in the context of AI. And then I'm gonna be sharing some actionable guidance on breaking in, what skills to develop, how to practice, and how to present your work more effectively. So in the end, we'll also close with a quick q and a. So let's start with understanding UX research to begin with and different functions that we work with. So to establish more context, consider, the products that you use daily. So there's Instagram, Spotify if you're listening to a lot of music. There's WhatsApp, TikTok, Facebook, Uber, DoorDash, anything and everything, all the apps that you love and enjoy every day. Each of this is built by a cross functional team, an army of people behind it with different roles and responsibilities.

So there's the product managers who define what to build and prioritize the road map that are UX designers that are determining the in the experience, the interface, and interaction patterns. There are engineers who are implementing the technical solution and actually developing the application. There are data scientists analyzing behavioral data and metrics that are applied AI teams developing the machine learning models powering intelligent features. There's marketing driving user acquisition, sales and leadership, setting business strategy, and multiple supporting functions. So where does UX research sit here? So we researchers, we determine what users actually need and not build on assumptions, not what stakeholders believe, not what the teams believe that we should build. User researchers are actually grounding on real user needs in real context and actual experiences that we should be tapping into. What should we be problem solving for?

So the UX researchers serve as the voice of the user throughout the product development process, and we are the ones ensuring that before a feature ships, someone has evaluated and validated that it's solving the right problem for real people. And that's the question we're answering today. What UX researchers actually do, how can you become one, and what are some skills that you can develop along the way, especially in the age of AI? Another myth that I also actually want to clarify, a myth that I wanna bust is, the most common conception. So are UX researchers basically designers? And I wanna take a moment just to clarify this as well. A UI designer is somebody who focuses on the visual aspect of things, the aesthetic layer, color systems, typography, iconography, pixel level details.

Their output is more about how the product looks. Then there is the UX or product designers, specifically focus on interaction design, the experience layer. So user flows, wireframes, prototypes, information architecture of the application, and then output is how the product works. Now UX researchers so me, as a senior researcher, I would specifically focus on the evidence and the insight layer. So we conduct studies to understand the user behavior, evaluate whether designs are effective, and identify unmet needs that the product and business should actually be focusing on. So our output is how we know what to build and whether it is working for the end users. The critical distinction is that research spans across the entire product development life cycle. So UX researchers are involved in discovery, understanding the problem space before any design work begins.

They're involved in evaluation, testing whether solutions actually work, and we're involved in post launch as well. So measuring the impact and identifying opportunities for iteration. UX researchers are strategic partners. That's what that that's how I think about this function, that they're strategic to the business, strategic to the product, and we are crucial in shaping what the what what the team should be solving for next and what the business should be focusing on. And then let's also take a moment to just think more about this. So before we discuss why UX research matters even more in the age of AI, I wanna ground us in why UX research is fundamental to product development. Behind every product experience, as I've mentioned before, that feels intuitive, every feature that solves a real problem, every interaction that just works, there's a research that informs those decisions.

So consider what happens without research. Teams would be build teams would be building based off of assumptions. Stakeholders would be prioritizing based on just intuition or the loudest voice in the room. Products would be shipping and failing because no one actually did the research, evaluated, and validated whether users really want this type of a solution or is there any other effective way to approach this. So UX research mitigates that risk. We provide evidence to inform product decisions. We reduce the cost of building the wrong thing by identifying problems early. We give teams the confidence that they are solving real user problems and not just imagining ones. The business case is pretty straightforward. It's significantly cheaper to discover a true user problem, a usability issue through a research session than after you have actually used engineering and product and design resources to ship a feature that was not based off of research.

It's far less costly to validate product market fit, with a bunch of research sessions and studies than with a failed product launch. So research isn't just a nice to have or a checkbox for organizations. It's a core competency for building products that succeed. And as we move into an era where AI introduces complexity and unpredictability in your two user experience, this becomes even more critical. So let's talk about why it's even more important in the age of AI. So the core issue over here is technically functional does not always mean it works for users. So many AI products are being built today and are technically impressive, but could fail to deliver value because teams didn't understand how users would actually interact with them.

Traditional software, products are deterministic. Given given the same input input, you give you come up with the same output. Users develop mental models. They know what to expect, and testing is relatively straightforward. But for AI products, these are this is fundamentally different. The same prompt can generate different responses. Behaviors can vary across users, context, and time. AI breaks established interaction patterns that users have learned over decades of software usage. So single hallucination or incorrect output can destroy users' trust entirely, and quality is again subjective. What constitutes a good AI response depends heavily on user expectations and context. And this is precisely why UX research is more valuable than ever. We are the discipline equipped to understand how users form mental models of AI. We study where expectations diverge from reality, and we identify the moments where trust breaks down and why.

Companies are investing billions in AI capabilities, but without understanding and evaluating how humans actually interact with these experiences, how they actually build trust, and what breaks trust, They are building without the user inside that determines success or failure, and that's why UX research is even more important in the age of AI.

Now let's talk about how we are actually using this as researchers. I like to think about this as like AI is a tool and as researchers, you are now the strategist. This is a question that I often get asked even in mentoring sessions. Will AI replace UX researchers? And I'd love to address this a little more. I often think about the Excel analogy. So we did did spreadsheet software replace accountants? I think of it that way. It replaced the manual arithmetic, but accountants who primarily did calculations are now moving forward to more analytics and judgment and strategic guidance, which has become more valuable. So the same dynamic applies to research. AI excels at specific tasks, and it can help you in your workflow.

For example, transcribing hours of interview recording in minutes, performing initial coding passes across large datasets that you're gathering through qualitative or quantitative research, generating draft discussion guides or research plans that you're developing, and summarizing secondary or desk research that you're doing, creating like, helping you create your reports and templates faster.

What AI cannot do, though, and this is where researchers provide irreplaceable value, is AI cannot form the right research questions, and it cannot determine which problems are worth solving. So as researchers, you're more of the strategic pillar guiding product strategy by connecting user insight to business decisions. AI cannot navigate organizational dynamics to ensure insights influence roadmaps, and that's a day to day work for UX researchers. And it cannot exercise judgment when data is ambiguous or contradictory, which in qualitative research is the norm. So the researchers who thrive will be the ones who leverage AI, to eliminate tedious work while focusing their expertise more on the strategic thinking, synthesis, and influence. And that's the skill set that you would focus on while leveraging AI to inform your workflow.

I'd love to give a quick sneak peek into the day in the life of a researcher. So I feel this visual really represents the scope of work in my role and in even every researcher's role. So there are multiple research projects that you're handling at the same time. You would be handling different methods. You might be running user interviews and surveys and analysis at the same time. Simultaneously, there is now AI product evaluation. So actually ensuring that the product AI product that you're working on is evaluated for trust, transparency, and actually building visibility on the actions that it's taking. A huge part of a researcher's job is also cross functional alignment.

So really taking your user insights, evangelizing that with your product team, and ensuring that the insights are being used to inform the product development and road map. The huge part of the work is also synthesizing and analyzing the data that you're collecting and using that for product strategy and product market fit assessment. So it's a nonlinear, job and requires constant context switching. But the impact I feel is tangible. So you contribute directly to products being used by millions of users, and that's what I'd be talking about next. So what would you do to build this skill? So let's specifically focus on what do we wanna learn, to build this muscle. How do you become a UX researcher? I feel these are five pillars that you would wanna start focusing on first to break into UX research. The first would be, like, getting the fundamentals right.

Really understanding what UX research is and what are the different research methods. So exploring and understanding different methods, but not limited to user interviews, usability testing, user surveys, diary studies, contextual inquiries, and increasingly, AI evaluation methods, both both qualitative and quantitative. More importantly, you need to understand when each method is appropriate and why. So truly deep diving into the product development life cycle and understanding which qualitative or a quantitative method fits into the different phases of product development life cycle. The second I would encourage focusing on is the analysis and synthesis techniques that UX researchers use. So data collection is one part of it, but the value really lies in transforming the data into insight through thematic analysis, affinity mapping, frameworks like journey maps.

And this is where aspiring researchers really need to focus on. So collecting data, but also focusing on hands on synthesizing and turning that into actionable findings. The third would be more of a soft skill and qualitative skill that you should work on, which is communication and influence. I I feel storytelling is something that is really crucial for UX researchers. Storytelling, stakeholder management, executive communication, taking your insights and actually putting them into a clear and actionable workflow so it's easier for users to for for your team to understand what are the things that need to be taken what are the things that need to be, done next for driving decisions.

So communication and storytelling and influence is something that you should be focusing on. The fourth would be AI fluency. So in this AI JF AI, I would say this is a non it is starting to become nonnegotiable. So you need to understand how to leverage AI tools in your workflow and how to also evaluate AI powered experiences and how to contribute to responsible AI development. And the last thing I would be focusing on would be product development knowledge. So understand the product development life cycle, agile methodologies, and where research creates value at each stage because we are operating within the system. So understanding how that system really works.

I also wanna talk a little bit more about what I mean by the AI skills and knowledge that would be super helpful for UX researchers to have. So my recommendation would be to develop a conceptual understanding of how large language models operate. Not the mathematics, but more about the behavioral characteristics. So they are nondeterministic and probabilistic. They lack persistent memory. So understanding these properties helps you understand why users experience confusion or frustration when they are working with AI products. The second is learning common AI failure models. So hallucinations confidently generating false information, overconfidence and uncertain responses, inconsistencies across similar queries. So you need to anticipate these failures before users encounter them. The third would be understanding users' mental models, especially while using AI. So users map AI to familiar paradigms. So search engines, auto complete, chat bots, voice assistance. These mental models create expectations that often don't align with actual AI capabilities.

Identifying and addressing these gap is core research work. So familiarizing yourself would actually equip you with the knowledge of how these experiences are evaluated. The fourth would be studying trust dynamics. So trust in AI systems is fragile and asymmetric. Users frequently overestimate AI capabilities initially and then lose confidence entirely after a single negative experience. So understanding these dynamics is essential for designing trustworthy AI experiences. And finally, developing hands on proficiency with AI tools. This is something that even I do in my day to day job. Working on Microsoft Access, I I I actively use Copilot for not just analysis or evaluation, but more also helping me in my workflow, helping me draft quick research plans, helping me with my analysis. I think it's more of thinking about how these different tools help you become more efficient in your workflow.

And understanding, how these, tools can be used and leveraged in your workflow is crucial, especially in the age of AI product development. The next thing I wanna talk about is tap into further just one of the first couple of things that you can do to actually get hands on, learning resources. So this is a template that I'd actually developed to share with a couple of people on on my content creation platform, and I feel this is a good framework to start with. If you're really starting as a beginner in AI and UX research, focusing more on the AI over here. There are a couple of things you can start to familiarize yourself, with what that means. So and especially in terms of course and reading, I would recommend going through a couple of courses. Like, there is AI for everyone on Coursera.

There's also introduction to what responsible AI means. These are all free courses that are available. If you just Google search them, this should be available to you, and I think that's a great starting point, to get your foot in the door. The third one that I would highly, highly recommend is Nielsen Norman Group, the NNG Group. These are this, this online platform are specifically known for having a really good knowledge source for UX articles and even AI articles as well as courses. So I would highly, highly recommend taking a look at, NNG Group. A couple of skills that you should be focusing on as a beginner in UX research and AI would be prompt prompt crafting.

So especially, let's say, if you're using AI to help you with your research plan or getting started with your research, I think this can be really done well with using tools like Cloud or ChatGPT and really focusing on, what output that you need, for these specific plans. I would focus on AI evaluation base basics as well. So really understanding how UX researchers contribute to testing model accuracy, usability, and trust. And this can be done both qualitative and quantitative, with qualitative and quantitative research methods. And the third would be practicing quantitative UX research with AI. Quantitative UX research in itself is a skill. So serve not just survey evaluation, but even understanding how do you develop a survey. And with the tools, the AI tools that are available to you, how do you speed through, the of the analysis techniques for quantitative UX research. And then just using generative AI to brainstorm and as I said, drafting different interview guides, research plans, or even drafting your presentations and outputs.

I think these tools can really help you become efficient in your workflow. So practicing this would definitely help you get your foot into the door. The another resource that I'd actually love to share is different resources to get your basic understanding of what UX research is and what user experience is as a whole. There are some really good foundational skill foundational learning resources and courses available. The ones that I would recommend is, like, Interaction Design Foundation, has a good course on introduction to UX research methods. So we'd highly recommend taking a look at that. Google has a good UX design certification available. And, again, the one that I recommended before was Nielsen Norman Group for UX research articles and courses. There are also different, books that are available and, podcasts that you can listen to to kind of develop your understanding of, UX research.

So, again, would recommend the Nielsen Norman Group podcast, and then online resources such as UXPlanet, UX Collective, and UXPA. And then for quantitative analysis and quantitative UX research, I would recommend going through MeasuringU. It's an, again, an online platform with different, resources and articles available for your disposal that'll help you get a true understanding of what quantitative UX research is. And taking a look at if you search for survey design one zero one on userinterviews.com, that's also a really good, course available, for your for for for your producer. And if you want access to these resources, I'm not sure if this deck is gonna be shared at the end of this session, but I'd be more than happy to share a deep dive on all of this. I have a toolkit that I'd like have been sharing with fellow mentees. I'm happy to pass on that link as well.

Just reach out to me on LinkedIn and I can send it your way. And then talking more about research and practice. So what can you do, like, start doing right now to get just a better understanding of what UX research and practice can be. So a couple of skills that UX researchers regularly use is doing user interviews and running usability tests. So I would really encourage focusing on what user interviews really mean. How do you develop a discussion guide and how do you in in how do you in how do you include open ended questions in this? How do you actually run these user interview sessions? So it can be even even if you take up a simple project, running these interviews with specific users, trends, or family is something that I highly recommend to get comfortable, with the practice of running these user tests and user interviews.

You're running usability tests as well. The way that I think about it is pick up any application or website that you might be using day to day and think about three-five realistic tasks that you would want to probably test out. You can collaborate with either either friends or family to actually run these tests and to understand and evaluate how they look at this experience, what are some points of struggles, hesitation, or confusion, to identify what you could do as UX researchers to identify points of friction and ways that you could develop and improve this experience.

I would also focus on the portfolio development part piece. I have a separate slide on this. I'd love to talk more on what you can include, but I would begin by documenting every project that you were doing. So really focusing on zooming in the context of this project, the method that you're using, and the findings and impact, and focusing more on the process and communicating your approach as a researcher than just to find an output. I also wanna talk a bit more about other tools that you can familiarize yourself as UX researchers. So there are a bunch of different tools, and this can also be company specific. But to begin with, there are a few tools that you can start taking a look at, specifically for research and recruitment.

User interviews, user testing, and discount are tools that are largely used by, different companies and different, UX researchers. So I would highly encourage you to explore these tools. Specifically for analysis and synthesis, I would focus on familiarizing yourself with Excel. If it's more statistical analysis or for thematic analysis, Miro and FigJam are some really good tools available in the market to get your hands on. AI powered tools, of course, I would recommend exploring Copilot, Cloud, Gemini to kind of see how you can use these tools specifically for quantitative or even qualitative, a bit of thematic analysis, specifically for product management and product development tools. Typically, product managers that you work with would be using Jira, Asana, Atlassian. So familiarizing yourself with these tools and understanding how these are used would be a good way to, start as well.

And then focusing on how you would be packaging your work. So your portfolio is gonna be the primary evidence of the work that you're doing. And I would focus here more on quality over quantity. So if you're able to do two to three deep dives and case studies, that would be more valuable than, doing multiple studies. And the the the focus should be on how you structure these case studies. So really providing the context. What was the problem that you were trying to solve and why did it matter? What the approach was? So what method did you use and why was it why was it appropriate for the study? And what did you discover and what are the implications?

And then in terms of impact, what did what what changed as a result of your research? And not change can be integrated in different ways. So not just what changed in your product, but also in terms of the team that you're working with. I think as researchers, we are not just having an impact on the product, but also on team alignment. It could be your team is focused on a specific solution, but users are actually drawn towards a different one. So you're as a researcher, you're building that alignment apart from also having immediate product impact. And then really focusing on the methods that you're using and expanding on, why you chose that method and how did it help develop the solution that you propose through your research.

Common miss mistakes that happen that I've seen in these portfolios are they're more method heavy, but light on the inside. So you could focus on let's say you've done a bunch of user interviews or usability studies and you explain what you did. But you also need to be clear on, okay, these are a bunch of studies that I led. What was the insight and what really changed, through the research that you led? It's also important that you shed shed some light on the failures and pivots. So let's say you decided to move forward with a particular project with an approach that you selected, but there are multiple things that change in this process. When you're working with a real product team, there are oftentimes pivots happening in terms of timeline shifts and accordingly, there are different methods that you need to explore.

So in the portfolio that you develop, I feel really shedding light on the pivots that you take would be super helpful. And then also explaining the so what. So what does the research mean to your product, but also to your stakeholders? What changed when you presented this to your team, and why was it important? So really focusing on this as well. And then let's also talk about what our hiring managers specifically are looking for. I feel especially when they are evaluating, UX researchers for different positions that they are hiring, I feel they're really looking for, can you explain, what you did and why you did it than just the UX research method that you chose for a particular problem that you're solving?

And can you really connect your insight to the action? They're also interested in really looking at the work that you did and not just in terms of what the impact was, but again, really understanding the context. What was the problem that you're solving? Why was it a problem? Who did you collaborate with? And what was the impact, in the end, not just on the product, but also on the people that you were working with? Hiring managers are also especially interested in your storytelling skills. So communication and influence, I feel, is really important to portray in this if if you're applying for a job as a UX researcher.

So it's not just about communicating your study, but also, in also sharing more on how these findings were used by your product team and if it actually landed and influenced decisions in the end. I feel as UX researchers, you need to be comfortable with ambiguity as well. So research is messy, and especially working with product teams can be pretty hectic. There are often times that you have to pivot and change your course of research, your timeline, and the method. So can you, as a researcher, are you comfortable with ambiguity? So even if, let's say, you present a portfolio piece with things that you did explaining the method of selling your approach, the actual study that you did, and the result, oftentimes, you might get questioned on, let's say, if things did not go as planned, what are some other things or what would you choose a different method or a different approach?

How do you navigate that sort of ambiguity? So that's the mindset that typically hiring managers are looking for. Of course, growth mindset and coachability. I think that's not just for researchers but for any position that you would apply. So are you open to feedback? Any are you open to, growing through this feedback? And if you're I think as humans, we make mistakes. So are you comfortable with more so learning from your mistake, and if you have that sort of a growth mindset? And then I think nowadays, in most positions I've been seeing is AI fluency for product development. I think for UX research positions, more so recently, people are really looking for experience in like, have you done AI evaluation?

Are you able do you have a foundational understanding of LLMs? Have you worked with product teams that are developing AI products? And while I feel it's difficult to have this sort of an experience if this is the first time that you're actually applying for a research position, but my recommendation would be to think about a portfolio piece that actually could shed some light on AI product development and your approach for, AI evaluation and research studies for AI products.

So, that would that's something that I would be, recommending for sure. And then just kind of rounding up everything that we, discussed in key takeaways. So as I mentioned at the beginning, UX research is a strategic function, so you're shaping product direction and not just validating decisions. It is critical, especially in AI product development, to build responsible AI products, especially in terms of portfolio. I know they shared a bunch of resources, and I'm gonna be happy to share that resources with y'all to get started, in your first research project. I would be I would I would encourage you focusing on becoming more fluent in just understanding and developing AI products so you're able to, have a more like, a better POV on how research can impact AI product development. And I think I'm almost on time, so I'm gonna just wrap up, and open for