Insights Unlocked
Using AI to streamline and elevate UX research with Google's Alita Kendrick
Mon, 26 May 2025
Episode web page: https://bit.ly/4370dIB ----------------------- Got a question? Want to recommend a guest? Or do you want to tell me how the show can be better? Send me a voice message via email at [email protected] ----------------------- In this episode of Insights Unlocked, we’re joined by Alita Kendrick, a UX researcher at Google leading accessibility research for the Google Cloud Platform and exploring new market opportunities. Alita discusses how generative AI is revolutionizing UX workflows—from streamlining planning docs to accelerating qualitative data analysis. Host Michael Domanic and Alita dive into how tools like Gemini, ChatGPT, and UserTesting’s AI features are cutting research time in half and giving researchers back the space to focus on strategy, storytelling, and democratizing insights across organizations. Alita also shares real-world use cases, common pitfalls to avoid when starting with AI, and how AI can even help uncover your own unconscious biases. Whether you’re a seasoned UX researcher or just starting to integrate AI into your work, this episode is packed with practical advice, inspiration, and a look at what the future holds for AI-powered research. Key topics discussed: How Alita uses AI throughout the research lifecycle—from planning to analysis Time savings and operational efficiencies achieved through AI tools Creative tools like NotebookLM, Claude, and Midjourney in UX workflows How to prompt AI effectively and avoid common early mistakes Balancing AI outputs with critical thinking and business context The role of AI in democratizing research and surfacing legacy insights Personal reflections on AI adoption, experimentation, and optimism for its future Resources & Links: Alita on Linkedin (https://www.linkedin.com/in/alita-kendrick/) Michael on LinkedIn (https://www.linkedin.com/in/michaeldomanic/) Alita’s author site on NNG (https://www.nngroup.com/people/alita-kendrick/) Alita’s Efficient UX course on NNG (https://www.nngroup.com/courses/efficient-ux/) Learn more about Insights Unlocked: https://www.usertesting.com/podcast
Chapter 1: What changes has AI brought to UX research?
Welcome back to the Insights Unlocked podcast. In this episode, we're joined by Alita Kendrick, a UX researcher at Google, to talk about how generative AI is changing the game for research teams. From cutting analysis time in half to helping researchers tell better stories and democratize insights, Alita shares practical tips and lessons learned from the front lines. Enjoy the show.
Welcome to Insights Unlocked, an original podcast from User Testing, where we bring you candid conversations and stories with the thinkers, doers, and builders behind some of the most successful digital products and experiences in the world, from concept to execution.
Welcome to the Insights Unlocked podcast. I'm Nathan Isaacs, Principal Content Marketing Manager at UserTesting. And joining us today as host is Michael Dominick, UserTesting's Head of AI. Welcome, Michael.
Hi, everyone. As always, it's great to be here.
And our guest today is Alita Kendrick. Alita is a UX researcher at Google, where she leads accessibility research for the Google Cloud Platform and explores new market opportunities. With a background at Nielsen Norman Group, she brings deep expertise in design systems, research methods, and turning insights into strategy. Welcome to the show, Alita.
Lovely to be here. Thanks, y'all.
So Alida, yeah, it's great to have you here. I think maybe a good place for us to start our conversation is to kind of pick up potentially where we left off in your presentation. So you had come to our Human Insights Summit last fall. That presentation really resonated with our attendees and with our audience.
So in it, you shared how AI tools are significantly reducing the time it takes for you to produce insights for your work. Can you help our listeners understand, you know, how are you and your team, how are you using tools like Gemini to improve your research process? And, you know, potentially any updates on, you know, use cases and things that you've been doing since that talk in the fall?
Absolutely. So I'll start a little bit high level and then we can kind of drill into each one of the phases. If I'm thinking about like a traditional research process, right, starting with defining the problem space, clearly articulating the problems we're looking to solve and the questions we're looking to answer.
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Chapter 2: How does Alita Kendrick use AI in her research process?
Really where I'm using generative AI specifically is helping me speed up the creation of some of my planning documents, managing some of my participant communications and stakeholder communications, as well as super lightweight things like developing a project timeline where maybe I have a bunch of times in my brain, but like the use of AI can just help me speed up the actual documentation of some of those things.
in the more planning cycle related to the specific method selection and things like that. Again, drafting some of that participant communications through the use of generative AI can speed some of that up and also help me determine kind of some of those study guides or additional resources used throughout my testing.
Usually I have like a full script with my participants that I create, that's kind of a common methodology, but this just helps me in getting that type time to delivery a little bit shorter. In the context of conducting the actual study, of course, this varies from study to study.
But one of my recent use cases with user testing is a great example of how a lot of the AI synthesis that you all use can really help speed up interpretation, which gives me as a practitioner a little bit more time to focus on the storytelling, the democratization of those insights and really pushing for action.
And then finally, of course, that large phase of analysis where I commented of how user testing can play a role. And then, of course, when I'm doing methods that may be more interview focused or concept testing in a moderated situation, of course, generative AI like Gemini can really help with qualitative data analysis. I love using this as sort of the halfway point for a lot of my studies.
I'll do a check-in with generative AI to say, okay, based on all of these transcripts and my core research questions, how am I looking? Like, am I answering those questions or what should I be prioritizing in the second half? So there's so, so many applications from those early stages of study definition all the way into analysis and presentation.
Yeah, that's great. I mean, you've talked about a lot like there's there's like, I feel like so much to unpack there. I think, you know, like one of the things that, you know, our listeners, they're in similar roles, a lot of our listeners are in similar roles to yours. And they're thinking about how do we integrate AI into our process.
But a lot of times, I think before they do that, they're kind of curious about like, how much time is this actually going to save me? Is that something that you're tracking and kind of reporting throughout the organization?
Yeah, from a qualitative perspective, absolutely. So I'll speak specifically to some of my experiences because I've become quite process-oriented over the past couple of years. And that's enabled me to really look more detailed at how much time I was spending at certain phases. So even just looking back at my calendar from...
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Chapter 3: What time savings can AI provide in research analysis?
So I heard you talk about Gemini as being like an important general purpose tool, like a general purpose AI tool in that process. You mentioned the AI features in user testing as a point solution that you're using. What other tools might be in the mix here that are helping you and your team?
Yeah, for all of the program areas that I own, there's a notebook LM for each one of them.
I love that product, by the way. I use it every day.
I love that. It's so cool to hear like two different levels of usage then of like consolidation of lots of insights. And then again, that kind of ideation, mind mapping, connecting the dots. I love to hear that.
Yeah, great. So Notebook LM, we've got Gemini as a general purpose tool, again, user testing as a point solution. Anything else that's a common part of the practice, like any other AI tools that are kind of in the mix there?
I'll leave it at that for the Google side. But on, as I mentioned, you know, kind of being an instructor at Nielsen Norman Group, I recently developed a class called Efficient UX, Doing More With Less. And that really has pushed me to learn a lot of the different AI tools out there. I mean, I had always been quite experimental and actively engaged with AI.
what's happening with GBT, what's happening with Claude, and different generative models as well as things like perplexity, mid-journey, and all of that. But through this class, I really focused a lot on learning those areas. And of course, you get the sense of the competitive analysis, what works best in different contexts.
But I think for me as someone who is not only a heavily active user of these, but also a designer of them, it is really nice to compare those. So depending on the types of tasks, particularly in the case of some of my consulting or my work with Nielsen Norman Group. I'll put it in a bunch, like I'll put the same prompts in Claude and in Gemini and GPT. As a researcher, we're curious, right?
One of my favorite things about Gemini, and this is so core to my workflow, is The voice experience of being able to just talk to Gemini and have it listen. And I use that all the time for mind mapping. That's completely revolutionized the way that I do my mind mapping and brainstorming. Because since the beginning of my career, I've been that person.
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Chapter 4: What are common mistakes when integrating AI into UX research?
And so it is, you know, when people are getting into those situations of more publishing and things like that, even if it's just, you know, your consistent research reports, that's something that you can all provide that as context to the model and say, okay, now based on what we're working on, improve this content, but insurance matching my way of speaking and my style.
Because I think you will be, I always think about, especially now in being in a large corporation, of just your brand as an individual and as a contributor to your team. And your brand is often seen through the deliverables that you're sharing. So when someone goes through kind of such a stark revamp around this time of generative AI, you're kind of like, oh,
Okay, like how much reliance do you have on this? And I think we might be getting to that point where we're like, you know, we want to make sure it's a merged view. It's the AI being helpful, but it's not leading the creation of all of this work.
Yeah, 100%. You touched on something earlier I'd like to come back to, and I think this was something that you brought up in your presentation in the fall, and that is really around the topic of maintaining objectivity in your UX research. I know it's a goal to remove your bias when you're pulling out insights and when you're trying to really report on what your users are telling you.
How is AI helping with that process?
Yeah, I love that because AI has really changed the way that I take notes during my sessions. Before generative AI, and I've had the same research note-taking template since the start of my career.
And it was quite simple in that it was a timestamp, a question or a theme that I'm noting, and then the actual note column, and then a separate column for tags of like question marks for follow-ups, exclamation points, and things like that. And when I was taking notes back then, it was like everything.
It would be quotes, it would be a key takeaway, it would be they clicked this and this was their workflow. So it was so many different levels of note taking because we want to get it all in.
Now with the use of generative AI, I'm already getting transcripts so I can really focus on whatever quotes I'm documenting, they're probably going to make it into the report or that's like a really nice high value quote. If I'm highlighting an action, it's probably a specific theme that I'm like, okay, this is going to be a top takeaway.
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Chapter 5: How can researchers maintain their voice when using AI?
Chapter 6: What tools does Alita recommend for AI integration?
Chapter 7: How does AI help in democratizing insights?
Of course, if you're doing more open-ended activities like brainstorming potential use cases for a feature or potential research questions in a topic space like information architecture overhauls, Things like that, maybe not as much specifying the output, but still defining the role and that context is really helpful.
I'll find some people when they're really trying to identify areas to use generative AI, they may get into specific of a context at their very first attempt and then receive outputs that don't meet their needs. And that kind of scars them away from it for a while. And the whole point with this is really being experimental. And I think that
You know, AI from a social aspect has really come about and in some ways scared some people. There's, of course, the talks of like, oh, it's going to steal my job. It's going to do this. It's going to do that. But I think now we're really at that phase where it is, you know, you do need to be experimenting. And even if in the beginning it's not giving you what you want.
Don't bring a fixed mindset to that. Take an experimental lens to say, okay, how can I improve these prompts? And Michael, I know you and I have talked about this, but the use of mid-journey has been that for me. And so with mid-journey getting started, I was like, I don't know how to write a really good visual prompt. Like I'm coming back with things that aren't really fitting my needs.
So then I'm like, I'm just going to ask GBT or I'm going to ask Jim and I to write the prompt for me. And instantly the quality shoots up. So-
Use use AI to use AI.
Absolutely.
Yes.
Yeah. So So speaking of the outputs, I think I remember in your talk in the fall, you had mentioned that you approach those outputs with a healthy level of skepticism, right? So how do you balance the usefulness of the output against making sure that you have that healthy skepticism?
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