Why do so many businesses struggle to turn insight into impact? At the X4 Summit in Salt Lake City, I sat down with Adam Hagerman, UX Research Director at Indeed, for a refreshingly candid conversation about how his team has helped reshape the company's approach to research, collaboration, and decision-making.
In a tech culture that values autonomy and individualism, aligning people around a shared understanding of user needs is no small feat—but it's exactly what Adam has been working on.
In this episode, we unpack how Indeed built a shared research framework that breaks down silos, creates a common language, and encourages teams across the business to talk to one another in more meaningful ways. Adam shares how his team tackles the challenge of information overload by focusing on relevance over volume—and how measurement itself becomes a shared system of accountability across functions.
We also discuss the evolving role of AI in research, including how Adam's team uses it to reduce cognitive overhead, accelerate workflows, and spark better questions. But perhaps most importantly, Adam talks about the mindset shift needed in times of rapid change—and why embracing uncertainty and remaining curious is more productive than clinging to old ways of working.
Whether you're a UX leader, researcher, or business decision-maker looking to get more from your data, there's plenty here to reflect on. How do you ensure your insights aren't just collected, but acted on? How do you build trust in research across teams? And how can we all get better at asking, "What problem are we actually trying to solve?"
Let me know what resonated with you. What challenges have you faced in translating research into action within your own organisation? I'd love to hear your thoughts.
[00:00:04] Welcome back to another special edition of the Tech Talks Daily podcast, coming to you once again from the Qualtrics X4 Summit here in Salt Lake City. And today I'm sitting down with Adam Hageman, UX Research Director at Indeed. We're going to have a fascinating discussion on the show floor about how research can drive real cross-functional change in large organisations.
[00:00:31] So, if you've ever wondered why so many companies struggle to turn insights into action, or why some businesses find themselves drowning in data, but still find themselves making gut-feel decisions or responding to the loudest voice in the meeting room, this conversation is for you, because Adam has been at the heart of Indeed's transformation. A transformation in how they measure user satisfaction, unify research insights,
[00:01:00] and ultimately get buy-in across multiple teams in this fast-moving, highly autonomous tech culture. And we'll also explore how his team built a shared research framework, tackled the challenge of controlling noise across multiple data sources, and even where AI fits into the future of UX research and hiring. But not only that, if you're a large organisation trying to scale research-driven decision-making,
[00:01:28] Adam has some pretty cool takeaways on how to navigate change without getting stuck in that infamous pilot purgatory. But enough from me. Let's get into it. How does a company like Indeed transform research into measurable impact? Let's find out as I beam your ears all the way to the show floor here at the X4 Summit.
[00:01:54] So, thank you for joining me here at X4 in Salt Lake. For everyone listening, can you just tell everyone a little about who you are and what you do? Sure. My name is Adam Hagerman and I lead a team of UX researchers at Indeed. I'm a UX research director. We look over our employer experiences. So, the people looking for people looking for jobs. And I've been at Indeed for about eight years. What else would you like to know? Well, since we bumped into each other on here, I saw you do a panel a couple of days ago.
[00:02:22] And before I came to the event, one of the big things that we're seeing every CEO talk about is, why am I not getting ROI from my tech project or my AI project? And here it's all about measurement. And when I was reading about yourself, you've had major transformation at Indeed that measures user satisfaction. So, what were the biggest challenges that you came across in unifying insights across so many different teams, so many different tools, because it's something we hear more and more about. The answer is you make people talk to each other.
[00:02:51] That's the answer. It's not easy, but the answer is you need people to talk to each other. You need people to exchange ideas. And I brought up a concept of shared vocabulary. If people are talking to each other and they're using different words to talk about the same concept, are they really talking to each other? So, early on, me and my boss identified the need to create that shared vocabulary. And part of that shared vocabulary is also a shared system of accountability. And that shared system of accountability is oftentimes measurement.
[00:03:17] So, as we were thinking about the problems we faced to advocate for users, what did we need to do? And we needed to create a shared vocabulary. We needed systems of accountability. And we needed frameworks that help all of these people do the right thing. Did you catch Rick Rubin's keynote today? He was talking about this topic as well and the importance of listening. Did you catch that at all? I did. And listening is the most important thing. I mean, obviously, I'm a UX research director, so I would say that.
[00:03:46] But I think it applies to no matter what capacity you're working in. If you're trying to solve problems for other people, you should know what their problems are. And you should be talking to them. You should be listening to them. And you should be leaving your version of truth to the side because you're trying to understand what their lived experience is so you can help them do what they need to do better, faster, cheaper, easier. And I think most people listening will have heard of Indeed.com. They've probably been there and applied for a job from that as well.
[00:04:14] And Indeed, if we take a look behind the curtain, Indeed's tech culture values individualism and autonomy. So how did you gain buy-in from stakeholders from across various departments to create that shared research framework? Yeah. Here we are. We're back at the golden nail or whatever it is. It depends on who you're working with. You've got to learn who they are, what motivates them, what incentives drive and values drive their decision making, and how can I contribute to that?
[00:04:43] Something I see from research teams a lot is a list of thou shalt nots. And I try to approach it from a different perspective, which is, okay, you're going to do that. But I mean, we're going to talk about what that means to do that. There are some alternatives. Did you talk about those alternatives? I engage them in dialogue. It works better for some than others. And for those that it doesn't really work, I have to reevaluate my system. Like, what am I doing? I do user research on how to do user research.
[00:05:10] The tactics are understanding what motivates them, what are their systems and values, and speak the language that they need to be spoken to. You've also mentioned that controlling the noise across multiple data sources is another huge challenge. So how did you approach defining that single version of the truth for decision making? Again, it sounds easy, but… Well, I didn't do it myself. I fed into systems that were already happening. There were data governance teams. Like, the work was happening. I didn't start this.
[00:05:40] But what I did is I chose to participate in the conversation. So there was already the cultural shift of, okay, we need to get our query system into one spot. We're going to make it a little bit user-friendly. Oh, our messaging system's going to go over there. These things were in motion. I just hitched my wagon. And you've also… Another quick question here. So how does AI factor into your work indeed? Could we… There's a lot of hype around AI. It's beginning to mature now. We're beginning to ask more of it.
[00:06:08] But what role do you see AI playing in research and decision making and everything moving forward indeed? It's a great place to start. The phrase I used in our panel, I stole it from somebody else, is cognitive overhead. With all of this noise floating around, what noise do I listen to? What is the important thing for me to grasp onto? That's like staring into the abyss.
[00:06:31] What we're trying to do is we're trying to identify what are the relevant signals that we should be paying attention to. What do those signals tell us about the user experience and what we can do about the user experience? This kind of veers off from your question, but I promise I'll get back. Data collection is an intentional act. It doesn't just manifest one day. Somebody has to say, I want to know about that thing. Part of the noise was all the people who said, I want to know about that thing. That's good.
[00:07:01] They're curious. They're doing what they should be doing. It's just over time, we get lots of signals that tell variations on a theme and getting to that shared vocabulary of, okay, this is the thing we're going to look at. Here's how we're going to talk about it. Here's what we can do as a result. That was the goal. And I think many listeners, especially business leaders in their organizations, they're struggling with turning insights into action. So how has Indeed successfully translated research into strategic change?
[00:07:31] Because there's a lot of people sat on the sidelines here trying to figure out the best way forward. How have you managed to do this successfully? What we're doing is we're trying to incrementally understand how we operate in an environment. We are a tool. There's a user. There's the world that they operate in. And then there's Indeed in between. We have a lot of information, but the information doesn't tell you what to do. The information informs a decision that you're going to make. You have to have clarity on what decision you want to make. That's oftentimes the hardest part.
[00:08:01] As I'm working with my stakeholders, I sound insufferable, but it's what problem are we really trying to solve? A lot of people take leaps of logic like, oh, well, we have to do AI. AI is an option. It's available. You can do it. What do you expect the AI to do better than whatever else? And is AI actually the best way to do that? Those are the questions I'm asking.
[00:08:28] I realized I switched topics really quickly, but how do we move from information into strategic change? We ask ourselves, what are we actually trying to solve? How does the data support a hypothesis we have? Do we need further clarity with an experiment, a test? Do we need to do additional research to fill in the gaps, improve our creative understanding of our role in the environment that we operate in? And it's also demonstrating to our stakeholders that we're giving them the keys to the city. I mean, obviously that's hyperbolic.
[00:08:58] But what we're doing is we're giving them keys to unlock that blank page problem. You've got a decision you need to make. We're helping you make that decision in a data informed way such that whatever you do is actually in the interest of our customers on the other side. And it's so refreshing to hear you say that because all since the beginning of the year, we had a lot of businesses talking about that they can't find ROI from their AI projects because they've gone. Did they know what they were trying to ROI? Exactly. Yeah, because they've gone AI first. We need to do AI.
[00:09:28] We need to do tech first, not understanding what problem we want to solve. So I'm curious from your side of things, everything that you're working on, how does AI factor in your work at AI at Indeed? And what role do you see AI playing in future decision making moving forward? Well, there's Indeed strategy and then there's how I do it on my team. Which one are you most interested in? I would say both. I think there's a lot of businesses, as I said earlier, they're set on the sidelines thinking we want to do this. We want to solve problems, but how does it work? How's it going to work in my business?
[00:09:57] So I think it'd be great from a team point of view and a business point of view. From the team point of view, my job is to make sure that my researchers are advocating for users and that they're placed in the right spots to have the right conversations with the right person at the right time. That's my job. AI can be one of the ways I help position them in the right places at the right time. What I mean by that is, do I want them poring over transcripts? Do I want them sitting staring at that blank page? I brought up the blank page question.
[00:10:25] Or do I want to empower them with, okay, here's how you can get started so you can do the real work that you're paid to do and that is going to give us outcomes better than had you not been there. So we're using AI as a tool to speed the process up. We're using AI as a tool to deal with some of that noise, help us figure out what we should be paying attention to. One of the things I find interesting about AI applications is you can interrogate it.
[00:10:53] I was actually having a conversation last night about what this tech transformation actually looks like. There was a school of thought for a while that computers were the, that was the technological transformation. But as I was thinking about it with this person, what we realized is that actually what we've been doing is we've just been sending files around since the Gutenberg press. Like our entire information architecture has operated around query and give me the file and I will make sense of the data.
[00:11:23] But what AI offers is we, we can say, this is the question we're trying to answer, assemble the sources, give us a place to start so we, we, we can dig in. And that interrogation aspect is where I'm pushing my team. You use AI as a way to start, get to the, your comparative advantage for the business. We're trying to figure it out. We have some early experiments that I probably shouldn't list off because I'll, I'll get them wrong, but we're trying to figure out what is the problem we need to solve?
[00:11:51] How do people hiring actually expect to interact with this? Do they expect to it? Is there a pleasant surprise we can give them along the way that, oh, I hadn't thought about that. That actually helps me interact with my environment a lot better. We're, we're exploring that right now. What advice would you give to other UX and research leaders that might be listening to our conversation today that they're trying to scale their insights in large, fast moving organizations. You've kind of been on this journey. Anything, any tips that you would share on that? The world is changing and be okay with that.
[00:12:22] This is a destabilizing moment for a lot of people in my profession. And some of the reactions I've seen are catastrophic. They're like, this is the end. Like I might as well just give up. I'm seeing other people who are cautious and curious, but the people I see who are thriving, they're the ones who just accept the world is changing there. It doesn't do us any good to avoid that. Yeah. Figure out how do we use this tool? What is this tool good for?
[00:12:51] How does it help us be more, help us be more effective? That, that would be the advice I give. And I'm curious, are there any emerging trends in UX research and AI driven insights that you think might have the biggest impact on both hiring and the job market? You strike me as someone who's going to be following this space closely. Is there anything that stands out to you or excites you? Once upon a time, I read a book called Imagine Communities by Benedict Anderson. It's like base level, like culture, anthropological cultural studies. And he brings up this idea of print capitalism.
[00:13:22] And I think we are in early stage of that print capitalism concept where there's a lot of stuff happening, but we don't quite know how it's going to shake out there. There will be consolidation. There will be failures. There will be massive successes, but we don't know which ones are going to be the thing. So I'm kind of in a watch and wait mode. I'm not ready to panic. I don't think there's cause to panic. Um, humans have dealt with this before.
[00:13:48] We've had technological revolutions and here we are talking to each other. I think acknowledging that it is a change and not rushing too quickly to a conclusion is my, my take. Fantastic. And of course we are here at the X4 by Qualtrics here in Salt Lake City. You've been on panels. You've been doing sessions, walking the show floor, talking interviews.
[00:14:16] If you take all the conversations that you've had, what are you going to be taking back home to your office? What are you going to be reflecting on? The conversation I had yesterday about like, really all we've been doing is like moving files around. That, that was actually kind of transformational for me. I was like, yeah, you're right. That it is a change in how things are working from the stuff that I've heard in the floors and on the panels. I'm curious to see what, what happens. I'm interested to try it. I want to poke at it a little bit, interrogate it myself.
[00:14:44] I think we're in that early flowering and I'm very excited to see what happens. And I think that's a perfect moment to end on. But before I let you get back on that show floor, anyone wanting to find out more information about anything we discussed today, the kind of work that you're doing in indeed, maybe even connect with you on LinkedIn or anything like that. Where would you like to point everyone? My LinkedIn profile, just Adam Hagerman. Awesome. Well, I'll add a link to that to make it nice and easily. But I appreciate how busy you are doing back to back interviews. You were one of the busiest people here.
[00:15:12] I've seen you running around, but just thanks for joining me today. Thanks for having me. A big thank you to Adam Hagerman from indeed for sparing a few minutes as he was dashing around the show floor here. And I for one enjoyed diving into the realities of UX research at scale and how teams can move from insights to tangible business decisions. And one of my biggest takeaways is it's not just about collecting data. It's about knowing which signals matter.
[00:15:41] Because, yeah, organizations often drown in research and metrics, but without that shared vocabulary, clear goals and a commitment to turning insights into action. All that data, that's just noise. And I think Adam's approach of aligning stakeholders, asking the right questions and integrating AI as a tool, not a silver bullet. That is a model that many businesses can learn from.
[00:16:07] So if you're interested in learning more about Indeed's research driven approach to hiring and UX, well, maybe you just want to connect with Adam. I'll drop his LinkedIn link in the show notes. And of course, I'd love to hear your thoughts on this. How are you and your organization ensuring that research insights actually drive change, meaningful change, tangible results? As always, drop me a message, leave a comment or join the conversation.
[00:16:37] You can get me at techblogwriteroutlook.com, LinkedIn, X, Instagram, just at Neil C. Hughes. But until next time, keep questioning, keep optimizing and most importantly, keep listening. Speak with you all again tomorrow. Bye for now. Bye for now.