How do we grow in the age of AI?
From The Inside Seat Edition 3: Kaleb Loosbrock and Adeline Salkeld sit down for an honest candid conversation between researchers
As we automate more of our work, what happens to our skills and judgment that make us good at what we do?
A question with a lot of nuance.
In this transcript, Adeline Salkeld and Kaleb Loosbrock sit down to share their perspectives as researchers and leaders navigating this new age of everchanging tools and automation. Adeline Salkeld is Head of Product Experience here at Optimal, joined by Kaleb Loosbrock, Principal Researcher, Consultant, and Founder of AIxUXR which is a private community fostering collaborative learning about AI within the UX Research industry. This conversation explores where the UX research field is heading, what we risk losing without being intentional about growth and preserving expertise.
Adeline: It’s interesting when you’re in leadership and wrestling with what AI can bring, because you want to be pragmatic and capture all the wins AI offers, but you also care deeply about the validity of the research and the findings. It’s a fascinating place to be.
First off, what does progressing as a UX researcher mean?
Junior, Mid-level, Senior, Staff, and Principal show a clear course of development in the United States, but not everywhere in the world has the same career infrastructure. There’s no universal title system around the world, no consistent rubric for growing talent in corporate environments. This gap, and lack of consistency around what makes a good researcher at each level and how to progress to the next, is exactly what motivated the UX Research Career Guide built by Kaleb with Karin den Bouwmeester at UXInsight, which also maps where AI might fit into each stage.
Automated tools are taking on more of the workload for many research teams from literature reviews to coding, analysis, and reporting. There’s a real tradeoff though, when you’re still building the foundational knowledge needed to interrogate what those tools are surfacing and why.
Kaleb: A lot of the research coming out of economic forums about the impact of AI has focused on lower-level job holders, and those people entering the market right now are being adversely affected. I understand why: they don’t yet have the skill set or knowledge base to recognise when AI is making something up. They don’t know the difference between quality and craft. That’s part of the reason I got involved with Karin den Bouwmeester at UXInsight, working on the career UX framework.
Understanding the impact of automation
Skills have historically been passed down person to person through generations with people building on the achievements of those before them, ultimately driving progress and advancement. In the age of AI, skill accumulation is changing because the ways we document and understand relationships in our data are changing. Learning now often involves a mix of manual and automated meaning-making, so the way we learn and develop expertise is shifting. The question this raises is how we mentor research expertise in others when much of the process is automated.
Kaleb: The real problem is going to be the atrophy of critical thinking, the atrophy of our skill sets, and the atrophy of our talent pipeline. How do we grow in the age of AI? That’s what actually keeps me up at night and makes me ruminate, not only about my own career, but about all the researchers I’ve met along the way that I deeply care about, the people I coach and mentor, and how to help them build resilient careers in the age of AI.
It shows up practically in teams every day, for instance in product designers leaning into research.
Adeline: You’ve got very talented product designers leaning into research. How do you grow them in a way that they can understand the nuances of speaking to real people? They’re keen to get in there, talk to users, and get that feedback, to sit with the messy, complex things you hear when speaking to humans, rather than just testing with AI and getting a neat list of things to change.
Ultimately, it’s about staying close to your data and users, recognizing when patterns may be overemphasized or nuances overlooked. This also requires understanding how the AI tools you use function, what they interpret, and how they interpret it.
Kaleb: There’s a lot of work to do, especially if you’re an untrained researcher or someone coming from a tangential field. You may know a little, but not enough to ask the right probing questions of AI: how many interviews did you observe that in? Give me an itemised list. Show me the verbatims. Give me timestamps. I don’t see that level of rigour from many research teams. They don’t follow up like that, because they’re not used to articulating their process.
To navigate workflow automation effectively, tools should at minimum provide verbatim responses, timestamps, and supporting evidence, allowing you to drill into insights and see what’s truly happening behind the data.
Adeline: You need that level of creative thinking, and the ability to dig into what you actually saw and what the real problem behind the findings is.
Kaleb: For junior researchers, AI should act as a Socratic tutor, a mentor that reviews your plans, your discussion guide, and gives you further things to think about. But ultimately, as a human, you should do what humans do best.
You are the arbiter of truth, the arbitrator of craft and quality.
What the evolution of the UXR role might look like
Kaleb shared a couple manifestations or evolutions of what the future of UX research might look like. Firstly, with deeply specialized researchers or ‘T-shaped people’ who are the arbiters of craft and quality and others who wing out to different places like communication design, presentation design or data science to help.
Kaleb: My theory is that you’ll have T-shaped people who go very deep. They’re going to be keeping an eye on the research, your craft and quality arbiters, able to navigate AI systems, trust them, and train them on what is good and what isn’t. Essentially, they become an AI training team, helping these tools and helping the broader team level up their craft and think of new ways of doing research. On the flip side, I think those deep specialists are still very valuable, but they’re going to become increasingly scarce. What we’ll see instead is people developing wings out into other areas.
Secondly, moving from an operator to a curator, curating the data pools for intelligent machines to help bring it in.
Kaleb: We haven’t been able to do this before, but now with these tools you can build knowledge graphs and ask: where is all my knowledge, and where are the holes in it? You can direct the research team to go and get the data that is creating AI bias, and then actually do something about it, because that’s what researchers do. We live and breathe bias, and we try to stamp it out wherever we find it. Hopefully we can bring equanimity and equality to datasets that have lacked it until now.
We’ve seen this before with transformative technologies like industrialization prompting the rise of specialized positions and educators in order to preserve and transfer knowledge onto the next generation. New positions were born that we never thought of, that we never even thought to think of, were born out of the new needs of new technology and people.
Adeline: I saw something recently about how AI doesn’t reduce workload, it just intensifies it, because you’re doing more, faster. And I think that’s what we’re seeing in the team now. We’re able to get more done faster, which is good in a way, but you still have to stop and ask: what is the truth behind these findings, rather than just ticking the box and moving on. That’s a risk, I think.
In terms of research at Optimal, I don’t think it necessarily requires a new skill, but it is changing the way we work. It’s about identifying which parts we need to sit with, give ourselves thinking time, and which parts we can speed up without risking going down a completely wrong path.
It’s “thinking fast and slow” as Kaleb puts it.
Kaleb: It also relates to the book on System 1 thinking, where some things you can just do by rote. That’s the part AI should handle. But when you need System 2 thinking, where you need to stop, pause, digest, and make real cognitive leaps and connections, that has to be human. Because AI doesn’t know the context of the environment you’re living in. It may not know the bureaucracy you’re facing.
Soft skills are the new hard skills?
I think in this age of AI, the traditional soft skills are now the hard skills. They’re the things that actually matter, and honestly, they were always the things that really mattered.
Storytelling is becoming even more important than it already was.
Adeline: It was always important for researchers to be able to tell a compelling story, to embody what they saw with the users. There is a risk that if you don’t sit with what you’ve got and create it, even if you’ve created it with AI, if you don’t sit with it, ingest it, and be able to embody that story yourself, you may not be able to articulate why this, or why that, when challenged. Because you have something that potentially doesn’t actually represent what you saw, and you haven’t owned it enough.
You need to be the owner of that story. And I think that’s worth calling out as an area of learning for junior researchers: how do I become an amazing storyteller so that I can be convincing and be the voice for the users?
Kaleb: Watching the videos, watching the people, asking the questions is part of it, but the larger part is pulling that into yourself. You start to understand intrinsically what they’re doing, so that you can advocate for them. Because if you just watch or read the report, do you actually know what it’s saying? And as they say, data doesn’t change people’s minds or beliefs. Stories do. Storytelling is what rallies people to build better products. The root of it is deep, intrinsic knowledge of who you’re designing for, what their life is like, what their world is like, and how your product fits into it.
Value is made from humans, and that defines what we’re doing.
Adeline: There’s a real risk of an AI tech echo chamber that creates more and more useless software for the majority of people, because we’re not talking to them. It’s not trained on them, the internet doesn’t represent them well, and so we just spiral and keep creating AI slop. And that’s quite terrifying to me.
The pendulum will swing
Kaleb: I did a round of research at Instacart looking at the future of shopping in the age of AI, talking to experts at the forefront of the field who were building this technology before it even hit mainstream. One fascinating conversation was with a participant who talked about AI as the special sauce. He said there would be a pendulum swing where everything moves to AI, AI becomes the cool special sauce, which I think we’re firmly in now. And then, he said, what you’ll see is the human becoming the luxury experience.
It’s the human expertise and experience that prevails.
Kaleb: That’s where I think craft becomes important. Oxford defines it as something you do with your hands, but I think it’s much more than that. Poetry is a craft. It’s something you hone, a skill developed over time, whittled and refined through the human sieve, through the human condition and experience, through words and metaphors. We look for that semblance of nature wherever we find it. And when we see something as artificial, it might be novel, but it’s never fully embraced. People are drawn in by the novelty, but they eventually lose interest because it isn’t human.
AI can amplify your capabilities by handling the grunt work and gathering data, but empathy, curiosity, critical thinking, and the ability to craft compelling stories from that data are uniquely human. UX research has always been (and will always be) about connecting with people: understanding their needs, advocating for them, and designing experiences that genuinely improve their lives. To do this well, we must intentionally grow and refine our skills, and share them with others.
We’d love to hear your thoughts on this. Where do you see the UX research role heading, or how is it evolving within your organization? How are you approaching the integration of automation for your team, your mentees, and yourself?
This article features Kaleb Loosbrock, previously Staff Researcher at Instacart and Lyft, and Founder of AIxUXR, a safe space for UX Research professionals to debate, discuss, learn, and grow together in the age of AI.
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ICYMI last month we caught up with Ruth Hendry on designing for AI without losing the human.
