The UX researchers who don't adapt to AI won't be replaced by it—they'll just be left out
The UX researchers who don't adapt to AI won't be replaced by it — they'll just be left out
There is a slow-moving crisis inside UX research teams, and it has nothing to do with artificial intelligence stealing jobs. It has to do with relevance.
That's the provocative but carefully argued thesis that Julien Bourjot-Deparis and Nastia Koro, both staff research consultants at UserTesting, laid out in a recent webinar on the future of UX research. Their argument is not that AI is coming for researchers. It's that researchers who fail to meet AI halfway—by upskilling, by getting strategic, by owning their seat at the table—will gradually find themselves on the outside of decisions that used to be theirs to shape.
"The real risk for researchers is not actually any AI stuff happening," Nastia said. "It's more like losing relevance."
The fork in the road
Julien opened the session by mapping the terrain. Across his conversations with customers spanning industries and company sizes, he sees organizations arriving at a genuine inflection point—leaders asking hard questions about what research is actually worth, whether AI tools can do it faster and cheaper, and whether the ROI of a research team can even be measured. These aren't hypothetical questions anymore. They're being asked in budget meetings.
ON-DEMAND WEBINAR
Research in the Age of AI: How to Stay Relevant, Build Trust, and Prove Your Worth
From that pressure, Julien sketches three plausible futures for the research profession. In the first, AI's promise turns out to be oversold — insights prove unreliable, product quality suffers, and the pendulum swings back toward human expertise. In the second, research dissolves as a distinct function, absorbed into the workflows of designers and product managers who lean on AI tools to compensate for their lack of methodological training. Insights become, as Julien puts it, "everywhere but owned nowhere."
The third scenario is the one he finds most compelling: the researcher's role doesn't disappear, it evolves. Execution gets automated. Orchestration becomes the job.
He also names a fourth path — the darkest one — where companies low on research maturity simply replace human inquiry with synthetic personas and AI-generated insights, never noticing what they've lost because research was never truly embedded to begin with.
The orchestration shift
If the optimistic scenario plays out, the day-to-day texture of research work changes significantly. Think of it less like a craft studio and more like an air traffic control tower — fewer people, but each one responsible for overseeing far more complexity. Senior researchers supervise pipelines, curate AI outputs, enforce methodological rigor, and translate findings into language that actually moves stakeholders.
Research ops, in this future, is no longer just about process. It becomes a governance function. Someone has to decide what good AI-assisted research looks like, what ethical guardrails apply, and what the difference is between a hallucination and an insight. Julien argues that researchers are the natural owners of that responsibility — if they're willing to claim it.
"You need someone that is legitimate in both the UXR practices and the AI tooling," he said.
ON-DEMAND WEBINAR
Designing the Insight System of Tomorrow: How UXR Leaders Can Shape the Role of Research in the Age of AI
Stakeholders are users too
Nastia's contribution to the session is where things get practically useful. She reframes the entire AI conversation around a skill that researchers already claim to have but often underuse: empathy—applied not to end users, but to the people inside the organization.
"Stakeholders are our users too," she said. "We need to speak their language."
Her argument is that AI creates an opening here. With automation handling the more mechanical dimensions of research—transcription, synthesis, initial analysis—researchers have time they've never had before. The question is what they do with it. Nastia is direct about where she thinks that time should go: into relationships. Into understanding what an executive actually needs when they ask for research. Into pressure-testing findings before a presentation by running them through an AI persona of a skeptical CFO.
It's a form of strategic research that the profession has always talked about and rarely prioritized. AI, paradoxically, may be what finally forces the issue.
The meta-skill bet
Underlying everything Nastia and Julien discuss is a conviction that the researchers who thrive won't necessarily be the ones who master the most tools. Tools churn. The underlying capabilities—judgment, curiosity, agency, the ability to connect an insight to a business outcome—are what last.
AI literacy matters, Nastia is careful to say, but not in the way most people mean it. It's not about prompting fluency. It's about knowing how AI fails.
"Do you know how it fails?" she asked. "If we know how something is failing—like hallucinating or any limitations—we know how to deal with it."
The researchers who understand that distinction will be the ones designing the systems, not just using them. And that, more than any particular tool or title, is what it means to stay relevant in the future of UX research.
"AI doesn't have to kill our profession," Nastia said. "It can help you amplifying your impact—but with only one condition: if you're going to be proactive stepping into this space."
