The Real Impact of ChatGPT on UX Research
You’re not a UX Researcher anymore.
LLMs have fundamentally changed how research projects end up on your plate.
I’m not talking about using AI to summarize user interviews or pull the perfect quote from your research repository. That’s small, everyday efficiency stuff.
I’m talking big picture here about how your colleagues interpret your role as a UX Researcher today and how they’ll work with you moving forward -- which has changed irreversibly since the adoption of LLMs.
Understanding these new rules and expectations is key to thriving as a researcher moving forward.
There’s no going back…
The Black Cab: Researchers Before ChatGPT
Before generative AI arrived, UX Researchers were in the driver’s seat.
Senior leaders brought UX Researchers their tough questions and strategic objectives to translate into a robust research plan.
They set the destination. You figured out how to get there.
Not anymore.
This dynamic started shifting in late 2024. I first noticed it during onboarding calls with UX Researchers joining OpinionX:
“I never thought I would be asked to ask end users about pricing. I’m a UX Researcher and I’m finding I do ‘crossover’ market research more often than I expected.” — UX Researcher with 5+ years of experience.
“And now they’re telling me to run pricing and conjoint surveys, which is the marketing team’s job, not mine.” — Research Director at a >$1bn company.
“I ran [a conjoint survey] through a research agency once before but now they expect me to design and run and analyze all this work on my own. It feels like I’m way out of my depth.” — Senior UX Research Manager, NYSE-listed tech company.
These leaders hadn’t spontaneously enrolled in User Research 101 on Udemy or binge-watched YouTube explainer videos after putting their kids to bed.
Yet somehow, they were confidently requesting advanced research methods by name -- methods they almost certainly had never heard of before.
What had changed? Why were UX Researchers suddenly being assigned advanced research methods for high-stakes quantitative projects with little notice or support?
The culprit was none other than ChatGPT, of course.
Before ChatGPT, UX Researchers were like London’s Black Cab taxi drivers; trusted to use their deep knowledge of every street in the city to find you the best route to your destination.
Today, the UX Researcher role has become more like an Uber Driver -- the most efficient route has already been mapped and it’s your job to follow it, preferably in the fastest and smoothest manner possible.
Your boss now uses ChatGPT to turn their strategic objectives into specific research methods, tools, and techniques, bypassing you in the process.
But this shift didn’t start with ChatGPT. It only accelerated it.
Let’s look at what led us here and what it means for UX Researchers moving forward.
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How Product-Led Growth Made Mixed-Methods Research Inevitable
Product-Led Growth (PLG) began reshaping software strategy in the late 2010s. It introduced three key shifts:
Free tiers and/or free trials became baseline expectations.
Purchasing became self-service for entry-level price points.
End-users started buying tools to solve their own problems, rather than relying on top-down procurement.
These changes pushed user experience (UX) to center stage. Before PLG, user experience was mostly treated as a way to reduce churn post-purchase. Today, UX itself is a key driver of revenue growth -- especially in freemium and free-trial models.
“If the buyer is now the end user, then user experience is your new salesperson” — from my January 2021 blog post How to do Mixed Methods Research without being a Quant Expert.
In the early 2010s, UX Research was deeply qualitative in nature, with a toolkit centered on usability studies, user interviews, and thematic analysis. Most UX Researchers came from cognitive science backgrounds, with PhDs in psychology and HCI being particularly common.
Once UX got its seat at the leadership table, researchers encountered a problem: the leadership team’s lingua franca was quantitative. UX Researchers needed hard data, not anecdotes, to participate in shaping key decisions.
To stay relevant in this new dynamic, UX Researchers had to adapt. Mixed methods research became the bridge. Its qualitative side ensured depth and nuance, while quantitative insights enabled scale and impact.
This shift in the role of UX also made it necessary to bring research in-house. UX had become a continuous driver of growth, not just something you tested sporadically.
Back then, there were three main types of in-house researchers: the UX Researcher, Market Researcher, and Consumer Insights Manager. Each had their own methods, skillsets, and focus areas -- often with little overlap, especially between UX and Market Researchers.
The traditional division of labor across these roles had made sense in the agency world, where large teams allowed for deep specialization. But in-house research teams were rarely that big. Sometimes, a single researcher was expected to support an entire product org, even in companies generating tens of millions in revenue.
In that context, methodological flexibility wasn’t optional. It was a necessity.
As more UX Researchers expanded their toolkit to include quantitative methods, a new kind of researcher emerged who could conduct user interviews and run a conjoint analysis survey in the same week. Differentiating this hybrid researcher gave rise to a new term: Mixed Methods Researcher.
I first wrote about this shift in late 2020, predicting that the 2020s would be the decade of Mixed Methods Researchers. At the time, only 7 people on LinkedIn had “Mixed Methods Researcher” in their title. Today, that number has grown to over 1,500 and it continues to rise.
The shift from "UX Researcher" to "Mixed Methods Researcher" was, for many, a clever act of professional self-preservation. This became a real factor during the 2022 tech layoffs, where those who had already upskilled in quant methods were more resilient to job losses. They didn’t just do more -- they could speak the quantitative language of their leadership team.
As mixed methods became the new gold standard in tech, the differences between UX Researcher, Market Researcher, and Consumer Insights Manager began to blur. The new archetype was the generalist in-house researcher; one person capable of completing the work that had required three job titles.
Although Product-Led Growth expanded the scope of what UX Researchers worked on, the process within their role had not changed drastically. Colleagues still brought their big strategic questions to the research team, expecting expert guidance on designing the right study that could find the answers they needed.
Researchers were still the trusted Black Cabbie…
Until ChatGPT arrived and everything changed.
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The Uber Driver: The Post-ChatGPT Researcher
By late 2024, LLMs had been widely adopted. ChatGPT boasted an active userbase of over 300 million people. With this proliferation of AI came a subtle but significant shift in how people collaborated with in-house UX Researchers.
This was when I started hearing the same story again and again from researchers being asked to run projects that were more advanced, quantitative, and outside their comfort zone than they had ever done before.
Tasks that once meant running a simple survey were now conjoint analysis projects. Turning survey results into charts had become segmentation or clustering analysis requests. Preference testing design mockups had been replaced by price sensitivity testing. The expectations and complexity kept growing…
UX Researchers weren’t the ones choosing these research methods. Their role in research design, once a core part of their work, was increasingly bypassed.
Instead of being the London Black Cabbie, trusted to use deep expertise to navigate any research challenge, the modern UX Researcher is being treated more and more like an Uber Driver -- expected to follow the AI-generated route to the destination as fast as is legally compliant.
ChatGPT didn’t simply change the tools and methods researchers are expected to use (that shift had been kickstarted by PLG years before). AI changed how the researcher’s role is perceived within the company.
With ChatGPT, your Chief Product Officer now figures out that conjoint analysis can help optimize your product’s recommendation engine. Your Chief Design Officer realizes they can create personalized paywalls based on a pairwise comparison survey that uncovers each customer segment’s top unmet needs. Your CEO learns that a survey combining Gabor Granger and MaxDiff can identify which existing features have the most potential as add-on purchases.
These aren’t hypotheticals. The stories from UX Researchers were a noticeable pattern by late 2024, and when I look at our website attribution data for the same time period, I can see it right there in the numbers too.
The volume of signups to OpinionX coming from LLMs began to grow exponentially around Q3 2024. Soon LLMs will surpass Google Search as OpinionX’s top source of new researchers -- driven without a doubt by non-researchers using AI to discover research methods they’d never heard of before and the asking their in-house researchers to go run these projects on OpinionX.
There’s no rolling ChatGPT back into its box.
This shift in how research projects are initiated, and how your expertise is viewed, is here to stay.
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More Strategic Research, Less Strategic Control
I’ve focused on some obvious trade-offs here for researchers:
You have less control over which methods you use.
You’ll feel out of your depth more often, pushed into advanced methodologies you’ve never used before.
You’re expected to master new techniques on the fly, regardless of their complexity or whether your current tool stack can support them.
But there are also upsides to these changes:
You’re working on more strategic research than ever before.
Your work is closer to the heart of product, pricing, and growth decisions.
Your voice at the leadership table is getting louder.
In a world where AI commoditizes software development -- where building product is 10x easier, faster, and cheaper -- knowing what to build, for whom, and why becomes your company’s competitive advantage.
And that competitive advantage can only come from excellent user research.
This is why my recent newsletter posts here have been guides to pricing research methods like Gabor Granger, Conjoint Analysis, Van Westendorp, and Marginal Willingness to Pay. It’s why I’ve been sharing real case studies showing these methods in action. It’s why we’ve been expanding OpinionX to support more advanced research methods as quickly as possible. It’s why I called this newsletter The Full Stack Researcher all the way back in 2020.
It’s not just about keeping up -- it’s about staying indispensable.
This shift isn’t temporary. It’s structural.
Embracing it is now part of what it means to succeed as a User Researcher.
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About The Author:
Daniel Kyne is the Founder & CEO of OpinionX, the survey platform for product research. Thousands of researchers use OpinionX to measure what matters most to their most important customer segments — enabling advanced research like conjoint, maxdiff, and segmentation analysis on an easy-to-use platform.
→ Try OpinionX for free today