Many UX researchers worry about AI’s impact on their job role. (As many as 68%, according to recent estimates…) It’s understandable. No one wants to be replaced by a bot.
The good news? AI isn’t taking over your role. It’s taking on tedious tasks you never liked doing in the first place.
Think meeting transcriptions, drafting screeners, or surfacing key themes across dozens of disparate docs. In turn, you get time back to spend on more strategic activities, such as storytelling, building narratives, and influencing internal decisions.
“AI isn’t replacing researchers,” explained Maryam Maleki, Principal UX Researcher at Microsoft, in a recent article. “But it is changing what it means to be an efficient, high-impact one.”
Read on to see how AI can help you level up your work.
Or skip over to our latest report about The Modern Research Workflow to learn how AI will transform research next year.
Pain Point No. 1: Determining the Right Research Questions
The Problem: One of the most challenging aspects of UX research is determining which problems to solve.
Sometimes, it’s hard because of the sheer number of potential questions. Other times, there are organizational politics at play. Teams may have incentives attached to the outcome of an inquiry. UX researchers have to peel back the layers to find out why a question is being asked — and then determine if it’s worth pursuing.
Going down the wrong rabbit hole can waste your time and the company’s money, jeopardizing future project approvals.
Where AI Can Help: Use AI tools to aggregate and surface research questions to pursue based on your dataset. Want to expand your thinking about the problem? Reverse engineer it. Ask AI for potential questions you haven’t considered.
“What are the questions we haven’t even thought to ask yet?” said Janelle Estes, Experience Design Platform Director at Bentley University.
Then, review it using your own judgment and context, like the top priorities for executive leadership.
Not sure what those are? Schedule collaborative workshops or review sessions with leadership to validate and prioritize the questions based on their goals.
Pain Point No. 2: Research Silos & Fragmented Data
The Problem: Customer insights and research functions (such as UX, CX, market, and data science) are fragmented across departments. There is limited knowledge sharing because each team has its own preferred tools and methods.
The result? Duplicative efforts.
“The amount of siloed systems that we’re using increases the time it takes for data to get to a person, and that lags time for them to make those informed decisions,” said Emily Chee, Sr. UX Researcher at Entertainment Partners.
Where AI Can Help: Create a modern research repository that leverages open APIs, AI, and large language models. This breaks down silos and allows everyone to operate from the same source of truth. Choosing a repository with AI-powered search capabilities also reduces the dependency on using tags to find things.
When the Twilio research team started building their repository, Kate Pazoles says they spent a lot of time creating tags, labels, and a comprehensive taxonomy to make everything searchable. “Now, we don’t even really have to do that because AI can surface those things for us,” Kate said.
Find out how to get the most out of your research repository.
Pain Point No. 3: Research is Too Slow for Product Development
The Problem: Planning, recruitment, execution, and reporting are time-consuming. Unfortunately, product development can’t wait. They need “good enough” insight to make the right call and release the product to market. If the research team can’t provide it, they’ll go by gut instinct instead. Once the research is ready, it will already be obsolete or serve only to support a previously made decision.
Where AI can help: Use AI to draft research plans, automate recruitment and scheduling, and generate quick summaries.
“AI has taken things from two weeks down to days,” says Lauren Nitta, Director of Pricing & Strategic Operations at Netwrix Corporation.
You can then share the key takeaways with executives, along with recommended next steps. When the project is done, add the insights to your repository so it can help answer future questions, too. Then, when you get a new inquiry, you can point non-researchers to check the repository first to see if an answer is already available.
Pain Point No. 4: Poor Communication of Insights
The Problem: Insights are often buried in long reports or shared at the wrong time. Executives spend up to 72% of their time in meetings, which means they have very little left over to sift through a 10-page PDF. Instead, they just want the highlights: what’s the problem, why should they care, and what should they do about it?
Unlike researchers, execs don’t value data just for the sake of it. They care about how it can be used to inform decisions that make the company money.
Where AI can help: Generate summaries, visualizations, and reports on demand using AI. When executives have an inquiry, they want it addressed yesterday — even if you just found out today.
Since we can’t time travel, steal Lauren’s strategy to deliver actionable recommendations faster. Using an AI chatbot, she talks out her learnings and asks it to synthesize her thoughts with the prompt: “Turn this into an executive summary for XYZ audience.”
Pain Point No. 5: Recruiting & Interviewing Participants
The Problem: Recruiting and interviewing participants is a time-consuming, slow, and inconsistent process. The task is increasingly difficult when you have a niche audience or need to find people who meet specific demographic criteria. The right screener questions can improve the quality and relevance of your interview subjects, but those take time to craft, too. And if you finish the interviews and realize you didn’t get what you needed? You’re right back at square 1 — everyone’s least favorite shape.
Where AI can help: Use AI-driven targeting and outreach tools to identify and engage the right participants. Build partnerships with expert networks and automate incentive management, while maintaining human oversight for relationship-building with hard-to-reach personas. You can also use AI for a head start on some of the interview elements, such as screeners or scripts.
“The place where we are using AI right now that’s working really well is getting a jump start on a script or a screener,” said Morgan Koufos, Lead UX Researcher at User Interviews. “You still have to refine the questions, but you’re not starting from the blank page.”
Let AI Work For You, Not Take Your Work Over
You’re expected to deliver insights that shape strategy while keeping pace with the speed of business. When you use AI to automate repetitive tasks, you can focus on what matters most — driving decisions with real business outcomes.
That’s where the real opportunity lies for UX research. So why waste time on tasks that don’t contribute to it? Plus, the more you use AI, the better you become at new skills companies want: prompt engineering, AI literacy, and the ability to integrate AI tools into daily workflows.
Want to learn more ways AI can work for you without taking over? Check out our latest report, “The Modern Research Workflow,” to learn how research leaders are incorporating AI into their everyday processes.
