5 Workflow Hacks UX Researchers Swear By

Each week, research teams lose 15-20 hours on manual, repetitive tasks. No wonder executives now have the expectation that you’ll get work done faster as AI becomes more mainstream. They’re no longer willing to wait weeks to find out the outcome of a query.

Research also has to keep up with the pace of business. So how do you do your job faster, without jeopardizing the quality of your work? 

Our community of UX researchers has the answers. Read on to see some of their favorite research workflow hacks with us.  

Want to skip the hacks and dive deep right away? Check out our full guide, "The Modern Research Workflow," and find out how AI will transform the research tech stack in 2026.

1. Use a third-party network for recruitment

Recruitment is the most challenging and impactful part of the research process, especially in B2B or niche markets. Finding the right participants can make or break a project. 

Lauren Nitta, Director of Pricing and Strategic Operations at Netwrix, uses third-party networks to find the right folks, even though it doesn’t run cheap. For a research project with C-suite participants at a previous employer, Lauren’s team spent roughly $1,300 per interview — which added up to $150,000 annually.

“I'm talking to people like the CTO at Uber,” Lauren said. “At the end of the day, it's an investment, but it was a high-quality response.” 

Once her company was on board with investing in the channel, Lauren said it made a huge difference in research impact.

2. Leverage AI for sentiment analysis

Multiple researchers described how AI research tools (like Marvin!) have drastically sped up and improved the qualitative analysis phase. Tasks that once took days or weeks — like manual tagging, coding, or searching for quotes — have been reduced to minutes.

For example, User Interviews Lead UX Researcher Morgan Koufos uses AI to automate thematic analysis. 

“You get the theme started more quickly,” Morgan said. 

However, all the UX researchers we spoke to agreed that human expertise is still needed to guide AI, validate outputs, and ensure context and nuance aren’t lost. 

As Bentley University's Experience Design Platform Director Janelle Estes puts it, "The human in the loop is really important, especially for qualitative research that can be very subjective."

3. Keep a human as the lead interviewer and moderator. 

Speaking of humans in the loop, interviews are one area many researchers felt shouldn’t be outsourced to a bot. 

Morgan stresses that, while AI can automate many research tasks, human-to-human interaction in interviews is irreplaceable. She compares it to customer support: "Half of it is just being heard by a real person." 

This connection builds trust, elicits deeper insights, and demonstrates to customers that their voices matter. 

“There's a difference between surface-level interviews and answers and stuff that is really going to drive your business forward,” Lauren said. 

4. Get iterative feedback throughout a research project. 

Many researchers take a more collaborative approach to give their work greater traction.

Morgan uses Miro boards for group brainstorming. (Fun fact: Miro is one of our 25+ product integrations.)

Then, she has everyone take 15 minutes and write down their favorite ideas. Once that’s done, she holds meetings with each department to build out action plans. She emphasizes the importance of iterative feedback, sharing drafts, and meeting with stakeholders by department to make insights more actionable.

This strategy is part of an increasingly popular trend called “research roadshows.” You curate your findings, like a “greatest hits of research” playlist. Then, you “play” them for key stakeholders in person at the opportune moment, like before a big decision. This roadshow approach can be time consuming, but it helps research drive real business outcomes.  

5. Use AI as a “thought partner,” but challenge its output.  

Researchers like Lauren and Janelle highlight that AI isn’t just a tool for speed. It has the potential to challenge our biases by surfacing themes we may have overlooked.

We all enter into projects with our own preconceived hypotheses or echo chambers. But getting value from AI requires new skills: effective prompting, critical output evaluation, and knowing when to rely on AI versus human judgment.

Lauren said she’s trained her team to use AI with greater intentionality.

"It's not just, ‘Hey, go use ChatGPT,’” she said. “You really have to think intentionally about how you use prompts." 

Janelle uses AI as a "thought partner," comparing and challenging its outputs with her own analysis.

There are so many ways to make your workflow work better for you. Use AI to automate tasks you never liked doing, get a jump start on a first draft, or a new perspective on a problem. The most important thing is to never stop being curious and open to trying a different approach. You might find one you like better than “the way we’ve always done it.”   

Get more proven techniques from your peers to optimize your workflow in our guide, “The Modern Research Workflow.”