The promise of AI solutions is growing louder in every industry. In customer research, it hands automated interviews and instant synthesis to brands that never had the budget, bandwidth, or headcount to run meaningful studies before.

But that leads to a newer and somewhat worrisome question: where does AI leave researchers?

At Listen, we think that’s the wrong question. AI is the newcomer. It has to prove its place in the room to the research team, not the other way around. 

A trusted research assistant becomes valuable once it has spoken to real people, asked good follow-up questions, performed great analysis, and backed up its claims with the data. Rather than displacing other researchers on the team or pushing them into a purely managerial role, it allows them to spend more time listening to customers, understanding what their audience needs, and brainstorming ways to serve them.

What does this look like in practice?

AI-moderated research has drastically reduced the time it takes to run a study. Recruiting can be handled online, scheduling can happen asynchronously, each interview can be conducted at whatever time is most convenient for the user, and synthesis is nearly instantaneous. A study that would have taken weeks or months using traditional research methods happens in hours. 

This means research is now accessible to teams in ways it wasn’t previously, and the teams that were utilizing it can now do more.

We’ve seen this firsthand. Microsoft’s research team used Listen to collect global customer video stories in a single day, work that previously ran six to eight weeks. Emeritus scaled its interview data roughly tenfold. KJT Group moved through exploratory research phases four times faster than before.

But speed is only one part of the equation. A researcher’s reputation is built on what they’re able to discover, and whether those findings are trustworthy. 

That’s where AI collaboration has to be put to the test.

What a research collaborator has to prove

What would you demand of a new hire before you let them run a research session on your behalf? You’d probably ask them about who they planned to talk to and what they would ask (and whether they knew what kind of follow-up questions needed to be considered). If they came back to you with results, you’d probably want to check their work, and you’d expect to see where they got that data.

AI owes you the exact same thing. It has to be able to:

Only once all of these things are true can AI truly be integrated into the team.

How do researchers and AI work together?

Once a trusted AI collaborator is running interviews, it allows researchers to spend more time doing the most critical and essential work. They decide what studies need to be run and how to frame the questions that need to be asked. They decide what kinds of individuals the study needs to hear from. They connect the findings to the decision that needs to be made and explain to leadership how it helps them make it.

That’s the work that researchers have always done. The only difference is that now it’s not in competition with the logistics of getting there.

Jane Justice Leibrock, Head of User Experience Research at Anthropic, found the freedom Listen granted liberating.

“Our researchers’ time is one of the scarcest commodities,” she said. “So if we can take the same person’s research skills and insights and quickly scale that up in terms of the scope, that’s very valuable.”

Rather than looking at AI as a researcher replacement, she sees it as a researcher enhancement. In a way, it is allowing more people to take on the role of researcher in various teams, creating studies that address their questions and blend into their workflows. 

It’s not just Anthropic. It’s true at every organization that’s using AI in research the right way — as a trusted research collaborator. 

At the end of the day, AI-moderated research isn’t just about getting to talk to more customers; it’s about connecting with more of them on a deeper level and unlocking new understanding.

AI-moderated studies mean more researchers, not fewer

The question shouldn’t be whether or not AI will replace researchers. That’s not going to happen.

The better questions are the same you’d ask of any new colleague:

• Who did you talk to? 

• How did you follow up on interesting answers?

• Can I see the data behind your conclusions?

Systems that pass those questions become collaborators worth trusting. Researchers who work with them get more time listening to customers rather than getting caught up in the logistics of arranging that listening.

Listen was built and shaped by researchers, and is now trusted by enterprise research teams around the globe. See how your research team could use Listen. Book a demo.