Market researchers spend their careers explaining other people's behavior, but there is far less research on the researchers themselves: what keeps them in the field, what a hard week looks like, how their work lands with stakeholders, and how much of the job they would let AI take on. This study set out to build that profile through in-depth interviews with 76 US market researchers working in consumer insights and market research, most with eight or more years of experience.
Of everything in that profile, attitudes toward AI showed the sharpest tension. 91% of these researchers are open to using AI in their workflow, yet only 29% trust AI-generated insights as much as traditional research, and just 18% feel the same about AI-written discussion guides, surveys and stimuli.
That gap explains how they want AI to fit into the job. They would gladly hand it the manual work of cleaning data, transcribing and formatting decks, as long as the judgment calls stay with them. The same protectiveness extends to people outside the function, with 68% uncomfortable with product managers running their own studies.
TL;DR: market researchers like their jobs, feel pressure mostly from deadlines and stakeholders, and will hand AI the manual work only once it proves it's accurate and keeps them in the loop.
Key takeaways
91% are open to AI in their workflow, but only 29% trust AI-generated insights as much as traditional research.
65% of those who described what AI would have to do to earn their trust said it must prove it's accurate. 52% want a human checking the output.
68% are uncomfortable with product managers running their own research, the lowest comfort score in the study.
Only 49% say non-researchers understand their work well.
80% enjoy their job, but only 21% would give a college senior an unqualified yes to a career in market research.
Open to AI, slow to trust it
On a five-point scale, almost every researcher said they're open to AI in their workflow. Trust scored much lower. For both AI-generated insights and AI-generated content, the most common answer was the middle option, "trust somewhat."
When asked which single step of the research process feels most manual, the top answer was data cleaning and organization (26%), followed by presenting findings to stakeholders (18%). Those are the tasks researchers most want AI to take on.
The open-ended answers drew the same line. Transcription, theming and deck formatting topped the list of things they'd hand off tomorrow. Deciding what the findings mean stayed with the researcher.
What AI has to prove before researchers hand it the work
Researchers were asked what would have to be true before they'd trust AI with their most frustrating manual step. Among researchers who answered, four conditions came up again and again:
Prove it's accurate (65%), often by checking AI output against human work.
Keep a human in the loop (52%), with a person reviewing or verifying.
Understand context (44%), including nuance, brand context and how respondents said things.
Show its work (15%), with citations or a trail back to the source.
Only 17% said they'd never fully trust AI for that step, and 15% said they already do. The rest would hand AI the work once those conditions are met.
Worried about the profession, less about themselves
On a standard scale measuring attitudes to smart technology and AI, completed by 52 researchers, 58% agreed AI will reduce the number of roles like theirs. Only 23% were personally worried about being replaced.
When asked to imagine their role being handed to an AI agent in 18 months, half of those who answered described calmly moving into an AI-augmented research role. 21% described real fear.
Few plan to leave. 64% expect to still be in a research role in five years, and 11% expect to leave.
Most don't want non-researchers running their own studies
Researchers are more comfortable with AI helping them than with anyone else doing research without them. 68% are uncomfortable with product managers running their own studies, and 59% feel the same about designers. Those were the lowest comfort scores in the whole study, lower than any of the AI trust questions.
If you lead an insights team and you're weighing "research for everyone" tools, expect pushback from your own researchers. Their concern is who uses the tool.
Book a demo to see how Listen keeps researchers in control of AI-moderated studies.
Stakeholders are the harder problem
That protectiveness makes more sense once you look at the people researchers work with every day. Researchers feel their work matters: 68% say their organization values research, and 67% say it often drives business decisions. Yet only 49% feel their non-researcher peers understand what they do, which helps explain why they're wary of handing research to people outside the function.
The gap shows up most clearly in the readout. When asked about a recent time stakeholders pushed back on findings, ignored them or read them the wrong way, 58% of researchers who described one called it a communication or framing problem, and many blamed their own presentation. 42% said the findings contradicted what stakeholders already believed, and 38% pointed to low research literacy in the organization.
What makes the worst weeks bad
Stakeholders also shape the hardest weeks of the job. On a typical week stress is moderate, with 20% rating it highly stressful and only 8% rating their work-life balance as poor. But when researchers described their most stressful week of the past six months, 56% pointed to tight deadlines, 44% to stakeholder demands and scope creep, and 42% to juggling several projects at once. Recruiting problems and tech failures barely came up.
What makes a market research tool stick
Time is also the first thing researchers judge their tools on. Most are reasonably happy with what they have, with 63% rating their current tools good or excellent, though 28% are unhappy with their research budget. When researchers explained why they kept one tool and dropped another, they cited:
Saving time (63%)
Output quality and accuracy (58%)
Ease of use (52%)
Fitting the existing workflow (50%)
AI features (42%)
On the other side, 35% had dropped a tool because of a steep learning curve or an awkward interface.
The consumer insights researchers behind these answers
The people carrying this load are experienced and attached to the work. 71% have eight or more years in the field. 62% said they were drawn in by curiosity about people and behavior, though 40% fell into it through a first job, an internship or a recruiter.
80% enjoy their job, but fewer would steer someone else into it. Only 21% of those who answered would give a smart college senior an unqualified yes, and 60% would say yes with a caveat. Almost every caveat was some version of "learn AI," which brings the profile back to where it started: researchers see AI as part of the job's future, as long as they stay the ones running it.
Work arrangements, at least, are settled. 49% prefer fully remote work and 7% want to be in the office full time.
Where Listen fits
Listen runs AI-moderated interviews with real people, then codes the answers and builds the report. Researchers design the study and review the output. Every theme links back to the interview it came from, which covers two of the conditions researchers set for trusting AI: a human in the loop and a way to check the source.
What to do with this
If you lead an insights team, point AI at the steps your team calls most manual: data cleaning, transcription and deck building. Keep interpretation and stakeholder framing with your researchers.
Before you roll out an AI research tool, test its output against your team's own work on a past study. 65% of researchers want proof of accuracy first.
Plan the stakeholder readout as carefully as the fieldwork. Most researchers who described a pushback blamed how the findings were communicated.
Bring researchers in before you hand research tools to PMs or designers. 68% are uncomfortable with PM-led research, and they often hold the budget.
Methodology
The study interviewed 76 market researchers in the United States in April and May 2026, using AI-moderated video interviews on Listen Labs. Participants were recruited from a professional panel and screened to be employed, working in a marketing or market research and consumer insights function, with market researcher or research analyst as their primary role. Each personally performed at least one core research activity (such as qualitative interviews, survey design or brand tracking) and had used a research platform in the past 12 months.
Rating-scale and multiple-choice results use a base of 76. Open-ended answers were coded into themes for respondents who gave a codable answer, and one answer could fit more than one theme, so those shares can add up to more than 100%. The technology-attitude scale results use a base of 52. No individual responses are quoted.
