Market research trends: What’s changing in research today

Market research and user research are distinct disciplines, but if you look at the questions researchers are being asked to answer today, the lines around the work are getting harder to draw. Researchers are moving between customer, product, market, and business questions that no longer fit neatly under one label.

That came through clearly in Maze’s State of Market Research Report. Researchers told us they had taken on an average of 4.5 additional responsibilities over the past two years, spanning market research, customer insights, strategy, analytics, competitive intelligence, AI governance, and tool management. And that broader role is only one part of the story.

Our study surfaced three trends happening in market research today:

1. Research demand is stretching resources
2. AI capacity is outpacing confidence
3. Strategic influence has a decision window

Taken together, they paint a picture of a research function with more reach and more ways to work—but also new challenges around what that added capacity means.

Demand can feel flattering. More people want research and more teams see its value—then you look at the queue.

Seventy percent of participants reported a rise in research requests, and 78% said expected delivery speed has increased over the past two years. At the same time, 64% said they now support more business functions, along with increased project complexity.

A market-entry decision might require customer feedback, product data, competitor activity, and market trends. Researchers increasingly have to bring those inputs together, even when they sit across different teams, methods, or disciplines. While it brings researchers closer to strategy, it also expands what they’re expected to own. And those resources haven’t kept pace with demand.

Across the demand measures tracked in the report, an average of 69% of participants reported increases, compared with just 27% who saw budget or headcount growth. More than half (57%) said their team doesn’t have enough resources to meet current demand.

That pressure changes how research gets done, pushing traditional disciplinary boundaries and requiring researchers to take on new responsibilities.

If adding more people and budget isn’t always an option, where does the extra capacity come from?

With research teams already stretched, AI steps in to support. 84% of participants agreed that AI increases research capacity, and teams are already using it across planning, analysis, synthesis, reporting, and knowledge management. For researchers working through a growing queue, that matters.

AI can make it possible to work across larger datasets, pull together insights from previous studies, analyze different sources, and explore questions that once depended on other teams. Hugh Lagrotteria, Staff Market Researcher, described using large language models (LLMs) to run SQL queries, merge old datasets, conduct correlation analyses, and move beyond the standard survey. As he puts it, “LLMs have enabled me to expand my scope beyond ad hoc primary research.”

AI isn’t only helping researchers do the same work faster; it’s also changing the work they take on in the first place. But capacity has arrived before many organizations have worked out their AI playbook. Only 55% said their organization has clear standards for when AI can and can’t be used. Just 17% said AI is integrated across the research process with clear ownership.

Ninety-six percent of researchers agreed that reviewing AI-generated outputs is essential. So while AI can help answer more questions, people still have to decide which answers they can trust. That judgment matters even more when speed makes an answer available in seconds.

Extra capacity only gets you so far. It also needs to reach the business to influence decisions.

Most participants say their organizations already trust research and see its business value:

If research is already trusted, what determines whether it influences the decision? Timing.

Only 33% of participants said research is involved when a problem or opportunity is first being defined, while another 27% said it enters while teams are exploring their options. The window of influence sits between those stages.

Forty-five percent of participants said all three of their most recent research projects informed a decision or led to action, and another 35% said two of their last three did. And 82% agreed that research helps their team learn over time, rather than answering one question at a time.

These findings point to a larger opportunity: connecting what the organization has already learned with the decisions it’s making now. At Maze, we call this approach a system of learning:

That is what makes research part of how an organization operates: it shapes the decision at hand and strengthens the foundation for the next.

Research has more influence across the business, and AI is giving teams more capacity to meet growing demand. How teams use that capacity changes what comes next. Our findings suggest it matters most while decisions are still open. And getting research into that window doesn’t always mean running another study; sometimes the evidence already exists.

When previous research is easy to find and use, teams can build on what they know and focus fresh research on what’s still uncertain. Each project becomes part of a growing body of knowledge, rather than another piece of work that ends with the readout.

Researchers are already working across more of the business. Making that work count means knowing what to speed up, what deserves a closer look, and how today’s learning can give tomorrow’s decision a better starting point.