Demand can feel flattering. It means more people are recognizing the value of research. But when demand outpaces capacity, time, and budget, that good feeling doesn’t last. Today, researchers support more of the business and draw on more kinds of evidence to answer increasingly complex questions.

Research has trust, but not enough resources

A user researcher may now be responsible for market research, customer insights, competitive intelligence, strategy, analytics, or AI governance—sometimes all within the same role. Participants told us they had taken on an average of 4.5 new responsibilities in the past two years. That’s a lot to squeeze into one job description.

The boundaries around the role are blurring because the evidence businesses need rarely sits neatly in one discipline. A question that once called for a survey might now require customer conversations, behavioral data, market trends, and competitive context. And qualitative methods that once meant exploring the experiences of a small group in depth can now surface rich insight across hundreds of people. Connecting those signals can bring researchers closer to the decisions shaping products and businesses. The job now involves explaining where the evidence agrees or conflicts, then helping the business decide what to do with it.

Researchers are taking on more

The scope isn’t the only thing expanding. Over the past two years, 78% of participants said delivery-speed expectations increased, 70% saw more research requests, and 64% faced more complex projects. Meanwhile, just 30% reported increased budgets and 24% reported increased headcount. Across the measures we tracked, rising demand outpaced rising resources by 42 points.

That squeeze appears across roles and company sizes. When urgent requests keep piling up, longer-term research and follow-up work slide down the list. More gets delivered, while less learning moves forward.

When the research queue keeps growing and the resources don’t, extra capacity sounds pretty appealing. That’s where AI enters the chat.

Eighty-four percent of participants agreed that AI increases research capacity. Teams are using it across study planning and synthesis, as well as to make previous research easier to find. Some researchers are even working with methods and data sources that would once have required support from other teams.

AI is giving researchers more room in stretched workflows. But room to do what—and under whose rules?

AI is creating more room but outpaces structured adoption

AI use is moving faster than the shared standards needed to guide it. Only 55% said their organization has clear rules for when AI can and can’t be used, and just 17% described it as integrated across the research process with clear ownership.

Even among organizations with the most established AI practices, adoption varies by task. AI is widely used to support planning and make sense of findings. Teams are more selective when it begins to shape the evidence itself.

Human review remains almost universal: 96% agreed that reviewing AI-generated outputs is essential. Researchers still define the objective and decide what role AI should play. They remain responsible for judging the evidence in the context of the decision.

AI may take on more of the work, but accountability still sits with people.

Researchers have already earned trust. Eighty-nine percent agreed that research is viewed as a strategic partner and 87% said senior leaders trust its findings. That trust only goes so far if research arrives after the decision has already been made.

Only 33% said research usually enters while a problem or opportunity is first being defined. Another 27% said it arrives while options are being explored. Our analysis found that strategic influence remains almost unchanged across those two stages. Then it drops as soon as a preferred direction begins to take hold.

Research has the most influence before a decision and direction are set

Research can arrive at the right time and still fall short if the findings don’t lead anywhere. Four in five participants said a majority of their three most recent projects informed a decision or led to action. Influence continues when researchers connect the evidence to a recommendation and stay involved as the team responds.

The learning also needs to travel. Eighty-two percent agreed that research helps their team learn over time, and 74% said findings are shared beyond the team that commissioned the work. Yet only 68% agreed that people across the organization can access and use previous research.

Usable research needs context. People have to know whether an insight still applies and when the decision calls for new evidence.

Research has earned credibility. Turning that trust into consistent influence depends on whether it reaches decisions in time and remains useful beyond the original project.

Research doesn’t have to be in the first meeting, but it does need to arrive before the answer is set. That won’t always require a new study. When previous research is easy to find and use, teams can bring what they already know into open decisions and focus new research on the remaining gaps.

As the boundaries between research disciplines blur, researchers are working across a wider mix of customer, market, product, and competitive evidence. AI can make that evidence easier to navigate, while researchers provide the context and judgment needed to use it well.

A system of learning connects that knowledge with the decisions being made today. Each study can shape the decision at hand and give the next one a stronger starting point.