The State of Research & AI

Democratization isn’t diminishing the role of insights; it’s expanding its influence. As AI gives more people access to customer knowledge, insights professionals are evolving from the gatekeepers of research into the experts who help organizations turn that knowledge into better decisions. This shift was evident in our conversations at TMRE, where respondents repeatedly identified a need for more actionable recommendations, stronger strategic guidance, and a clearer connection between research and business outcomes. As one respondent put it, “We need to be consultants influencing business decisions and most of that has nothing to do with methodological expertise.” The value of insights is increasingly measured not by the research produced, but by the decisions it enables.

This was echoed by panelists from HP and Peloton, who described how democratizing research has freed their teams to focus on more complex strategic questions and engage earlier in decision-making. But this expanded influence comes with expanded expectations. Researchers are being asked to know more, move faster, and influence across more parts of the business. The opportunity is enormous: a more customer-centric organization, powered by insights expertise that reaches further than ever before. The challenge is ensuring insights teams have the skills, confidence, and capacity to deliver on that promise. Across TMRE, we also heard clients describe the emerging role of insights as an orchestrator or guide: helping stakeholders who now have direct access to data ask better questions, interpret what they are seeing, and connect customer knowledge across functions that once operated in silos.

AI promises unprecedented efficiency, but that efficiency isn’t necessarily translating into breathing room. It’s unlocking research that previously wasn’t feasible, while raising expectations for how quickly insights should be delivered. Speed and AI adoption were recurring themes in our TMRE survey, with 25 of 32 respondents explicitly referencing AI or automation when asked what was changing their work most. Several also pointed to shorter timelines, resource constraints, and growing pressure to do more with less. Microsoft’s panelist captured the paradox: the ability to conduct research in a fraction of the time doesn’t necessarily mean less work. Instead, it can mean more requests, more decisions about what to prioritize, and more research that wouldn’t have been commissioned before. That makes prioritization a core capability: when AI expands what is possible, insights teams have to become more deliberate, even ruthless, about which questions they answer.

The emotional toll of that transformation was equally evident. TMRE respondents described feeling excited, curious, hopeful, uncertain, nervous, and overwhelmed—sometimes all at once. They also raised concerns about job security, the pace of upskilling, and how the next generation of researchers will develop foundational skills when AI increasingly handles the groundwork. The panelists acknowledged the same tensions within their teams, emphasizing the importance of shared learning, vulnerability, and recognizing that people adapt at different speeds. The next challenge for insights leaders may be less about accelerating AI adoption and more about making that acceleration sustainable. The organizations that thrive won’t necessarily be those that move fastest, but those that help their people keep up without burning out.

In an industry racing to automate everything, something unexpected is happening: the desire for old-school, human-to-human research is growing. AI can simulate consumers, synthesize thousands of conversations, and uncover patterns at unprecedented speed. But as these capabilities become ubiquitous, the things AI can’t replicate become increasingly valuable. Our TMRE survey revealed a clear distinction between the tasks researchers are eager to automate and the human connection, empathy, and judgment they’re determined to preserve. Rather than signaling the end of traditional research, AI may be making the case for getting closer to real people than ever before.

This tension came to life on our panel, where HP described a renewed appetite for ethnographic research, including a recent study observing consumers struggling to set up printers in their homes. Watching real frustration unfold was powerful enough to make senior engineering leaders visibly uncomfortable—an impact no AI-generated summary could quite replicate. Peloton similarly emphasized the enduring importance of human-led lab research and ethnography, even as AI becomes embedded in its workflows. The irony is that the more AI can tell us about consumers, the more valuable it becomes to experience their reality firsthand. And with AI accelerating so much of the research process, insights teams may finally have more opportunity to invest in the deeply human experiences that make insights impossible to ignore.

Organizations have spent years accumulating customer knowledge, yet much of that intelligence remains buried in past projects, presentations, and disconnected data sources. AI-enabled knowledge management is beginning to change that equation, making existing research more accessible and actionable. At TMRE, respondents called for better synthesis across research projects, a single source of truth, and, perhaps most importantly, “Data at the moment decisions are being discussed.” The message is clear: businesses don’t necessarily need more data. They need better ways to access, connect, and apply the knowledge they already have, at the moment it matters.

In our panel, HP and Peloton described how AI-powered research repositories are enabling their teams to rapidly draw on years of existing research, contribute to strategic conversations earlier, and identify where genuine knowledge gaps remain. This represents a fundamental shift from research as a series of standalone projects to an always-on intelligence capability. Historical knowledge provides context, while fresh primary research fills gaps, tests assumptions, and keeps insights grounded in today’s consumers. The opportunity is to get smarter about what existing evidence can be trusted and reused, what needs updating, and when the right answer is to go back to real people.