We spent our time at The Market Research Event (TMRE) 2026 in Denver walking the floor and sitting in on sessions. In our recap of CRC Chicago, we noted that AI had moved from exploration to implementation across the insights industry. At TMRE, that move was official. The conversation has shifted from “Should we use AI?” to where to use it, how to implement it responsibly, and whether it is actually helping teams make better decisions.
For consumer insights leaders, marketers, and innovation teams, that last question matters more than which tools to adopt. When speed is available to everyone, the advantage shifts to the quality of the inputs, the strength of the evidence, and the judgment to know what is worth acting on.
Here are six takeaways from TMRE 2026, and what we think they mean for brands.
1. The real opportunity is learning from more consumers, more often
This was probably one of our favorite points of the conference. The teams that use AI to engage with more consumers more frequently and to create a continuous stream of learning will have an advantage. Using AI simply to do the same research faster, or to research less, misses the bigger opportunity.
For insights teams, this changes how the value of AI gets measured. Time saved matters, but the larger prize is a deeper and more current understanding of consumers, built from more conversations rather than fewer. Teams that treat AI mainly as a way to cut back on research risk knowing less about their consumers just as the pace of change picks up.
2. Quality starts with what goes into AI
For a while, the AI conversation was dominated by doing things faster and cheaper. Now the focus is shifting to quality: the quality of the inputs, the quality of the outputs, and ultimately whether the decision is getting better. Speed and efficiency are becoming table stakes. Quality is the next frontier.
One interesting question raised in Denver: what do consumers and organizations actually think AI is? How people conceptualize AI influences how they use it, what they trust it to do, and where they may overestimate or underestimate its capabilities.
For brands, the takeaway is that AI is only as valuable as the mental models and inputs behind it. Getting the inputs right, and being clear about what a tool can and cannot do, is where quality starts.
3. When AI multiplies the options, evidence and judgment matter more
One of the more provocative discussions was about how much of marketing is based on practices repeated for years versus things that have actually been proven to drive outcomes. There was a strong push toward evidence over conventional wisdom, and AI raises the stakes. Technology can scale an assumption just as easily as it can scale a truth.
More capability also creates a new challenge: prioritization. If AI makes it possible to generate, explore, and test dramatically more ideas, content, and opportunities, scarcity shifts. The question becomes what is actually worth pursuing, and judgment and prioritization may become more important, not less.
Human expertise and oversight also become more important as AI scales. AI can surface patterns, generate possibilities, and accelerate learning, but someone still needs to understand the business context and determine what the information actually means.
For brands, that puts a premium on two things: evidence that shows what actually drives outcomes, and people who can judge what is worth pursuing. A metric isn’t a decision.
4. Not every problem is an AI problem
There was still a real appreciation for traditional market research methods, particularly when the job is to observe actual behavior, understand context, or explore a genuine white space. Sometimes you need to watch someone struggle through an experience to understand the problem.
A related point came up over and over: start with the problem, not the technology. Nobody cares how much AI you have. They care about the problem you are solving. The fundamental job of insights is still to help the organization make better decisions. What is changing dramatically is how teams can get there.
For insights teams, AI and traditional research aren’t necessarily an either/or choice. The method should follow the question.
5. Insights has an opportunity to be embedded across the business
There was a lot of conversation about working cross-functionally and connecting insights more closely with marketing, innovation, product, and other teams. Rather than research being something that gets “delivered,” the opportunity is to create connected conversations and make consumer understanding part of how teams operate. This was also the first year TMRE was held alongside Content Marketing World.
Democratization is a big part of that shift. As tools become easier for non-researchers to use, insights can evolve from being the gatekeeper of research to helping the broader organization ask better questions, use better inputs, and interpret what comes back appropriately.
There is also an interesting tension for insights leaders right now. AI creates enormous opportunity, but insights teams are being asked to prove their value and work differently. A recurring theme was meeting teams where they are, working across functions, and demonstrating impact through the decisions insights enables, not simply the volume of research produced.
Leadership itself is changing, too. One discussion that stood out was about “taking the mask off” as a leader and acknowledging that nobody has every answer right now. With the pace of change, the expectation shouldn’t be a perfect decision every time. It is to build teams that can learn, adapt, and course-correct quickly.
6. Insights is moving toward foresight
As an industry, we are moving from insights toward foresight. It is not enough to understand where consumers are today. The opportunity is to understand where they have been, what they are trying to accomplish, what is changing around them, and what that could mean for the business next.
Another of our favorite ideas: future-proofing isn’t about perfectly predicting the future. The companies that win aren’t always the ones that correctly predict every wave. They are the ones that adapt early.
For brands, long-term strategy can be grounded in what isn’t changing (fundamental human needs, motivations, and behaviors), even as the way those needs are expressed continues to evolve.
Where Behaviorally Fits Into These Shifts
Several of these takeaways come back to the same discipline: learning from what shoppers actually do, then using that behavioral evidence to decide what is worth pursuing. That is the foundation Behaviorally builds on, with predictive AI tools backed by a first-party behavioral database of more than 233,000 validated packs and 2.4 billion observed behaviors, and decades of experience grounded in behavioral science.
PackPower Score brings evidence to the prioritization question, with a single-number prediction of a pack’s sales performance before launch. myBehaviorally delivers continuous, always-on packaging insights, supporting the shift from one-off projects toward the kind of ongoing learning we heard about at TMRE. And Studio B combines human insights and AI through its three-step methodology of Humanize, Design, and Predict, guiding brands from design to shopper impact.
The thread we kept coming back to after TMRE 2026: our jobs haven’t fundamentally changed. We are still trying to understand people and help businesses make better decisions. But the way we do that is changing dramatically. AI gives us the ability to learn faster, engage more people, and explore more possibilities. That doesn’t make human understanding less important. It makes quality, evidence, context, judgment, and the ability to turn learning into action even more valuable.
Contact us today to learn more.
