The stage is set: With TMRE 2026 quickly approaching, October 5-7 in Denver, the market research community is planning to gather for its annual conference. The keynotes have been highlighted, the major themes presented, the numerous sessions and diverse tracks are all set to get underway.
But before we cut the ribbon on the opening of the show this fall, All Things Insights wanted to gauge just what makes this “Insight to Impact” conference tick. The answer: The market research community itself, and in particular the speakers set for the event. For that, we want to thank the TMRE 2026 speakers for participating in our speaker series. Ultimately, the speakers, and the attendees of the show, are what make the conference truly special.
Turning AI Tools to Your Competitive Advantage
What are the best (most helpful) ways to incorporate generative AI tools into market research operations? What other opportunities do you see for their use?
“There is so much potential with AI. If you disaggregate the value chain, there is an opportunity to drive value for the firm and the client. Doing things better, faster, and more insightful is what AI (if used well) will do in the end. Those who resist embracing the technology will become obsolete.” —Jacob Beniflah, Executive Director, Center for Multicultural Science
“To use AI as a brainstorming partner. It is good for taking a first pass at an idea to get something ‘on paper’ and then the researcher can take back over where critical thinking is essential.” —Emily Neville, Director, CX Research, Rocket
“GenAI gives you back your thinking time. It doesn’t REPLACE thinking by any means. But it absorbs what you know, how you think, and can help make it make sense when you’re stuck. It’s super helpful when I need to consider open-ends, ethnographic notes, social listening outputs, survey building and drives optimization without the human fatigue. It can help tighten the questions you go into research asking. Essentially driving better focus.
The caveat I’d always add to any conversation on this topic: AI is not intelligence. Intelligence is the capacity to ask the right question. The cultural depth, the human judgment, the understanding of WHY before the WHAT still falls on us. We’re still driving the ship and we need to keep doing so. The researchers who’ll shape this field aren’t the ones who use AI the most. They’re the ones who know what to ask it and how to swing it their way.” —Sofia Vazquez-Barrios, Director of Consumer Insights, North America, Fine Fragrance Experience, DSM-Firmenich
“Based on ARF studies, the most helpful uses are: 1. AI-assisted evidence synthesis. Use generative AI to accelerate literature reviews, case-study summaries, conference takeaways and “what we know” briefs—but keep human review in place.
2. Prompt-based consumer journey research. ARF-related work shows that consumers are using AI for shopping advice, and that brands need to study not only whether they appear in AI answers, but how AI explains them. The key operational use is systematic prompt testing across categories, needs, claims, and competitive contexts.
3. AI visibility and brand meaning audits. ARF/MSI work shows that small prompt changes can shift the “story” attached to the same product, including benefits, caveats and comparisons. Market research teams should track AI-generated brand narratives as part of brand health and search intelligence.
4. Bias and parity testing. The ARF/Iris Flex/MSI studies show that gendered language cues can influence AI outputs, including pricing, category breadth and strategic usefulness. This suggests a clear research operations role: audit AI tools for unequal enablement across prompt styles, roles and user groups.
5. Faster measurement and optimization workflows. ARF commerce and shopper materials point to AI agents compressing the insight cycle, helping detect anomalies and generate optimization recommendations, while still requiring human oversight.
Other opportunities: answer engine optimization research, AI shopping-agent measurement, metadata/content audits for AI retrievability, synthetic-control support for incrementality testing, and creative/claims testing across AI-generated recommendation environments. The broader opportunity is not replacing researchers, but making research operations faster, more systematic, and better able to study AI as a new consumer-facing influence channel.” —Tracy Adams, Sr. Director, Research & Insights, The ARF (Advertising Research Foundation)
“I think we often frame generative AI too narrowly as a productivity tool. It certainly helps researchers design studies, synthesize qualitative findings, and accelerate analysis. Those efficiencies matter, but the bigger opportunity is that AI is changing the kinds of evidence market researchers work with.
Through MSI, we’ve been exploring how the field is moving from direct observation toward interpreting modeled, inferred, and synthetic signals. Synthetic data and AI-generated respondents are becoming part of the research ecosystem. I don’t see them as replacements for primary research, but as complementary sources of evidence that require rigorous validation and clear standards for when they can and cannot be trusted.
I also think AI raises the value of human judgment. As analysis becomes increasingly automated, researchers create the most value by framing the right questions, interpreting ambiguous evidence, and helping organizations make defensible decisions under uncertainty. That’s where I think the profession is headed, and where MSI can help by building the evidence base for how AI can improve marketing research while maintaining rigor and trust.” —Keith Smith, Managing Director, Marketing Science Institute
“The biggest opportunity is not to use generative AI as a faster version of the old research process, but to redesign how decisions get made. The operating model, and using each tool for the right purpose, matters. In our Now/Wow/How approach:
NOW: Act fast with AI. AI search helps teams understand what the world knows, while knowledge bases make an organization’s existing research instantly accessible. AI personas can explore how a customer type might think, while Digital Twins go further: they model real individuals, grounded in lived experience and cultural context. Brought together in Twin Communities, they allow teams to test ideas, explore scenarios, challenge assumptions and generate early direction for quick, lower-risk decisions.
WOW: Discover what others miss. When a decision is strategic, culturally sensitive or high-risk, AI simulation alone is not enough. We return to real people and culture through fresh, AI-enabled qualitative and quantitative research, direct consumer engagement, co-creation, immersive experiences, and cultural exploration. This uncovers deeper motivations, emerging needs, cultural tensions, and unmet opportunities. It goes beyond simply validating what is already known. The goal is to generate a “Wow” insight: a distinctive human or cultural truth that reveals new white space for growth and gives the organization a stronger point of view.
HOW: Turn understanding into distinctive action. Insight only creates value when it changes what a brand does. We combine human expertise, creative judgment, AI-enabled ideation and visualization in iterative, outcome-focused sprints. Teams translate opportunities into strategies, concepts, experiences and communications, then use Twin Communities to pressure-test, refine and optimize them at speed. This closes the gap between insight and execution, helping brands move from an interesting finding to something concrete they can do, say, or create.” —Niels Neudecker, Managing Director, North America, Human8
“I’m not sure there is a ‘best’ here. At least not yet. Every operation is different, and needs fluctuate. That said, here are a couple of thoughts. The key here is to improve efficiency and use AI to augment critical thinking by researchers, not to replace them.
Use AI to speed up proposal drafts, survey outlines, discussion guides, recruiting screeners, coding frameworks, topline summaries, and first-level drafts.
Use AI to help organize open ends, interview transcripts, call notes, social/listening data, reviews, support tickets, and other unstructured inputs. Again, first-pass/first level.
Use AI to help with deliverables. Chatting with the data is here and will be the norm in short order, in my opinion.”—Bart Borkosky, CRO, OvationMR
“There are some really helpful ways to incorporate generative AI into your market research operations. The first is ‘Synthetic Persona Friction Engine.’ Instead of asking AI to summarize a stack of focus group transcripts, feed it your raw proprietary data, ICP attributes, and behavioral constraints to construct dynamic, interactive customer personas. Then, run a ‘Counter-Narrative Stress Test.’ Force the AI personas to ruthlessly poke holes in your new product positioning or messaging frameworks from a place of extreme skepticism. This approach uncovers hidden psychological friction points and messaging gaps before you spend a dime on live market testing. It transforms qualitative analysis from a lagging, passive review into an active, predictive simulator.
The second is ‘Predictive Trend Mapping via Cross-Silo Synthesis.’ Right now, market research, customer success tickets, and product usage data live in completely isolated enterprise silos. The true unlock is giving an enterprise-grade LLM secure access across these disparate data lakes to perform continuous, multi-dimensional pattern matching. AI can detect faint, early-stage churn signals or emerging product feature demands by connecting a sudden shift in customer support sentiment directly to macroeconomic trend data or subtle drop-offs in specific workflow usage. By synthesizing these disconnected data streams in real time, market research evolves from a quarterly, reactive reporting function into a proactive, high-velocity engine that predicts market shifts and drives immediate product roadmap adjustments.” —Patrick Ward, Director, Institutional Marketing, Vanguard
TMRE Session Spotlight: Turning AI into Real Impact for Marketing and Insights
TMRE 2026 will be held in Denver on October 5-7, and the AI in Action Summit will take place October 7. This special event offers deeper insights and networking opportunities in the realm of AI for insights professionals.
The keynote, “Turning AI into Real Impact for Marketing and Insights,” will be presented by Shuchi Sarkar, Chief Marketing Officer, AAA Club Alliance. AI has moved beyond experimentation—but many marketing and insights teams are still struggling to translate its promise into measurable business impact. This session cuts through the hype to focus on what actually works. We’ll explore practical, real-world applications of AI across the marketing and insights lifecycle—from audience understanding and segmentation to campaign optimization, personalization, and decision intelligence. Through concrete examples and use cases, you’ll see how leading teams are embedding AI into everyday workflows to drive efficiency, improve outcomes, and unlock new sources of value.
Attendees will walk away with a clear framework for prioritizing AI initiatives, identifying high-impact use cases, and scaling adoption within their organizations—without needing massive transformation efforts.
Click here for more TMRE 2026 registration information and the show agenda.
Video: “What Is an AI Anyway? | Mustafa Suleyman,” courtesy of TED.
