October 28, 2026
The Market Research Event (TMRE) AI in Action Summit in Colorado convened data and analytics professionals to share insights on how companies can use AI to build smarter data insights while keeping human intelligence at the center. Across sessions on agentic research, synthetic data and automated delivery chains, the prevailing message was clear: While the technology now handles the middle of the work, final decisions must be made by humans.
Four key themes stood out, each with direct implications for healthcare companies:
1. The research operating model is flipping:
Across the industry, the middle of the research delivery chain is being automated — largely on the vendor and tooling side. Survey programming, data processing and open-end coding, once the labor-intensive “back office” of research, are increasingly handled by platforms and AI agents. Yet, framing the business problem still requires human judgment, and fielding still requires real people who can provide real answers. The dividing lines between AI-led and human-led work keep shifting, but design and interpretation remain firmly on the human side.
At the same time, every AI-generated output now requires an independent check. The emerging standard is a verification table built into the infrastructure — survey programming tested against the specifications, fielding measured against quota targets, response validation compared with human decisions on the same responses and open-end coding checked against a human-coded sample.
Verification is no longer a step in the process. It is the process. Google’s Suraj Rajdev made the stakes personal, noting that with reports going to executive forums and customers, “Our names are on the line,” which is why validation is, in his words, one of the most important things.
Takeaways for healthcare:
- Ask market research partners, agencies and vendors the traceability questions. Can every number in a report or data table be traced to a question, a wave and a base? What checks the AI’s output and is that check independent of the AI? In a regulated industry, “The AI said so” is not a methodology section.
- Draw your own fence posts. Map which tasks in the workflow are AI-led versus human-led. Then revisit the line regularly. Keep human oversight non-negotiable in problem framing, interpretation and quality assurance.
2. AI audience proxies are only as good as the research beneath them.
Digital twins, synthetic panels, AI-modeled audiences — AI-mediated proxies are becoming a standard layer in the research stack: fast, scalable, always available. But a proxy is only as good as what it replaces. These approaches work best when they are grounded in continuous, real-world research that is curated, tagged and refreshed. They lose effectiveness when that grounding goes stale.
If there is one rule for using AI-mediated audiences, it is this: governance first, automation second. That means anchoring models in real research, scoring use cases for reliability and continuously validating against human panels. Because synthetic audiences are only as dependable as the data behind them, maintaining a fresh stream of primary research is essential.
Takeaways for healthcare:
- Treat continuous research as infrastructure. AI audience proxies need a standing feed of real data, not a one-time training set. It is critical to fund the research engine that keeps them honest.
- Validate on a schedule, not on faith. Ask when the underlying data was last refreshed and checked against human responses, especially in patient and HCP work, where the stakes demand it.
3. The metric is shifting from speed-to-insight to decision velocity.
Summit chair Kendra Speed, drawing on two decades of leading research at organizations like LinkedIn, framed how the industry has evolved. She noted that it started with speed-to-insight (how quickly can we learn?), then moved to insight-to-action (does the learning get used?). Now, she says the industry has moved to decision velocity — shifting from ambiguity to evidence, judgment and action. In other words, the bottleneck is no longer generating learning — it is creating organizational movement.
AI accelerates the first half of that equation, but not the second. As one speaker put it, today’s AI acts as a tireless junior analyst: fast, literal and fluent — including when it is wrong. In this environment, the scarce skill is no longer choosing between speed and rigor; it is having the judgement to know what the decision requires, when a question demands full rigor and when a directional read is good enough.
Takeaways for healthcare:
- Name the decision behind every readout. Frame each insight around the specific business decision it should inform and identify who owns that decision. If the decision cannot be named, the insight is not ready.
- Measure AI by understanding, not pace. The relevant question is not how much faster the team works, but how much better it knows the customer.
4. Trend reports don’t change things. Workflows do.
Ask any model for the top trends and the result is a perfectly good, perfectly generic answer — and it is also the same answer your competitors received. The real competitive advantage, therefore, is not uncovering the trends, but understanding what they mean for a specific brand and audience.
Getting that distinction demands a disciplined, staged workflow where teams gather, verify, prioritize, contextualize, expand options and stress-test. In this framework, AI does the heavy lifting, and a human makes every consequential choice: setting scope, checking evidence, writing the brief and deciding what goes to primary research. Yogesh Chavda of the University of South Carolina extended this challenge to vendors, saying: “Start thinking about new solutions where AI is the architect, not just the orchestrator.”
Takeaways for healthcare:
- Retire the one-prompt trend reports. Select a single shift that matters to the brand and run it through the full workflow. One worked example may outperform a forty-slide landscape.
- Build always-on sensing. Replace the static trends deck with continuous signal capture and a short, scored list of bets reviewed on a cadence, not a calendar.
The consensus at the conference was that the teams making real progress share a common blueprint: AI does the legwork, humans make the critical choices, real people provide the truth, and a business decision anchors it all. As AI continues to reshape the research landscape, the competitive edge will come not from the most automated workflow, but from pairing capable tools with human judgment so that reliable, verified data builds trust and moves decisions.
Kasia Elliott-Maksymowicz, Account Director, Research and Analytics at GCI Health, specializes in market research, data analysis and strategic consulting. Leveraging a blend of qualitative and quantitative research and employing AI-driven approaches for data analysis and enrichment, she helps guide clients from complex data to actionable insights to drive business growth.