Panel Discussion: AI and the Future of Marketing Research 
What happens when AI becomes not just a tool for analyzing marketing research but a participant in it? As organizations experiment with synthetic respondents and AI-generated consumers, longstanding assumptions about research design and validation are being challenged. This panel brings together industry leaders and academic researchers to explore where AI is delivering meaningful value today, where expectations may be outpacing reality, and how marketing research is likely to evolve in the coming years. 
Raymond Burke – Professor of Marketing, Kelley School of Business, Indiana University 
Leabe Commisso – Senior Vice President of Strategic Growth, Ipsos 
Ankit Dhawan – Founder & CEO, BluePill AI  
Stefano Puntoni – Professor of Marketing, The Wharton School 
Moderator: Wendy Moe – Dean’s Professor of Marketing, Robert H. Smith School of Business, University of Maryland 

Collaborative Intelligence: Reconstructing the Invisible Consumer From Fragmented Data 
Firms routinely use large-scale consumer surveys to support segmentation, targeting, and product strategy, but these surveys are often modular by design and fragmented in use, making it difficult to represent and interpret some consumers. The framework supports accurate prediction, outperforming off-the-shelf GPT and linear multi-task baselines, while recovering interpretable heterogeneity that fragmentation can leave unseen. 
Alice Li – Associate Professor, Fisher School of Business, The Ohio State University

From Reviews to Actionable Insights: An LLM-Based Approach for Attribute and Feature Extraction 
Firms have access to vast amounts of customer review data but often lack a scalable way to turn it into actionable decisions. This research introduces an LLM-based framework that distinguishes broad perceptual attributes from specific, managerially actionable product and service features. Applied to 20,000 Starbucks Yelp reviews, the approach yields results that closely align with human coding and strongly predict customer ratings, while processing reviews in seconds rather than minutes. The resulting structured data support dashboards that track sentiment across stores and over time, identify customer “joy points” and “pain points,” and prioritize high-impact improvements. 
Khaled Boughanmi – Assistant Professor of Marketing, Cornell University, Johnson Graduate School of Management 

Closing the Innovation Forecast Gap: An AI/ML Approach to New Product Forecasting and Incrementality at Kenvue 
Forecasting new product demand remains a persistent challenge because of historical sales data are often unavailable, and traditional methods frequently rely on surveys, analogs, or subjective judgment. This presentation introduces an AI/ML-based forecasting framework that unifies demand prediction and incrementality measurement within a decision-intelligence platform. The solution integrates syndicated POS data, historical launches, execution variables, and AI-enriched product attributes to forecast Year 1 and Year 2 performance while estimating cannibalization and net-new growth. 

Beyond improved accuracy, the approach enables scalable concept evaluation, scenario simulation, and execution guardrails, allowing organizations to make faster, more confident innovation decisions while reducing dependence on costly concept-testing studies. 
JJ Huang – Director, Global Data Science, Kenvue 

Who Chops the Onions? The Hidden Ingredients in Every Profitable AI Agent 
AI agents are moving into business processes much faster than firms are learning how to evaluate them. This session traces where agentification stands today and walks through its six stages, from deciding whether agents belong in a workflow at all to measuring what they actually returned. At each stage we pair anecdotes from the field with the best available academic and industry evidence to show where AI initiatives most often succeed, stall, or leak value. The central question we ask is: where in your own process does the next dollar of return come from? 
Dr. Adithya (Adi) Pattabhiramaiah – The Sharon A. and David B. Pearce Associate Professor of Marketing and Faculty Director, Center for AI in Business at the Scheller College of Business, Georgia Institute of Technology