MSI Forum: When AI Disrupts — Rethinking Data, Insights, and Modeling – AGENDA
February 11 – February 12 @ 8:00 am – 5:00 pm PST • Los Angeles | In-Person (Feb. 12 will be from 8:00am – 12:00pm)
February 11
8:00 – 9:00am
Breakfast & Registration
9:00 – 9:15am
Opening Remarks
Tulin Erdem – NYU Stern School of Business; MSI Executive Director
9:15 – 9:45am
AI in Disguise
Generative content creation has significant potential to transform advertising. This research examines how generative AI–enabled visual ad creation affects real-world advertising effectiveness, drawing on more than 16 billion ad impressions and 116 million clicks from a leading online ad platform. A quasi-experimental design compares 4,633 “sibling ads”—AI-generated and human-made visuals launched by the same advertisers under identical campaign conditions. AI-generated images deliver higher clickthrough rates than human-made images, but only when they do not appear AI-generated. Visual features shaping perceptions of AI generation are identified: while clearer images and larger faces are associated with human-made ads, aesthetics and intense color saturation tend to signal AI use. These findings inform both advertising platforms offering AI-powered creative tools and advertisers adopting them.
Oded Netzer – Vice Dean of Research and the Arthur J. Samberg Professor of Business, Columbia Business School
9:45 – 10:15am
Misfits and the Machine: Why AI Alone Can’t Crack Creative Effectiveness
AI promises faster, cheaper creative—but effectiveness tells a more complex story. Through real-world case studies, we will discuss when AI enhances advertising performance, when it undermines it, and why human creativity & empathy still matters.
Megan Franklin – Vice President – Creative Excellence, Ipsos
10:15 – 10:45am
When Consumers Use AI: How Generative Tools Reshape Complaints, Resolution, and Fairness
Generative AI tools such as ChatGPT are rapidly changing how consumers communicate with firms, especially in high-stakes situations such as complaints, appeals, and service disputes. This session presents large-scale evidence from over one million consumer finance complaints, complemented by controlled experiments, to examine how AI-assisted communication shapes firm responses and resolution outcomes. Practical implications for firms will be shared, including: (1) How AI-assisted consumer communication is changing complaint quality, tone, and resolution rates; (2) Whether firms may be responding to presentation quality rather than substantive merit; (3) Implications for customer experience design, complaint handling, and frontline decision-making; and (4) Risks and opportunities for fairness, consistency, and regulatory compliance as AI-mediated communication becomes widespread. The discussion will center on how firms can adapt processes and governance to operate effectively and fairly in an environment where both consumers and organizations increasingly rely on AI to communicate.
Jiwoong Shin – Professor of Marketing, Yale University; Senior Editor, Marketing Science
10:45 – 11:10am
Morning Break
11:10 – 11:40am
When AI Becomes the VoC Whisperer: A New Model for Customer Insights
As AI shifts how consumers search, create, and communicate, the signals marketers rely on are also shifting faster than what traditional measurement tools can keep up with. This session introduces a new analytic model that leverages the best of what we know to generate high-quality customer insights but uses AI to surface early, actionable shifts 90% earlier. Learn how machine generated relationships and human prowess work together to reveal emerging competitors, evolving associations, and narrative momentum long before they appear in surveys. The discussion will highlight what this means for marketers’ analytical toolkits, the skills required to work alongside AI driven insight systems, and where human judgment remains essential in interpreting consumer voice.
Samantha Moore – Director of Market Research, Microsoft
11:40am – 12:10pm
Consumer Responses to AI Usage
As artificial intelligence moves from a back-end tool to a front-facing consumer partner, companies must increasingly pay attention to consumers’ receptivity to AI use and its disclosure. This session will discuss the impact of AI use disclosure on consumer engagement with content and ethical perceptions. Learn how consumer characteristics can systematically predict consumers’ AI receptivity. In particular, understand recent research exploring a counterintuitive link between consumers’ AI literacy and their receptivity to AI use.
Stephanie Tully – Assistant Professor, USC Marshall School of Business
12:10 – 12:40pm
AI, Consumers, and Trust – Adobe‘s Data-Based Perspective
Using data from billions commercial web journeys, billions more creative and generative AI events, and more than 1 trillion transactions, Adobe has a uniquely broad, deep, and timely perspective on the consumer experience of AI. Taylor Schreiner, head of Adobe Digital Insights, will tell the story of this unprecedentedly rapid revolution in both the use of and trust in AI. Together we will explore the future implications of the story the data are telling.
Taylor Schreiner – Senior Director and Head of Adobe Digital Insights
12:40 – 12:50pm
Donald Lehmann Tribute: A Legacy of Rigor, Relevance, and Service
This tribute honors Donald Lehmann’s lasting contributions to marketing through his influential scholarship, dedicated mentorship, and deep commitment to the Marketing Science Institute, including his service as Executive Director. Don championed rigorous research with real-world relevance and played a central role in strengthening the bridge between academic insight and managerial practice. In this brief reflection, we celebrate his impact on the discipline, the MSI community, and the values he instilled that continue to guide marketing science today.
Carl Mela – T. Austin Finch Foundation Professor of Marketing, Duke University
12:50 – 1:50pm
Lunch
1:50 – 2:20pm
AI Search and Generative Engine Optimization
AI search is transforming how customers discover, evaluate, and choose brands—shifting from keyword queries to conversational, intent-driven journeys. As LLMs become the new front door to commerce, brand visibility within these AI answers increasingly determines which brands are considered and which are ignored. In this session, we’ll demystify how AI search works, why some brands surface while others disappear, and what this shift means for marketers. Hear the latest research on how marketing science can help brands optimize their presence in AI-generated recommendations.
Kartik Hosanagar – John C. Hower Professor of Technology and Digital Business and a Professor of Marketing, The Wharton School of the University of Pennsylvania
2:20 – 2:50pm
Leadership Panel: Navigating AI’s Disruption of Marketing
This leadership panel brings together senior marketing and analytics leaders, alongside leading academics, to examine how artificial intelligence is reshaping the foundations of marketing, from data and consumer understanding to search, advertising, and analytics. Anchored in the five core themes of the conference, the discussion will integrate real-world organizational experience with rigorous research perspectives. Panelists will explore where assumptions are being challenged, what strategic decisions marketers face today, and how collaboration between industry and academia can help guide marketing practice in an AI-driven future.
Vassilis Bakopoulos – SVP and Head of Industry Research, Marketing and Media Alliance
Ron Friedman – Vice President, Mattel Future Lab
Dr. Zainab Jamal – Director of Data Sciences & Analytics at Hewlett Packard Enterprise
Helen Wolf, Ph.D. – Senior Director, Global Insights, Colgate-Palmolive Company
Moderator: Scott McDonald, Ph.D. – President & CEO, ARF
2:50 – 3:20pm
The Impact of LLM Adoption on Online User Behavior
This session examines how people change their online behavior after they start using large language models (LLMs) like ChatGPT instead of traditional search engines. It shows that once users adopt LLMs, they conduct fewer searches on regular search engines and visit fewer smaller websites, which can reduce ad exposure and traffic that many information providers rely on. The effect is uneven: big sites see little change, but niche educational sites and some Q&A platforms lose visits. In particular, display advertising exposure drops for frequent online shoppers, suggesting important revenue impacts for advertisers and publishers.
Tai Lam – Assistant Professor of Marketing, UCLA Anderson School of Business
3:20 – 3:40pm
Afternoon Break
3:40 – 4:10pm
Generative AI, Platform Stances, and Content Creator Behavior
Generative Artificial Intelligence (AI) technologies have emerged as a transformative force in the content creator economy. This paper examines how platform stances toward Generative AI shape creator behavior on output-centric visual arts platforms. Two independent natural experiments are conducted on leading Chinese platforms: Lofter’s launch of an AI image generator (signaling a pro-AI stance) and Graffiti Kingdom’s prohibition of AI-generated artwork (signaling an anti-AI stance). The analysis shows that creators reduced their activity on Lofter following the AI generator launch, while activity increased, at least in the short term, on Graffiti Kingdom after the AI prohibition. Heterogeneity analysis reveals that higher-productivity, higher-popularity, multi-homing, and AI-averse creators show larger reductions in activity on Lofter. Through analysis of creator posts, three primary concerns are identified as driving resistance: replacement risk, perceived low quality, and copyright infringement.
Runshan Fu – Assistant Professor of Marketing at NYU’s Stern School of Business
4:10 – 4:40pm
Improving GenAI Content by Teaching AI the Why
Generative AI is increasingly used to produce marketing content, but off-the-shelf models are often misaligned with business goals. Standard fine-tuning approaches rely on experiments that identify what content correlates with higher engagement. Optimizing only on these outcomes, however, risks overfitting, reward hacking, and poor generalization—often leading models to drift toward clickbait. Learn about a new framework that enables large language models to learn why content works. The model automatically generates and validates hypotheses about underlying mechanisms—such as emotional tone or framing—and combines this knowledge with observed performance when generating new content. Using data from over 23,000 A/B-tested news headlines, we show that this approach outperforms standard methods in improving engagement while remaining more robust and less prone to clickbait, because it is grounded in generalizable principles rather than surface correlations.
K. Sudhir– James Frank Professor of Marketing, Private Enterprise and Management, Yale School of Management
4:40 – 4:55pm
AGC Dissertation Award: Winner and Honorable Mentions
The AGC Dissertation Award recognizes rigorous, original doctoral research with the potential to influence marketing scholarship and practice. In this session, we will announce this year’s winner and recognize honorable mention recipients whose work exemplifies methodological excellence and relevance to important marketing problems. Attendees are invited to engage further with these scholars and explore the practical and academic implications of their research by visiting their posters at the conference reception.
4:55 – 5:00pm
Closing Remarks
Scott McDonald, Ph.D. – President & CEO, ARF
5:00 – 6:30pm
Cocktail Reception
February 12
8:00 – 9:00am
Breakfast & Registration
9:00 – 9:10am
Opening Remarks
Tulin Erdem – NYU Stern School of Business; MSI Executive Director
9:10 – 9:40am
Toward the Next MSI Research Priorities
This session offers a preview of the research directions MSI anticipates shaping our next set of Research Priorities. Drawing on guiding questions surfaced through recent virtual discussions with our member organizations, and informed by emerging shifts in AI, data, and marketing practice, we will share early themes, open questions, and areas of opportunity likely to define the next research cycle. Rather than presenting a finalized agenda, the session is intended to invite reflection, dialogue, and input as MSI refines a forward-looking research roadmap for marketing science.
Keith Smith – Managing Director of the Marketing Science Institute
9:40 – 10:10am
From Measurement to Intelligence: How AI is Reshaping Marketing Science Work
This talk will explore how AI is fundamentally reshaping marketing science work, from metrics measurement and marketing research to experimentation and attribution frameworks. It will focus on how AI enhances traditional marketing analytics approaches, addresses challenges, and redefines the roles of marketing analysts. We will discuss the implications and highlight the shift in business needs from reporting outcomes to continuously improving decisions.
Richard Qiao – Director of Marketing Science, State Farm
10:10 – 10:30am
Morning Break
10:30 – 11:00am
Generative AI for the Voice-of-the-Customer
Identifying customer needs is the cornerstone of innovation, yet for over 30 years market research has relied on exhaustive manual interpretation by professional analysts. While traditional tools can assist this task, the final formulation of “jobs to be done” always required human judgment—until now. In this session, Prof. Artem Timoshenko will discuss how Generative AI is revolutionizing the Voice-of-the-Customer by sharing results from a blind study where supervised fine-tuned LLMs —even “small” ones — extracted customer needs as effectively as human experts to provide precise, hallucination-free insights for product management and marketing strategies.
Artem Timoshenko – Associate Professor of Marketing, Northwestern University Kellogg School of Management
11:00 – 11:30am
Attention to the Journey: Transformer Models for Customer Lifetime Value Prediction in the Presence of Marketing Interventions
Learn about a transformer-based attention model for customer lifetime value prediction that incorporates marketing touchpoints in the customer journey, including emails, promotions, and engagement signals. Benchmarked against RFM-based stochastic models and HMMs, the model then leverages attention weights to investigate whether strategies that drive short-term conversion undermine long-term value. Current CLV models treat customer behavior as independent of marketing actions, missing predictive signal in journey sequences. This approach achieves higher recall in identifying high-value customers, with implications for acquisition efficiency and customer equity accuracy. Attention interpretability reveals that the link between promotional acquisition and lifetime value is heterogeneous, offering actionable paths to identify high-potential customers.
Grant West, Ph.D. – Senior Director, in4mation insights
Zipei Lu – Ph.D. Candidate, University of Maryland Robert H. Smith School of Business
11:30am – 12:00pm
Generative AI and Firm Productivity: Field Experiments in Online Retail
This research presents large-scale causal evidence on the productivity effects of Generative AI (GenAI) in consumer-facing business workflows. Using randomized field experiments conducted on a major cross-border online retail platform, the study evaluates GenAI deployments across customer service, search, advertising, and seller support. Holding prices, labor, and capital constant, the analysis measures revenue-based productivity using sales and conversion outcomes. The results show that most GenAI applications generate meaningful productivity gains, driven primarily by improved consumer experience and reduced marketplace frictions rather than cost savings. Effects are heterogeneous, with larger benefits for smaller sellers and less experienced consumers. The findings offer actionable insights for firms on where GenAI delivers short-term value and contribute to broader discussions on how AI reshapes productivity, competition, and value creation in digital markets.
Dante Donati – Assistant Professor of Business, Columbia Business School
12:00 – 12:10pm
Closing Remarks
Scott McDonald, Ph.D. – President & CEO, ARF