The GenAI Future of Consumer Research
Ming-Hui Huang,Roland T Rust
In their 2025 article, The GenAI Future of Consumer Research, Huang and Rust explore how generative artificial intelligence (GenAI) is expected to transform consumer behavior and the field of consumer research. GenAI refers to AI systems that can generate original content—such as text, images, or videos—based on user input. Tools like ChatGPT, DALL·E, and other advanced models are becoming increasingly integrated into consumers’ everyday lives, changing the way people create, communicate, and make decisions.
The authors propose that the development and impact of GenAI will unfold in three main stages: democratization, the average trap, and model collapse. These stages offer a roadmap for understanding both the benefits and potential risks GenAI poses for consumers and researchers.
1.Democratization
In the first stage, democratization, GenAI makes powerful tools accessible to the general public. This shift allows ordinary consumers to generate content that previously required expertise—such as personalized product designs, creative marketing messages, or even original stories and images. GenAI lowers barriers to participation and expands the creative potential of consumers.
From a research perspective, this stage brings exciting opportunities. Researchers can use GenAI to analyze large volumes of consumer data, simulate responses, and even assist in theory development. More importantly, GenAI changes how consumers behave and express themselves, which opens new avenues for studying identity, creativity, and decision-making in the digital age.
2.The Average Trap
Despite its early promise, GenAI faces significant limitations. In the second stage, known as the average trap, GenAI-generated content starts to feel repetitive, generic, and uninspired. This happens because GenAI models are trained to predict the most likely next word or image based on existing data. As a result, they tend to produce content that reflects statistical averages, not human originality.
For consumers, this can lead to homogenized experiences, recommendations, designs, or messages that all feel the same. For marketers, it becomes harder to stand out or create emotional impact. And for researchers, studying consumer behavior may become more difficult as GenAI-generated responses lack depth or individual nuance.
3.Model Collapse
The third stage, model collapse, is a possible future scenario where GenAI systems begin training on their own outputs rather than on real human-generated data. As these models become increasingly self-referential, their quality declines. They begin to lose touch with human preferences, or amplify existing biases.
This presents serious risks for consumer research. If AI-generated data dominate the digital landscape, researchers may end up studying machine-generated patterns rather than real consumer behavior. Insights become less reliable, and models trained on flawed data could produce misleading results.
Implications for Consumer Research
Huang and Rust argue that consumer researchers need to engage with GenAI not just as a tool but as a subject of study. GenAI is changing the way consumers think, create, and interact with brands. It’s also changing the information environment that shapes preferences and decisions.
The authors emphasize that consumer research must evolve to keep pace. This includes developing new research methods to separate human-generated from AI-generated content, reevaluating how we collect and interpret data, and rethinking the theories that explain consumer behavior in this new landscape.
While the paper acknowledges GenAI’s challenges, it also recognizes its potential. With thoughtful design and careful use, GenAI can support creativity, expand access to knowledge, and help researchers better understand a rapidly changing consumer world.
Read the full paper:
Ming-Hui Huang, Roland T Rust, The GenAI Future of Consumer Research, Journal of Consumer Research, Volume 52, Issue 1, June 2025, Pages 4-17.
