GenAI’s commodification of consumer research: How algorithmic standardization erodes behavioral authenticity and reinforces corporate power

GenAI’s training data is overwhelmingly sourced from Western, urban, and digitally literate populations, excluding indigenous knowledge systems that prioritize relational and ecological contexts over individualistic consumer behavior. Traditional methods like participatory observation or storytelling-based research are systematically devalued in favor of scalable, quantifiable outputs. The erasure of these perspectives reinforces colonial patterns of knowledge extraction, where marginalized communities’ lived experiences are reduced to training data without compensation or recognition.

The current push toward AI-driven consumer research mirrors historical shifts in market research, such as the rise of psychological profiling in the early 20th century or the standardization of Nielsen ratings in the 1950s, which prioritized mass-market appeal over nuanced human behavior. Each wave of 'scientific' market research has been accompanied by critiques of bias and reductionism, yet corporate interests have consistently overridden these concerns in the name of efficiency. The commodification of consumer data today follows the same logic as the enclosure of common lands in the Industrial Revolution, where communal knowledge is privatized and repackaged as proprietary insight.

Cross-culturally, consumer behavior is shaped by vastly different epistemologies—from the gift economies of Melanesia to the time-bound rituals of Hindu *darshan* in India—yet GenAI’s models flatten these into a single 'consumer' archetype. In East Asia, for instance, purchasing decisions are often influenced by collective social validation (e.g., *bazaar* culture in Pakistan or *mottainai* in Japan), which are poorly represented in datasets dominated by Western individualism. The lack of cultural diversity in training data not only biases outputs but also reinforces a neocolonial gaze where 'global' consumer trends are defined by Silicon Valley’s worldview.

Scientifically, GenAI’s outputs are only as valid as their training data, which is rife with selection bias, historical inaccuracies, and cultural blind spots. Studies show that LLMs trained on internet text reproduce stereotypes and amplify majority-group perspectives while marginalizing minority voices, a phenomenon well-documented in NLP research. The assumption that 'more data equals better results' ignores the fact that GenAI’s training corpora are curated by corporate entities with vested interests in maintaining the status quo, not by independent researchers seeking truth.

Artistically and spiritually, consumer research has long been critiqued for reducing human experience to transactional metrics, a trend that GenAI exacerbates by quantifying even creativity and emotion. The commodification of 'consumer insights' risks turning human behavior into a series of predictable, monetizable patterns, erasing the sacred and the ineffable in favor of algorithmic predictability. Indigenous and spiritual traditions often frame consumption as a sacred act tied to community and ecology, a perspective that clashes with GenAI’s utilitarian approach to 'user behavior.'

Future modelling suggests that GenAI’s dominance in consumer research could lead to a homogenization of global markets, where brands cater to a narrow set of 'optimized' consumer profiles while ignoring diverse needs and desires. This could exacerbate social fragmentation, as corporations double down on predictive algorithms that reinforce existing inequalities rather than addressing them. Scenario planning must consider the long-term effects of a research ecosystem where human creativity and dissent are systematically filtered out in favor of 'safe,' generic outputs.

Marginalized voices—including low-income communities, racial minorities, and non-Western populations—are systematically excluded from GenAI’s training data and thus from the 'consumer insights' it generates. The focus on 'average' or 'mainstream' behavior erases the lived experiences of those who do not fit corporate archetypes, reinforcing structural exclusion in both research and market access. Even when these groups are included in datasets, their perspectives are often tokenized or misrepresented, further entrenching power imbalances in how consumer behavior is understood and acted upon.