AI and the future of multicultural market segmentation

By Mario X. Carrasco

Are AI tools inclusive? 

We’re halfway through 2025 and one thing is undeniable: AI is no longer on the horizon, it is in the room. For the market research industry, this has come faster than most expected. What felt like an existential threat just a year ago is now transforming how researchers approach everything from segmentation to recruitment to data analysis.

But as AI becomes embedded in our workflows, a critical question arises. Are the datasets powering these models truly inclusive? Do they reflect the diverse populations researchers aim to understand, or are they building the next generation of tools on top of the same old blind spots?

Why traditional datasets pose risks 

Market research has long struggled with inclusivity. Reaching Spanish-dominant Latinos, Gen Z respondents and even male participants has always been difficult. Despite decades of effort, many of these groups continue to be underrepresented in online panels and large-scale studies.

Now, imagine deploying AI on top of these incomplete datasets. Instead of closing representation gaps, AI trained on biased data risks amplifying them at scale. Biases that were once isolated can now be baked into algorithms and amplified across the entire research ecosystem, undermining the potential of AI to drive more inclusive insights.

AI’s pivot from threat to tool

When AI began gaining traction in the industry, initial skepticism emerged among some researchers, particularly regarding the use of synthetic data and AI-powered moderators. These tools seemed impersonal, disconnected from the human insights that drive understanding and trust among respondents.

Yet, over time, AI has proven itself capable of complementing, rather than replacing, researchers’ work. Instead of diluting what makes insights meaningful, AI can expand them by enabling researchers to finally address representation issues that more conventional methods have never been able to. This shift has prompted a more intentional approach to innovation. If synthetic data is going to shape the future of insights, it must be inclusive by design, representing the full diversity of the populations it aims to model.

How market research drives ethical AI

The market research industry is uniquely positioned to lead in this space. While many tech companies face lawsuits for training AI on copyrighted or illegally scraped data, researchers have operated under strict privacy laws like GDPR and CCPA for decades. Upholding consent, data stewardship and adherence to ethical standards has been the norm.

Our datasets are not only large, but they are also permission-based and carefully vetted. This makes them ideal for training AI models that need to mirror real-world diversity.

But it is not enough to have access to data. The same rigor applied when building representative samples must be applied to training AI models. This means proactively identifying gaps, asking who is missing from the data and taking measurable steps to responsibly include them.

Rethinking multicultural market segmentation

This brings us to the future of multicultural segmentation. Relying solely on broad demographic categories or historical internal datasets is no longer sufficient. Today’s consumers are multidimensional, and AI gives us the tools to see them more clearly.

To generate synthetic data that accurately reflects multicultural audiences, it is essential to incorporate information from historically underrepresented communities. This requires collaboration between technologists and cultural experts, as well as a commitment to designing systems that accurately reflect the reality of diverse identities.

For researchers generating synthetic datasets, combining privacy-compliant methods with culturally rich data points, powered by AI, helps ensure that communities often left out of the conversation are fully represented moving forward.

The road ahead

AI is not a passing trend. It is here to stay, and it is reshaping how we segment audiences, recruit respondents and activate insights. However, AI’s success depends on the quality and inclusiveness of the data behind it, and the researchers guiding its application.

For market research professionals, this is a challenge worth embracing. With deep expertise, ethical frameworks and a foundation in representative sampling, the industry is uniquely positioned to ensure that AI serves all communities, not just the most accessible ones.

The future of multicultural segmentation will belong to those who successfully integrate innovation and intention because the question is no longer whether to adopt AI, but how to use it in a way that advances representation.

Those investing in synthetic data and inclusive segmentation strategies play a crucial role in achieving this, and those seeking better representation in data must continue to demand it.