When AI meets cultural intelligence: Navigating human nuance in pharma market research

In an era where AI is reshaping how we collect and analyze data, market research is undergoing a rapid transformation. In the pharmaceutical sector—where complexity, compliance, and emotional stakes run high—AI has the potential to unlock nuanced insights at scale. But as with any tool, its power lies not just in what it reveals, but in how we interpret what it shows us.

This is especially true when culture is in play, as seen in some of the real-world market research scenarios explored below.

Bringing patient voices into focus with AI and cultural intelligence

At Escalent, we practice cultural listening and cultural intelligence as a core part of our approach—tuning into the values, symbols, language, and lived experiences that shape how individuals perceive and respond to the world around them. In pharma, these cultural signals can influence everything from how a person describes their symptoms to their beliefs about treatment options or perceptions of health equity.

AI can amplify our ability to detect these cultural cues in healthcare. When trained and tuned with care, AI systems can surface patterns across languages, metaphors, expressions, and affective responses that might otherwise go unnoticed. After all, as human researchers, we have our own cultural lenses that may, at times, obscure our analytical view. The algorithm can pull out insights that our eyes might otherwise skip over.

Here’s how this looks in practice:

In an exploratory study into patient attitudes towards over-the-counter flu products, a respondent recounts using alcohol baths to reduce fevers. While this is a custom strongly rooted in culture, it is not evident to an analyst because the practice is also common in their own community. However, because the AI platform being used is directed to point out outlier practices, it pulls these references to the surface. The analyst, made aware of the practice as an outlier, is then able to think critically about it and view it through the broader lens of attitudinal variation.

What are the limitations of AI in understanding cultural nuance in pharma market research?

AI doesn’t know what’s culturally meaningful—it knows what’s statistically significant. And therein lies a risk: when AI identifies a rare phrase or unusual metaphor, it may assign it outsized importance, mistaking difference for depth. What’s an outlier in one dataset might be a deeply rooted truth in another. Conversely, something AI deems frequent and “central” might be culturally neutral or even irrelevant to the pharma research objectives.

Without human interpretation grounded in cultural intelligence, AI can distort reality. A single mention of a cultural practice may be algorithmically weighted as a trend. An emotionally charged anecdote might be over-indexed as a driver of behavior. Dialectical differences may read as simplistic or unemotional. And subtle, meaningful cues—like how someone pauses when describing a diagnosis—may be lost entirely.

Here’s how this looks in practice:

A respondent is shown a piece of stimuli showing a family barbeque. She sees it, leans back in her chair, and crosses her arms before saying she think it is “a… fine… picture of a family having fun.” The AI platform reads this as “a fine picture,” or an enthusiastic endorsement. However, the analyst watching can tell immediately that the respondent feels very ambivalent towards the image, and that “fine” actually means “just okay” or “just good enough” in this context.

How to create a culture-first AI strategy in healthcare research

So how do we harness AI’s strengths for generating healthcare insights without losing sight of the cultural nuance that defines great qualitative research?

It starts with intention. Before data even hits the model, we need to ask: are we prepared with the background setup needed to use the platform critically? We need to know where the tool is drawing its information from and how it weights different data sources; and what voices it has been fed as “normal” vs. “colloquial”.

In order for them to support robust analysis, we need to train AI models with a rich understanding of culture. That means feeding them inputs that include regional dialects, community-specific rituals, and experiences of underrepresented groups—not just mainstream, “neutral” narratives. It means assigning subgroup segmentation that gives the program as much intra-group nuance as we have available. And it also means designing analysis protocols that blend machine insights with human interpretation—always second-guessing the tool and maintaining the curiosity needed to uncover the richest insights.

Here’s how this looks in practice:

When a project involves respondents from multiple vernacular groups, consider uploading prep content that gives examples of those different vernaculars. This will help the tool to assign legitimacy to all respondents’ language instead of over-indexing on those respondents who speak in a more formal, “proper” style.

OR

When an AI tool provides outlier examples, re-read the larger section of that transcript and even re-watch the relevant section of the interview to make sure the tool is not misinterpreting or distorting the meaning of the respondents’ words.

Finally, we must cultivate teams that bring their own diverse cultural lenses to the table. At Escalent, we view cultural intelligence not as a “nice to have” but as a critical muscle for any team using AI. Our Cultural Advisory Group is a global, cross-functional network of audience experts whose lived experiences, educational backgrounds, languages and on-the-job insights inform how we engage with, analyze, and represent audiences traditionally left out of the mainstream. We don’t just read what the machine tells us—we ask why, and for whom, that insight matters.

The future of pharma insights: AI-driven market research grounded in ethics and empathy

There’s no question that AI is helping us go farther and faster in pharmaceutical market research. But if we don’t build in guardrails—cultural context, human interpretation, ethical rigor—we risk producing AI-driven research that’s technically impressive but disconnected from real people’s lives.

We believe that cultural listening and AI aren’t at odds. They’re powerful partners—when guided with care. Because in a field where understanding patient perspectives can literally save lives, “good enough” isn’t good enough. We always strive not only to listen, but to hear.

Meet our authors

Rosalie Schurman, Senior Qualitative Analyst, Health & Life Sciences, Escalent
Kisha Payton, Chief Belonging & Inclusion Officer, Escalent Group