Why cultural relevance still depends on human judgement
By Iris Griffiths, ACROSS Research (and ICG member)
AI can produce a questionnaire that is perfectly translated into Portuguese and still asks the wrong questions. When research moves between Asian markets and Brazil, the challenge is not simply moving between languages, but between cultural systems.
Lately, more of my work has involved an Asian–Latin American crossover. I find this as exciting as when I moved to the UK decades ago and began conducting qualitative research in Latin America from a British and European perspective. I am again learning to see Latin American markets and behaviour; now, through the perspectives of clients and colleagues in Asia.
It feels like a professional renaissance—a renewed opportunity to learn and share as artificial intelligence transforms both our industry and our individual roles.
AI has transformed market research. Translation, transcription, summarisation, discussion-guide drafting and initial analysis are increasingly automated. The efficiencies are valuable. Yet language—already an important and often overlooked dimension of international research—can become a greater weakness when AI output is accepted without sufficient linguistic and cultural scrutiny.
Fluent-looking research materials can hide rather than solve a problem. A sentence may be grammatically correct yet feel unnatural to a Brazilian participant; a response scale may be clear but culturally awkward; and words such as “premium”, “natural” or “good value” may carry associations quite different from those intended in the original brief.
Language is not a production task to complete after the research design. Translation and interpreting shape how questions are understood, participants express themselves and international teams interpret findings. Without linguistic, research and cultural scrutiny, automation can strip away tone, context, purpose and meaning.
A growing share of my work is for Asian end-clients, often in partnership with researchers across Asia. In recent projects, I have increasingly received research materials translated or drafted with AI, sometimes without clear evidence of human linguistic or cultural review. This is not an issue confined to Asia, but it is through this work that the risks have become especially visible to me.
This has sharpened my interest in how insight travels between Asia and Latin America. China, Japan and South Korea are distinct markets in which I would like to deepen relationships and develop more work. Projects moving between these countries and Brazil must work naturally in both cultural contexts.
The task requires double localisation: research materials must make sense to Brazilian participants, and Brazilian findings must make sense to decision-makers in the brand’s home market.
With this in mind, I suggest three principles for using AI more intelligently.
1. Examine the assumptions hidden inside the brief
Research briefs are never culturally neutral. They carry assumptions about who decides, what convenience means, why people trust a product and whether a category solves the same problem as in the home market.
Before fieldwork, the local researcher should turn those assumptions into questions. Is the intended user also the buyer? Does the proposed benefit have emotional relevance? Would consumers genuinely encounter the product in the situation shown? A visible assumption can be tested; an invisible one can quietly distort the whole project.
2. Translate meanings, not merely words
Brazilian Portuguese—or any other research language—is not simply a delivery mechanism. Vocabulary, rhythm, register and tone affect how participants understand both the question and the person asking it. Literal translation—or overreliance on AI-translated materials—can make a moderator sound remote, excessively formal or unlike someone a participant would normally speak to.
This is where linguistic and research expertise must meet. AI output should be reviewed not only for accuracy but for research function: Will the wording prompt the intended thought? Does the scale distinguish responses naturally? Are the examples recognisable? Translation is complete only when the material helps achieve the research objectives.
3. Design for human rapport—not just culturally fluent wording
AI can help draft discussion guides and suggest culturally appropriate wording. But material that sounds relevant on the page may not feel natural in the research room. Cultural relevance is not something AI can certify: it is revealed through tone, timing, humour, hesitation and what remains unsaid.
Brazilian conversations often benefit from warmth and connection. Enthusiasm may reflect interest, but also friendliness or reluctance to disappoint. Skilled moderators read these nuances and probe beyond the first “yes” through concrete occasions, trade-offs and recent behaviour. Human conversation may be less polished than AI-generated language, but its imperfections carry meaning.
The human work between the words
AI will continue to improve, and market researchers should use it intelligently. But speed and fluency are not the same as understanding. The more effortless automation becomes, the more important it is to examine the cultural assumptions, research purpose and meaning behind the words.
Bringing qualitative research together with language and intercultural expertise helps international research move beyond technically correct translation towards genuine insight. It is the difference between hearing Portuguese answers and understanding Brazilian consumers.
I am keen to connect with colleagues and clients interested in the evolving links between Asia and Latin America. If this crossover is part of your work, I would be delighted to continue the conversation and explore what we might learn together.
Over several decades, I have built two distinctive and complementary professional paths: one as a qualitative market researcher and the other as a conference interpreter, translator and intercultural trainer. Together, they give me a distinctive perspective on what international research can lose between languages, cultures and markets—and on how to preserve the meaning that turns responses into genuine insight.