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For years, qualitative research was treated as a method to use selectively: valuable, but too slow and costly to scale. AI was expected to push it even further to the margins. Instead, the opposite is happening. As organizations gain access to more data than they can meaningfully interpret, the human “why” has become more valuable—not less.
Jerome Linder, Head of Market, Sensory and Consumer Insights, APAC Taste, Nutrition and Health at Symrise AG, sees AI not as a replacement for qualitative expertise, but as an accelerator. Ahead of QUAL360 APAC 2026, he explains how technology is removing traditional constraints, why human judgment remains essential, and what global research teams must understand about conducting qualitative research across APAC.
Why data abundance is bringing qualitative research back
For years, qual was sidelined for being slow and costly — reserved for select research questions. What changed is not that qual got cheaper first, but that quant data became overwhelming. Numbers are everywhere now, pulled from multiple sources, and most organizations have the information without understanding what is behind it. The “why” went missing, and qualitative inquiry is uniquely equipped to uncover it.
There is also a trust dimension. When data is scraped from many sources at different times, leaders increasingly want validation from real people. Talking to consumers has become a way to confirm what the numbers only suggest — not a legacy method, but a check on a fragmented evidence base whose reliability is increasingly difficult to assess.
How AI changes the economics of qualitative research
The two constraints that held qual back, time and cost, are exactly what the technology removes. Speech-to-text, LLMs, and multilingual capability let teams interview anywhere from 20 to 200 participants in a handful of days, with thematic clustering that even an experienced researcher working under time pressure might take longer to do or miss entirely. For Jerome, whose field is the subconscious world of taste, smell, and experience — territory a quant questionnaire cannot reach — doing this work at scale and low cost is what makes it viable again.
But scale is not a substitute for judgment. Errors and hallucinations creep in, metaphors and cross-language nuance get lost, and that is why the human in the loop stays essential. The models do not write the discussion guides or decide what the findings mean. They handle the volume so researchers can spend their energy on interpretation.
Speed and scale, however, are only the first layer of AI’s impact. The more consequential shift may be how the technology changes the interview itself.
The most interesting shift: Moderators that go off-piste
One emerging capability stands out. A skilled human moderator hears an interesting answer and follows it — “that’s fascinating, tell me more” — instead of rigidly following the discussion guide. That instinct was hard to scale. A well-trained AI moderator can now drill into a promising thread, in ways a rigid 10-minute interview never allowed. The risk is obvious — let it wander too far, and it strays outside the topic entirely — but the opportunity is real: agility and rigor, at scale, which qual has never had at the same time.
Yet more responsive technology does not eliminate the need for cultural expertise. In a region as diverse as APAC, it makes that expertise even more important.
Why one-size-fits-all research fails in APAC
The most common mistake Jerome sees is teams applying one methodology across every region, decided somewhere far from the market. In APAC, that fails quickly. The nuance is behavioral: in rural Indonesia, people are far more vocal talking about a dish or a drink in a group than one-on-one, while urban consumers open up more easily on their own. Food carries particular cultural weight, and the method has to flex to the culture rather than the other way around. Understanding that difference — community versus individual — is the difference between qual that works in the region and qual that only looks like it does.
What Drives Qualitative Evolution Beyond Tools?
These tensions, between speed and rigor, scale and nuance, automation and human judgment, will shape the next phase of qualitative research. They are also at the center of the conversation Jerome Linder will lead at QUAL360 APAC 2026.
In the closing panel “The Future of Qual: What Drives Qualitative Evolution Beyond Tools” — Linder will join Belinda Pang of IFF, Chitkala Nishandar of 3M, and Renu Singh of PepsiCo GCC to examine how research teams can introduce AI responsibly while protecting cultural context, interpretive rigor, and the human judgment that gives qualitative insight its value.
Join the conversation at QUAL360 APAC 2026.
📅 November 3–4, 2026 | Singapore
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