"The bottleneck has moved," Chui Chui told host Jason Giles. "It used to be synthesizing. But now I think the bottleneck has moved to the interpretations."

That observation reframes the conversation around AI. Rather than replacing researchers, AI is elevating the importance of interpretation, context, and cultural understanding. Those qualities have always mattered in cross-cultural user research. Today, they matter even more.

Why cultural blind spots matter more in AI-powered customer insights

The promise of AI has always been scale. Feed an AI model hundreds of interviews, survey responses, or support conversations, and it will identify recurring themes in a fraction of the time a human team could.

That's an extraordinary capability.

It's also where cultural blind spots begin to emerge.

Large language models are exceptionally good at recognizing linguistic similarities. If hundreds of customers mention trust, convenience, value, or affordability, AI naturally groups those comments together into coherent themes. From a technical perspective, that's exactly what it's supposed to do.

But people don't experience products through language alone. They experience them through culture, social norms, local economies, regulations, and expectations that vary dramatically from one market to another. Two customers may use the same word while describing entirely different frustrations.

That distinction is easy to overlook in AI-powered customer insights, particularly when beautifully written summaries create the impression that the analysis is complete.

Why cross-cultural user research reveals what AI misses

One example from the conversation illustrates this perfectly.

Imagine conducting global user research across six countries. After analyzing hundreds of interviews, AI reports that trust is the biggest barrier preventing customers from adopting your product.

That sounds like an actionable insight.

Except it isn't.

"In one market, trust might mean I've never heard about this brand," Chui Chui explained. "In another, it means I'm not confident putting my credit card details into your app. In another country, it might be privacy or scams."

Those are not variations of the same problem. They are entirely different business challenges that require different responses, Chui Chui said. One organization may need stronger brand marketing. Another may need to redesign its payment experience. A third may need to address privacy concerns more directly.

The language is identical.

The customer motivation is not.

This is precisely why cross-cultural user research remains essential, even as AI becomes more capable. AI identifies the pattern. Researchers uncover the reason behind it.

Customer research is about meaning, not just customer feedback

Perhaps the most memorable example from the interview comes from Chui Chui's work conducting customer research for Spotify across multiple international markets.

Again and again, participants offered the same explanation for not subscribing.

"It's too expensive."

If an AI model were tasked with summarizing those interviews, price would almost certainly emerge as the dominant finding. Most organizations would immediately begin debating discounts or promotional offers.

Chui Chui resisted that temptation.

"The real question we need to ask ourselves is expensive compared to what?"

That single question uncovered entirely different motivations across countries. In Colombia, participants happily spent significantly more money attending concerts because live experiences created lasting memories. In South Africa, mobile data represented a far higher priority because it enabled communication and daily business. In other markets, recurring subscriptions simply didn't fit local income patterns.

The complaint never changed.

Its meaning did.

This is where the difference between customer feedback and customer insights becomes obvious. Customers are often excellent at describing what they experience, but uncovering why they experience it still requires curiosity, context, and thoughtful interpretation.

Why global user research goes beyond translation

Many organizations assume advances in AI translation have largely solved international research.

Translation, however, is only the beginning.

Jason shared how moving to the United Kingdom taught him that seemingly positive comments often carried meanings he initially missed. Chui Chui responded with a story from her own family. After her mother prepared a meal, a guest repeatedly complimented the food. To Chui Chui, who has lived in Britain for years, those comments sounded like polite appreciation. Her sister immediately recognized something different: the guest was politely asking for another serving.

"Translation is actually the easiest part," Chui Chui said. "The harder question is, what did the customer really mean?"

That distinction sits at the heart of global user research. AI can translate words with remarkable accuracy. Understanding cultural intent is considerably more difficult.

AI in UX research makes human expertise more valuable

The conversation challenges one of the most common assumptions about AI: that better automation naturally reduces the need for researchers.

Jason observed exactly the opposite.

Experienced researchers notice hesitation before an answer. They recognize shifts in tone. They follow unexpected comments that don't fit the discussion guide because experience tells them something important is hiding beneath the surface.

Those moments rarely appear in transcripts.

They almost never emerge from automated summaries.

Rather than replacing researchers, AI in UX research is changing where they create value. Less time is spent organizing interviews and generating summaries. More time can be devoted to validating assumptions, interpreting cultural context, and connecting insights across markets.

Ironically, AI may be making uniquely human skills more valuable, not less.

Better AI-powered customer insights begin with better questions

One practical takeaway from the episode is Chui Chui's Global Fit Loop, a simple framework for improving AI-powered customer insights before they become business decisions.

Rather than jumping directly from an AI-generated theme to an action plan, she encourages teams to ask three questions:

• What is the signal?
• What is driving that signal in this market?
• What business decision should change because of it?

That middle question is the one organizations often skip.

It is also the question most likely to uncover cultural blind spots before they become expensive product decisions.

The future of customer insights belongs to organizations that understand culture

The race to adopt AI has largely been framed around speed.

Speed certainly matters.

But organizations that consistently outperform competitors won't simply be the ones generating customer insights faster. They'll be the ones that recognize where AI reaches its limits and where human judgment becomes indispensable.

The companies that combine AI-powered customer insights with deep cross-cultural user research will avoid a mistake that becomes increasingly tempting as AI improves: assuming that similar language always reflects similar meaning.

As Chui Chui put it, "I would much rather you spend another ten minutes to challenge the interpretations than spending six months to build something that is wrong."

Related UserTesting resources

• Chui Chui Tan on LinkedIn 
• Beyō Global website
• Chui Chui's book Research for Global Growth
• Chui Chui Tan on YouTube
• Jason Giles on LinkedIn
• Research in the age of AI: How to stay relevant, build trust, and prove your worth. This on-demand webinar explores how AI is reshaping UX research while reinforcing the importance of researcher expertise, critical thinking, and trust. It complements Chui Chui Tan's message that AI can accelerate research, but human interpretation is essential for avoiding cultural blind spots and making better business decisions.
• Research as Organizational Intelligence: A playbook for research leaders in the AI era. This guide examines how research leaders can evolve from service providers to strategic advisors as AI changes the research landscape. It aligns closely with the episode's discussion about shifting from synthesizing data to interpreting it, while ensuring human judgment and organizational context remain at the center of customer insights.
• Ep. 201: Why AI in user research isn't replacing real people (yet). Mario Callegaro discusses how AI and synthetic users are transforming UX research, where AI adds value, where it falls short, and why human judgment remains indispensable. It's an excellent companion episode for listeners interested in the evolving role of researchers in an AI-powered world.
• The UX researchers who don't adapt to AI won't be replaced by it—they'll just be left out. This article explores how the role of UX researchers is evolving alongside AI, arguing that the greatest opportunity lies in becoming strategic interpreters of customer behavior rather than simply producing research outputs. It reinforces one of Chui Chui's central themes: AI can generate summaries, but people provide the context and meaning behind customer insights.

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