We’re back with the third edition of Global WINdow, where we explore the pressing questions shaping the insights industry. In this edition, we gather expert perspectives from WIN members across Peru, Greece, Argentina, China, Italy, Spain, Ecuador, Vietnam, Germany, Sweden, Slovenia and Malaysia.
As artificial intelligence continues to reshape the research industry, one thing is clear: the future of insight depends on the synergy between human interpretation and AI-driven analysis. Around the world, researchers are adapting to new ethical frameworks and client expectations—navigating the complexities of responsible AI use while ensuring that nuance, context, and cultural depth are not lost.
Local frameworks shape global responsibility
From Lima to Milan, the development of responsible AI is increasingly grounded in local regulation. Peru, for example, has recently updated its Personal Data Protection Regulation “While not AI-specific it sets a foundation for responsible AI practices. It mandates transparency in automated decision-making, appointing Data Protection Officers, and rapid breach reporting. These provisions directly impact AI applications in market research, especially where profiling and large-scale data processing are involved. This new framework pushes companies to adopt ethical, privacy-first approaches.” explains Urpi Torrado, CEO at Datum International.
Italy is also seeing recent shifts. “In March the Italian Senate approved a first draft law on Artificial Intelligence,” shares Nikos Kotoulas of Doxa “Italy’s framework mandates compliance with transparency, security, non-discrimination, and sustainability, aligning with the EU AI Act, but adding some nuances. This approach demonstrates how localized ethical frameworks can reinforce global standards while addressing national concerns like democratic integrity and cultural values.”
China’s approach, as Barry Tse, Founder at WisdomAsia, notes, is “increasingly shaped by a comprehensive, state-led ethical governance framework that integrates national strategy, sector-specific regulation, and international cooperation. China has institutionalised ethical AI through policies like the New Generation AI Ethics Guidelines (2021), which articulate six core principles including human well-being, fairness, privacy, and accountability. While enforcement and adaptability to emerging technologies remain ongoing challenges, China’s model is distinctive in fusing legal, technical, and moral regulation under a unified governance vision.”
But it’s not the same for all countries, Janja Božič Marolt, Founder at Mediana, Slovenia, is waiting for a more structured framework as “unfortunately we do not have local regulations implemented, yet”. Same goes for Ecuador, as Nancy Cordova, Vice President at CEDATOS, says “There is not a major control on AI practices in our market yet”.
Human context is still king
Despite the rapid development of AI capabilities, researchers across the WIN network are united in their view: human interpretation remains essential.
“Human oversight remains vital, not only at the final interpretive stage, but from the outset, to ensure meaningful pattern recognition and analytical soundness” says Urpi, Peru, “This is especially true for qualitative research, where AI tools can assist in reducing surface-level bias but cannot yet replicate the discernment needed to detect nuance, context, and emotional depth in respondent narratives. Responsible AI use in research requires us to retain a strong degree of human judgement to preserve the authenticity and “soul” of our participants’ voices.”
Constanza Cilley, Executive Director at Voices! Argentina, agrees. “AI is a powerful assistant, but not a replacement for human interpretation. The central role in making sense of these findings still belongs to humans. Technological advancements are enabling us to dedicate more time to what truly adds value: deep understanding, strategic consulting, and bringing context to the data.”
This balance is echoed by Estefanía Clavero, CEO at Instituto DYM in Spain: “The application of artificial intelligence must be carried out in compliance with the fundamental ethical principles of market research, as well as the current legal framework—particularly regarding intellectual property and data protection. Human oversight is essential to ensure that results are interpreted correctly within the project’s context, ensuring the accuracy, relevance, and applicability of the insights. This combination of automated analysis and expert judgment enables more informed and responsible decision-making.”
AI Attitudes Vary, Transparency Doesn’t
Some members are experiencing growing curiosity amongst end-clients when it comes to AI: “In our experience, clients rarely express explicit concerns,” says Tse, China. “Many are intrigued, or even reassured, when they learn that AI has been deployed to enhance analytical rigour and efficiency. We are transparent in attributing those gains to our integration of AI tools; this builds trust and helps clients appreciate the value-add of responsible AI adoption.”
Titos Simitzis, General Manager at Alternative Research Solutions in Greece, agrees: “using AI to check the quality of raw data is quite reassuring to our clients”; and so does Karin Nelsson, CEO at Demoskop in Sweden, “Clients do not really express concerns, they’re curious about what we can do and how insight can be enhanced. We are very transparent with how we work and have a very clear AI Policy in place.”
This lack of concern is a worry for Wai Yu See Toh, CEO at Central Force International in Malaysia, “Unfortunately clients aren’t showing as much concern as they should, they lack proper understanding of data sources and how AI models are being trained. There needs to be more education centred around where data sources are coming from and how is AI accessing these data points. Without identifying these sources, it is impossible to determine the accuracy and reliability of AI.”
But not everyone is experiencing concern-free clients; Xavier Depouilly, General Manager at Indochina Research (Vietnam), notes that “Some clients are prohibiting the use of AI or requesting detailed information on how AI is applied and what level of human supervision or guidance is involved. It is important to be clear. We include disclaimers in our reports and results to explain when and how AI has been used. Transparency is key to ensuring we can harness the benefits of AI while maintaining the quality and integrity of our detailed and critical research work.”
“Some of our clients express worries about AI delivering biased findings or replacing human judgment,” says Cilley, Argentina. “We make it clear to our clients that while we are increasingly incorporating AI-driven methodologies, we validate these findings through more traditional, proven approaches and always add a layer of human interpretation. We find that this transparent dialogue helps build trust.”
What role can WIN play?
As AI becomes more embedded in research, there’s a clear opportunity—and responsibility—for the WIN network to lead the way.
Kotoulas, Italy, says that he’s looking for WIN to “Share best practices, fuelling the discovery of smart applications, and building collective knowledge to help local researchers navigate the global AI ecosystem with confidence.”
Simitzis, Greece, suggests to “use the global knowledge and experience of WIN to create a rule book, a set of do’s and don’ts which can be used in our work, and communicated to our clients.”
See Toh agrees on wanting “to educate clients and the public about data sources, data credibility, and data verification. This is crucial in understanding when we should trust AI-driven research insights or when we should be hyper-critical of it.”
A shared future
Artificial intelligence is here to stay. But so is human insight. As local laws catch up to innovation, and as clients seek reassurance as much as results, the research industry must keep both minds and machines in the loop.
With global networks like WIN—committed to transparency, collaboration, and ethics—we can build an ethical and efficient path forward.