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The potential of artificial intelligence in qualitative research is transformative, promising faster insights, scalability and innovative new approaches to data collection, analysis and reporting. As the pace of AI transformation accelerates, research platforms are making significant strides in improving language and analytics support for different regions. However, when it comes to application in Asian markets, the road to realizing the potential of artificial intelligence is complex.
On FUEL Asiawe recognized an industry-wide need for rigorous, experience-based validation of AI tools to assess their true potential for non-Western language and cultural contexts. This case study is part of that journey where we test AI-driven qualitative research methods using real-world data in the Thai language, measuring the strengths, limitations and overall effectiveness of each approach.
As a sister article to Democratizing research innovation: How AI is reshaping qualitative researchthis section explores how different research technologies perform when confronted with the nuances of Asian languages. It provides a practical, science-backed look at three main approaches: traditional human-only analysis, commercially available qualitative research platforms, and a DIY AI approach that uses generic AI tools not specifically designed for market research. Our goal is to shed light on how these methods stack up and highlight unique considerations and potential solutions for using AI tools to conduct qualitative research in Asia.
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This case study explores three approaches to qualitative analysis on a dataset of 10 online in-depth interviews conducted in Thailand. Each method offers different advantages and challenges, allowing us to test the limits of their regional adaptability and effectiveness.
Our researcher watched the interviews live and took extensive notes, allowing the researcher to capture subtle cues and contextual nuances in real time. Using a mixture of thematic and narrative analysis, the researcher systematically identified recurring themes while also weaving in the narrative flow of each interview. This approach set the bar for quality, providing a rich, detailed understanding of the data and serving as a valuable benchmark for comparing AI-driven methods.
Commercial qualitative research platforms are designed specifically for simplified, high-quality analysis. These tools are already delivering on promises of simplicity and scalability, helping us generate insights faster than ever. While some challenges remain in supporting Asian languages, we view these platforms as critical and believe they will continue to improve as demand in the region grows.
Given the current pace of change, all AI tools are now ‘work in progress’ and current commercial offerings may work better in some contexts than others. This is sure to change quickly with ongoing development, fueled by increasing demand from our region. Once the language issues are resolved, it will offer an affordable and user-friendly option, especially for researchers with limited technical resources or time or willingness to experiment.
The flexibility of the DIY AI approach was clear from beginning. With the three main tools we used, we created workflows that were highly tailored to the needs of our project, including the desire to maintain a consistent brand identity and style. These workflows can be easily adapted to different projects or client needs. The customization potential here was unparalleled, allowing us to experiment with different approaches to qualitative analysis to quickly and iteratively create something we were proud to put our brand behind.
While this flexibility offers an advantage, it comes with an initial time investment. Unlike specialized platforms, setting up DIY AI requires initial adjustments to workflows and queries, along with trial and error to get the best results. However, this flexibility can be a huge advantage, allowing us to quickly tailor analysis and reporting to meet the unique needs of each project. Apart from the time cost, this approach is inexpensive – a subscription to Chat GPT and about $15 in transcription costs.
Our results show that, when designed well, DIY AI can match – and sometimes even outperform – commercial platforms. With carefully crafted instructions and outputs compliant with FUEL Asiastandards, we have maintained strong control over the quality of analysis and reporting. This adaptability is key to creating tailored, impactful and powerful insights.
When evaluating our findings across all three approaches, several key findings emerged, highlighting both challenges and opportunities in implementing AI-driven qualitative research for Asian markets.
As our case study demonstrates, AI is a powerful enabler for qualitative research, with the potential to improve analysis speed, consistency and adaptability. However, from our initial small trial, it appears that much needs to be done to bridge the development gap in Asian markets, where language and budget issues present different challenges. To advance the conversation and explore new solutions, FUEL Asia teamed up with Insight platforms to co-host the upcoming Asia Digital Insights Summit in January, a landmark event for the research community in Asia, bringing together leaders in research and technology discuss the future of technology-driven insights in Asia.
The summit will feature a dynamic agenda, including keynote sessions, expert panels and live demonstrations of breakthrough research technologies – addressing both quantitative and qualitative use cases.
This case study illustrates the development of artificial intelligence in qualitative research, where both customized DIY approaches and specialized platforms have a role to play. FUEL Asia remains committed to advancing this dialogue, and we invite you to join us at Asia Digital Insights Summit. Together, we can drive research innovation that strengthens the industry, creating solutions that meet the diverse and growing needs of Asian markets.