In 2017, FUEL Asia presented a case study at IIEX Asia Pacific that explored the changing dynamics of the client-agency relationship. This shift was driven by the growing access clients had to data and the rise of DIY research tools. Back then, we were in the era of the "democratisation of research"—the ability for anyone to conduct research, which pushed agencies to adapt to this new reality.
Fast forward to today, we’re on the brink of another transformation, this time led by artificial intelligence (AI). This wave isn’t just about simplifying research processes—it’s about enabling innovation within research, allowing any qualitative researcher, from freelancers to boutique agencies, to innovate without needing technical expertise (or coding experience). With cautious optimism, we think that this might lead to a growth in qualitative research.
At FUEL Asia, specialists in qualitative research and cultural insights across Asia, our journey with AI has evolved from scepticism to curiosity, then excitement, and now a stance of cautious optimism. From the outset, we were clear that we didn’t want to engage in a “race to the bottom” but rather focus on how we can use AI to conduct deeper and better qualitative research while also acknowledging clients' desire for speed and efficiency.
We use a wide range of qualitative methods in our work and as qualitative specialists, we know that the deepest level of understanding comes from ethnographic approaches, or at least interacting with and observing people in their natural environment. The reality though is that a lot of qualitative work has shifted online, whether that be groups, IDIs, digital ethnography or online communities. It is in this world of course where AI thrives.
AI’s Role in the Stages of a Qualitative Research Project
AI can be used at every step of a qualitative research project, from the initial design to delivering insights. At FUEL Asia, we’ve been experimenting hands-on with AI tools at each stage, looking for ways to enhance how we deliver value to our clients. Here's how AI is making a difference across the process:
1. Project Design
A well-designed research project begins with a clear articulation of the business problem and an understanding of what’s already known on the topic. AI can assist in this stage by analysing large volumes of existing data, such as past studies, customer feedback, or social media content. This helps to identify knowledge gaps or formulate hypotheses, sharpening the research objectives to ensure the study addresses current and relevant issues. It can also frame the research context in creative and structured ways – for example by doing a Situational Analysis or using a PEST framework.
Moreover, when it comes to qualitative studies that use stimulus, generative AI tools can be used as valuable ‘co-pilots’. For instance, many clients have defined customer personas. Using AI, we can simulate interactions with these personas to co-create stimulus or get feedback from AI-generated personas, crafting language and visuals that resonate with the intended audience. This approach can also be used to localise stimulus for global projects, where ideas have the potential to get ‘lost in translation’.
2. Discussion or Activity Guide Design
Generative AI can support the development of discussion and activity guides by suggesting relevant themes and questions based on the research objectives and target audience. This doesn’t replace human creativity but provides a structured starting point, allowing researchers to focus on fine-tuning the guide to the specific cultural and contextual nuances of the target audience. Conversely, we’ve also found AI tools helpful in making suggestions to improve the final guide. Whether using at the front or back end (or both), it can help save time and write better guides.
3. Moderation
There are many established platforms already offering chatbots to moderate interviews; we are interested to see how this develops. We think that these types of studies may replace some quantitative research, by offering greater depth of insight and being more participant centric (especially as the chatbot experience improves). AI moderation is especially promising in online communities, where AI can probe participants and respond to tasks, reducing the time required from human moderators.
4. Transcription & Translation
For our international clients conducting qualitative research in Asia, translation and transcription are often required. While live translation still benefits from the human touch, especially for capturing tone and nuance during sessions, AI can streamline the transcription process when speed is of the essence. We currently offer clients:
AI-only transcription (when speed is the priority)
AI + human review (balancing speed and accuracy)
Human-only transcription (for high-quality, precise outcomes)
Choosing the right platform for each Asian language is crucial, as some tools perform better than others based on linguistic complexity. For languages with complex tonal variations, such as Mandarin, Thai or Vietnamese, we usually recommend human reviewers are always involved to capture both tone and meaning accurately. Additionally, AI-powered transcription and translation can be invaluable in iterative research designs, allowing transcripts to be quickly summarised and analysed, enabling researchers to pivot or adapt their approach mid-fieldwork.
5. Analysis
We tested AI tools alongside our traditional methods to see how they compare, finding that AI can offer fresh perspectives that complement our in-depth analysis. While there are challenges with some AI platforms handling Asian languages, through trial and error, we’ve found solutions that work well.
One of our inspirations came from a webinar by Ray Poynter, which explored how AI can help you do better qualitative research, by running different types of qualitative analysis. As experienced qualitative researchers, we bring to our projects our experience, ways of thinking and analysis frameworks. While this is inherently a strength and what clients pay for, it is useful to have a LLM run the analysis to generate different perspectives. Of course, AI models also have bias, but offering different perspectives can certainly sharpen your analysis and make connections you might not have seen.
We took this idea further and analysed data using established frameworks like Maslow’s Hierarchy or Jobs-to-be-Done. We even experimented by adopting the style of well-known qualitative researchers or academics from the social and behavioural sciences, generating results in line with their thinking and methodologies.
A word of caution: as qualitative researchers our job is to look for meaning, based on not just what’s said (and what’s not said), but how it’s said (including body language) and in what context. Currently human intelligence is far superior to AI in this regard and why AI should be your co-pilot and not ‘leading the expedition’.
6. Reporting & Insight Dissemination
AI is changing the game when it comes to reporting. Imagine turning your research findings into a dynamic video presentation, complete with AI-generated visuals or even an avatar to walk your client through the results in multiple languages. Tools like Midjourney and Gamma.app can take visualisation and reporting to the next level - ensuring that insights aren't just presented but brought to life in ways that resonate across your organisation. This technology is already here and accessible to anyone willing to invest a little time and experiment.
One of the best uses for Generative AI we’ve found is to build interactive personas, where stakeholders can engage with dynamic models that support decision-making, ensuring that research has impact throughout an organisation (and at all levels). Moreover, AI can help cross-reference findings with existing studies, quickly summarise related literature, and suggest areas for further research, giving researchers an opportunity to add more value to their clients.
A word of caution: the best qualitative researchers tend to be great storytellers – and while you can learn storytelling, every great storyteller has their own unique voice. AI tools excel at editing and can mimic the style of your favourite writers, and it’s very easy to produce impressive content – but it just doesn’t sound like you, and you lose authenticity. It’s wise to train AI on your own content and any brand guidelines you have and always make sure that the final edit is your own. Our good friend Dave McCaughan emphasises this point here.
Challenges of AI—Bias, Data Privacy & Transparency
While AI opens exciting possibilities, it also comes with its challenges, particularly around bias. AI models are often trained on predominantly Western, English-language datasets, which can distort insights when applied to Asia’s diverse cultural and linguistic contexts. This is why we combine AI-driven analysis with insights from local experts, ensuring that cultural and linguistic nuances are always respected.
Transparency is another key concern. AI systems, especially those using deep learning, are often seen as “black boxes” where the decision-making process isn’t clear (ChatGPT’s 4o model now actually shows its ‘thinking’ process, which we find useful). Using prompts to ask for sources or evidence is also important to ensure the AI model is not hallucinating. It’s recommended to let clients know when using AI in their projects. This is vital not only for compliance—particularly as some clients may prohibit the use of AI with their data - but also for maintaining trust and integrity in the partnership. It's equally important to understand how AI tools manage data storage and whether the data is used for training purposes. Data compliance concerns can typically be managed through the appropriate settings within the AI tools.
Unlocking Creativity and Insight: AI’s Role in Research Innovation
The purpose of this article is to showcase how AI can be applied to different stages of qualitative research and how any qualitative researcher can use AI to innovate. It's not just about automating tasks—it’s about freeing up more time to dig deeper and deliver insights that truly inspire action. And while AI is our powerful co-pilot, it's our human touch and cultural understanding that remain at the heart of what we do.
We’ve focused here primarily on how AI is in many aspects a leveller for research innovation. We haven’t explored the myriad other ways that AI can help SMEs ‘punch above their weight’. That’s perhaps a blog for another day.
At FUEL Asia, we’re in the early stages of what we hope will be a fun journey. Our experience so far is that experimenting with AI is time well spent. Yes, we might go down a few rabbit holes, but we’ll also learn and implement things that will help us create value for our clients. We welcome your thoughts on this evolving topic. Have you experimented with AI in your qualitative research, or are you considering it? Share your comments and feedback below—we’d love to hear from you – and we’d be happy to respond to any questions you have.
And if you’re looking for a trusted partner to navigate the complexities of qualitative research in Asia, get in touch with us at FUEL Asia. We’ll also be at ESOMAR APAC in Bangkok from 6th to 8th November, and we’d love to see you there.
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