Ever since its inception, qualitative research has been about listening between the lines - decoding the unspoken surfacing and understanding the emotion and adding nuance and context. Over the past couple of decades, the explosion in data, the under-investment in training on core skills, and the use of technology in qualitative data collection, have had the unintended consequence of the practice moving away from making meaning of the spoken and the unspoken. Qualitative research faces an existential challenge as tech and AI enable large-scale analysis of first-party data. Does this mean that smarter AI models can replace qualitative research?
The real question is not about what we are losing. It’s about what value qualitative research can add by redefining its role in this era.
Around the world, AI is accelerating innovation, increasing productivity, and reshaping nearly every industry. With 75% of marketers globally excited about GenAI (up from 68% last year, according to Kantar's Media Reactions 2025), the enthusiasm is palpable. But as we rush to adopt these powerful tools, we must move beyond the efficiency narrative to embrace a new story about building value through true partnership — tapping into the unique and combined strengths of human expertise and AI.
Humans bring the ability to read between the lines, uncover unspoken meaning, and empathize with others. This ability relies on interpretation of irony, tone, inhibitions, and especially body language, not easily accessible to AI, and is grounded in the wider cultural setting, which adds important social context. Additionally, when analysing and reporting findings, a human moderator gives their insights meaning by applying ethical judgment and contextual storytelling.
AI brings a new and different set of capabilities, the most obvious being speed and scalability which allows it to process hours of interviews in minutes. However, the benefits of AI go far further than mere speed. AI can detect patterns across massive datasets which humans might miss because of cognitive limitations and cultural biases. It democratises advanced analytics by making statistical analyses accessible to non-technical people. Lastly, AI brings tireless consistency to labour-intensive tasks like transcription, coding, and clustering.
AI can accelerate the mechanical aspects of analysis and humans inject meaning, ethics, and creativity into AI outputs. The human moderator can focus their attention on interpreting the behaviour and attitudes of other humans, while AI frees them from laborious manual review of transcripts and videos to identify consistent patterns or specific verbatims. However, to realise these benefits may require moderators to develop new skills. The quality of AI output is highly dependent on the quality of prompts used. Despite excitement over AI's potential benefits, 33% of global marketers in Kantar's Media Reactions 2025 agreed that their team lacked the right skills to use GenAI effectively. So, to make the most of what AI has to offer, qualitative experts will need to educate themselves on how best to use AI if they are to remain effective in this new era.
Bias identification
AI's thematic clustering challenges researcher biases, surfacing patterns we might overlook. Given the right prompts, generative AI can work collaboratively to validate hypotheses, refine ideas, and define consumer segments with fresh perspectives that check our innate assumptions, adding value that is beyond simple efficiency.
Frames of reference
AI-driven simulations expand conceptual frames, helping us anticipate emerging behaviours. Working with AI offers the opportunity to go beyond the scope of individual discussions to pull in additional contextual data, search social media conversations for related themes, and identify relevant case studies that broaden our understanding.
Enhanced storytelling
GenAI models are brilliant teachers of how to write and can be an exceptional partner when a good qualitative researcher finds the right stories to tell. They help us structure narratives, find compelling verbatims, and present insights in ways that resonate with diverse audiences.
This understanding has driven the development of KAiA for Qual, Kantar's AI assistant, which enhances the quality, speed, and efficiency of our qualitative research. KAiA for Qual helps analysts set up projects, upload and manage files, and summarize findings across multiple languages and transcript formats. More importantly, analysts can interact with a Large Language Model that has access to Kantar's Innovation, Brand Strategy, and Brand Equity frameworks, acting as an always-on collaborator for brainstorming, analysis, insight generation, and adding depth to conclusions. But the real magic in this partnership comes from three ways in which the analyst and the model work together:
1. The analyst using KAiA for Qual to find interesting hypotheses to explore in the data. The model has inbuilt prompts to inspire the analyst to look at unconventional hypotheses. For example – what is missing or under-represented in consumer narratives about skincare rituals?
2. The analyst brings their embodied memory of their first-hand interaction with the respondents as a contextual layer to the model’s reading of the data. An example of this is the addition of non-verbal responses to the verbal reactions to a concept. Together, this helps go beyond what respondents may not have (or may have poorly) articulated to give a more nuanced view of how a concept landed.
3. The model can be used to inspire starting points to build a narrative and tell a story.
As Tara Prabhakar, Kantar Global Head of Qualitative, notes: "By integrating cutting-edge GenAI with Kantar's proprietary IP in Qualitative research, this platform empowers our teams to analyse vast volumes of qualitative data in hours rather than days. The result is not just faster delivery—it’s about creating narratives that enhance emotional predisposition, shining a light on frictions or missed opportunities in the experience journey, and identifying surprising new spaces that a brand can stretch into. This is a new era for Qualitative research at Kantar; one where we train our people to bring back their core skills, broaden their analysis frames, and elevate their storytelling abilities".
What if the next leap in creativity is sparked by a machine? What if the next breakthrough in empathy comes from an algorithm? What if AI learns to identify underlying motivations? These aren't rhetorical questions—they're invitations to reimagine our practice of qualitative research. As AI models are trained to develop more human skills, qualitative researchers should train more too. We need to accentuate our core skills – active listening, cultural grounding, and seeing the whole person, going beyond what is said, shown, or posted.
The challenge—and opportunity—for qualitative leaders is clear: don't just adopt AI. Embrace it. Learn from it. Expand through it. And, most importantly, go back to the core skills and use and flaunt them. Let clients see the transformative value that we create by combining deep qual skills and smart AI models. When both moderator/analyst and AI play to their strengths, the outcome is more meaningful consumer insights that can create authentic and sustainable brand growth.
Discover how Kantar’s quintessentially human qualitative research helps you grow your brand with meaning, difference, and authenticity. Visit today: Qualitative Research Design & Consumer Insights | Kantar
The real question is not about what we are losing. It’s about what value qualitative research can add by redefining its role in this era.
Around the world, AI is accelerating innovation, increasing productivity, and reshaping nearly every industry. With 75% of marketers globally excited about GenAI (up from 68% last year, according to Kantar's Media Reactions 2025), the enthusiasm is palpable. But as we rush to adopt these powerful tools, we must move beyond the efficiency narrative to embrace a new story about building value through true partnership — tapping into the unique and combined strengths of human expertise and AI.
The power of complementarity
The future of qualitative research isn't a zero-sum game between human and machine. Instead, it's about understanding and leveraging what each does best.Humans bring the ability to read between the lines, uncover unspoken meaning, and empathize with others. This ability relies on interpretation of irony, tone, inhibitions, and especially body language, not easily accessible to AI, and is grounded in the wider cultural setting, which adds important social context. Additionally, when analysing and reporting findings, a human moderator gives their insights meaning by applying ethical judgment and contextual storytelling.
AI brings a new and different set of capabilities, the most obvious being speed and scalability which allows it to process hours of interviews in minutes. However, the benefits of AI go far further than mere speed. AI can detect patterns across massive datasets which humans might miss because of cognitive limitations and cultural biases. It democratises advanced analytics by making statistical analyses accessible to non-technical people. Lastly, AI brings tireless consistency to labour-intensive tasks like transcription, coding, and clustering.
AI can accelerate the mechanical aspects of analysis and humans inject meaning, ethics, and creativity into AI outputs. The human moderator can focus their attention on interpreting the behaviour and attitudes of other humans, while AI frees them from laborious manual review of transcripts and videos to identify consistent patterns or specific verbatims. However, to realise these benefits may require moderators to develop new skills. The quality of AI output is highly dependent on the quality of prompts used. Despite excitement over AI's potential benefits, 33% of global marketers in Kantar's Media Reactions 2025 agreed that their team lacked the right skills to use GenAI effectively. So, to make the most of what AI has to offer, qualitative experts will need to educate themselves on how best to use AI if they are to remain effective in this new era.
How Kantar Qual optimises AI-human collaboration
At Kantar, we've been working to answer a fundamental question: How can we elevate the deep human understanding that our core qualitative skills bring with the help of AI? We believe the answer lies in three critical areas:Bias identification
AI's thematic clustering challenges researcher biases, surfacing patterns we might overlook. Given the right prompts, generative AI can work collaboratively to validate hypotheses, refine ideas, and define consumer segments with fresh perspectives that check our innate assumptions, adding value that is beyond simple efficiency.
Frames of reference
AI-driven simulations expand conceptual frames, helping us anticipate emerging behaviours. Working with AI offers the opportunity to go beyond the scope of individual discussions to pull in additional contextual data, search social media conversations for related themes, and identify relevant case studies that broaden our understanding.
Enhanced storytelling
GenAI models are brilliant teachers of how to write and can be an exceptional partner when a good qualitative researcher finds the right stories to tell. They help us structure narratives, find compelling verbatims, and present insights in ways that resonate with diverse audiences.
This understanding has driven the development of KAiA for Qual, Kantar's AI assistant, which enhances the quality, speed, and efficiency of our qualitative research. KAiA for Qual helps analysts set up projects, upload and manage files, and summarize findings across multiple languages and transcript formats. More importantly, analysts can interact with a Large Language Model that has access to Kantar's Innovation, Brand Strategy, and Brand Equity frameworks, acting as an always-on collaborator for brainstorming, analysis, insight generation, and adding depth to conclusions. But the real magic in this partnership comes from three ways in which the analyst and the model work together:
1. The analyst using KAiA for Qual to find interesting hypotheses to explore in the data. The model has inbuilt prompts to inspire the analyst to look at unconventional hypotheses. For example – what is missing or under-represented in consumer narratives about skincare rituals?
2. The analyst brings their embodied memory of their first-hand interaction with the respondents as a contextual layer to the model’s reading of the data. An example of this is the addition of non-verbal responses to the verbal reactions to a concept. Together, this helps go beyond what respondents may not have (or may have poorly) articulated to give a more nuanced view of how a concept landed.
3. The model can be used to inspire starting points to build a narrative and tell a story.
As Tara Prabhakar, Kantar Global Head of Qualitative, notes: "By integrating cutting-edge GenAI with Kantar's proprietary IP in Qualitative research, this platform empowers our teams to analyse vast volumes of qualitative data in hours rather than days. The result is not just faster delivery—it’s about creating narratives that enhance emotional predisposition, shining a light on frictions or missed opportunities in the experience journey, and identifying surprising new spaces that a brand can stretch into. This is a new era for Qualitative research at Kantar; one where we train our people to bring back their core skills, broaden their analysis frames, and elevate their storytelling abilities".
Beyond efficiency to true partnership
The future of qualitative research isn't about choosing between human and machine. It's about designing a partnership where AI provokes, accelerates, and scales while humans interpret, contextualize, and inspire. In embracing this partnership, we're not losing the essence of qualitative research. We're evolving it for a new era where machines help us become more human, not less.What if the next leap in creativity is sparked by a machine? What if the next breakthrough in empathy comes from an algorithm? What if AI learns to identify underlying motivations? These aren't rhetorical questions—they're invitations to reimagine our practice of qualitative research. As AI models are trained to develop more human skills, qualitative researchers should train more too. We need to accentuate our core skills – active listening, cultural grounding, and seeing the whole person, going beyond what is said, shown, or posted.
The challenge—and opportunity—for qualitative leaders is clear: don't just adopt AI. Embrace it. Learn from it. Expand through it. And, most importantly, go back to the core skills and use and flaunt them. Let clients see the transformative value that we create by combining deep qual skills and smart AI models. When both moderator/analyst and AI play to their strengths, the outcome is more meaningful consumer insights that can create authentic and sustainable brand growth.
Discover how Kantar’s quintessentially human qualitative research helps you grow your brand with meaning, difference, and authenticity. Visit today: Qualitative Research Design & Consumer Insights | Kantar
