Remove the friction, not the Human: A perspective of the role of AI in Qualitative Research in South Africa

It is not new news that AI is rapidly changing qualitative research. From transcription and synthesis to analysis, hypothesis generation, AI-led conversations and increasingly AI pack and innovation work. But as the industry races to adopt new tools, the more useful question may not be “Where can we use AI?” but “Where does AI genuinely make the research better and where do we still need proximity to real human experience?”

Drawing on nearly 30 years of qualitative research across Africa and other international markets, Samantha brings a practical, non-European perspective to this question. Africa provides a particularly powerful lens through which to examine AI-enabled research. Multilingual, enormous differences in lived context. Informal and formal economies existing alongside one another. Culturally nuanced communication and consumer realities, that are not always well represented in the data on which technology relies. AI can help us find patterns, interrogate more information, and remove enormous amounts of friction from the research process. But a convincing representation of human experience is not necessarily evidence of human experience.

Rather than arguing for either traditional research or AI, the session introduces a practical “bothism” approach. Deciding deliberately, where technology should accelerate, augment or scale research and where fresh human discovery remains essential. Through real methodological examples from WhatsApp diaries and mobile ethnography, to unconventional fieldwork environments and AI-assisted analysis, the session demonstrates why methodology should follow the question, the consumer and the context, rather than the technology.

The session will leave researchers with a simple way of thinking about AI in their own work.

Key Takeaways:

• Thinking about how to identify the parts of qualitative research where AI can remove friction and increase analytical capacity.
• How to distinguish AI-generated or modelled understanding from fresh human discovery.
• How to decide when scale is useful and when depth, intimacy and cultural context matter more.
• How to use AI as an analytical partner without outsourcing researcher judgement.
• How to protect proximity to real consumers while still embracing the speed and possibilities of AI-enabled research.

The central message is simple: the future of qualitative research is neither human nor AI. It is knowing how to combine the strengths of both, essentially removing friction, without removing humanity.