Summary
When research teams are small, the hardest tradeoff is often between depth and scale. Live interviews surface rich, contextual insights, but they’re also time-consuming, resource-heavy, thus often deprioritized when bandwidth is low. In this session, I’ll share how I experimented with AI-moderated interviews to bridge that gap, using technology to recover depth and empathy without requiring live facilitation. Faced with the need to understand our customers’ decision-making (those who purchased our platform, and those who didn’t), I initially relied on surveys. However, I found they lacked the nuance that real conversations reveal. By introducing AI moderation, I created a way for participants to engage in adaptive, conversational interviews that went beyond the limits of static forms. I’ll walk through how I set up these sessions, what prompts worked (and didn’t), and how I analyzed the results. I’ll also share how I’ve used other AI tools like ChatGPT and Perplexity to assist with synthesis and bias-checking, creating a workflow that both expands my analytical reach and strengthens the rigor of my findings. As AI tools continue to enter the researcher’s toolkit, this case study illustrates how we can thoughtfully integrate them to expand—not erode—the human depth of qualitative work. It offers a model for how lean teams can maintain research quality while navigating the realities of limited time, budget, and capacity. This talk will explore the emerging space between automation and augmentation, finding opportunity for depth when time and resources are tight.”
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