Turning Voices into Vision: How AI Helped Make Sense of Human Stories

Last week, I found myself back where it all began, deep in qualitative data.

A national not-for-profit had shared a mountain of open-text survey responses: pages of heartfelt reflections about their future direction. The kind of material that’s too rich to skim, but too heavy to process manually.

Here’s the twist: I studied sociology twenty years ago, but I’m no expert researcher. So before diving in, I asked ChatGPT to help me think like one.

I prompted it to explain how a social scientist might approach this kind of qualitative analysis; how to code, cluster, and interpret data ethically and meaningfully. The feedback I got felt like sitting beside a patient academic mentor, reminding me of concepts I hadn’t touched since uni.

That conversation shaped the next prompt, one designed to help code, organise, and summarise hundreds of individual comments into usable insights. The AI handled the structure; I brought the context, interpretation, and care.

What came out was a clear, human-centred overview; patterns of alignment, areas for renewal, and a shared sense of direction. It turned hundreds of voices into something leaders could actually see and work with.

What excites me most is how AI can act like an always-available mentor; giving us access to expert ways of thinking, helping us reconnect with our own skills, and supporting community-based work that’s grounded, practical, and ethical.