We’ve all seen how AI can superpower our product work by summarizing user feedback and transcribing interviews. But the conversation is rapidly shifting. Now, we’re seeing AI tools that don’t just analyze discovery data — they actively generate it.
I’m talking about AI creating detailed user personas from a simple prompt, drafting entire user interview scripts, and even simulating customer feedback sessions. On one hand, this is a massive efficiency gain. It can help us overcome writer’s block, explore more personas than we have time to interview, and accelerate the early stages of discovery, freeing us up for more strategic thinking.
On the other hand, it puts a layer of abstraction between us and the user. Are we losing the genuine empathy and unexpected insights that only come from a real conversation? As we integrate these powerful tools, it becomes even more vital to have a robust system for capturing and analyzing real customer conversations to validate these AI-generated assumptions. Centralizing genuine feedback in an insights hub, like we’re building at https://leera.io, ensures we’re augmenting our intuition, not replacing it.
This isn’t about rejecting the technology, but defining the new best practices for using it responsibly.
How are you using generative AI in your discovery process, and where do you draw the line between helpful assistance and a dangerous abstraction from your users?