AI is everywhere in our PM toolkit now, and it’s a game-changer for processing user feedback. We can instantly summarize thousands of survey responses, analyze sentiment from support tickets, and even generate user personas based on data. The efficiency gains are undeniable.
But here’s the uncomfortable question: are we becoming lazy?
Are we letting AI-generated summaries replace the hard work of genuine customer empathy? An algorithm can tell you what users are saying, but it often misses the why. It can’t replicate the visceral “aha!” moment of hearing a customer’s tone of voice during an interview, seeing their frustration as they navigate a prototype, or uncovering an unstated need that was never explicitly mentioned in a feedback form.
These deep, qualitative insights are the bedrock of innovative products. Over-relying on AI for research risks creating a layer of abstraction between us and our users, leading us to build for the statistical mean rather than for real human beings. While our team at Leera.io uses AI to help synthesize feedback, we treat it as a tool to point us to the most impactful conversations, not replace them.
How are you and your team striking the right balance between leveraging AI for research efficiency and maintaining a deep, authentic connection with your customers?