Generative AI has made it easy to summarize and analyze customer feedback at scale. What it hasn't made easy is knowing whether the insight coming out the other end is meaningful. Organizations and insights teams are increasingly under pressure to democratize insights across the business but with this comes risk. The first is that outputs can be accepted without anyone asking whether they're truly reflective, and the second is that human connection to the customer fades away. AI is great at identifying patterns and poor at understanding context and emotional. Where the underlying feedback is incomplete, it doesn't hesitate, it simply scales the incorrect assumption.

 

Ekaterina Mironova works across voice of customer, AI, customer insights and knowledge management at Uber, and previously built a customer experience department from the ground up at an early-stage startup, hiring the team and creating the customer operations, processes, and workflows that helped the company scale rapidly across global markets. In this session, Kate makes an optimistic but clear-eyed case for where AI can help insights functions and where human judgement is still essential. She will argue that AI doesn't reduce the need for customer insights but raises the value of high-quality customer feedback. This session will reframe the ROI conversation that insights teams keep losing: the question isn't what voice of customer costs, it's what it costs to make decisions without truly understanding your customers.