I completely agree, but don't you think that relying too heavily on AI can lead to overlooking some of the more nuanced customer needs that humans are better at picking up on?

I've seen some companies struggle with integrating AI-driven solutions because they overlook the importance of data quality; how do you think we can ensure that the data feeding these solutions is accurate and unbiased?

I completely agree, I've seen some impressive results from incorporating AI-driven insights into our product roadmap, but struggle to quantify the ROI of human intuition - have you come across any frameworks for measuring that?

We actually tried to implement a similar approach last quarter, but found that our AI models were over-relying on historical data and missing some key nuances - how do you think we can better calibrate AI to account for those edge cases?

I'd love to add that our team faced a similar challenge and found that involving cross-functional teams in the AI decision-making process helped strike a better balance between AI-driven solutions and human intuition, what are your thoughts on that approach?

I'd love to add that our team faced a similar challenge and found that involving cross-functional teams in the AI-driven solution design helped ensure that human intuition wasn't lost in the process, what are your thoughts on that approach?

I've seen some success with that balance by having AI handle the repetitive tasks and freeing up human designers to focus on empathy-driven decisions, but how do you think we can measure the ROI of that human intuition in product development?