AI can now read, tag, and cluster thousands of reviews, tickets, and transcripts in minutes — work that used to take a research team weeks. But faster isn't automatically better: clustering can flatten nuance, amplify the loudest segment, or miss feedback that doesn't fit a neat category. This panel brings together leaders who've put AI feedback tools into production to debate where the technology genuinely earns its place, where a human still needs to check its work, and what guardrails they've built in to catch it when it gets things wrong. Key takeaways: - Where AI-assisted clustering has measurably sped up insight work, and where it's introduced blind spots - What guardrails or human-in-the-loop checkpoints leading teams have built in - How to tell the difference between a genuine signal and an artifact of the model's own biases