Why LLMs Make Confident Mistakes When Analyzing Your Product Data
Product teams have started routing analytical questions directly to LLMs: "What's causing the churn spike?" "Why did conversion drop after the redesign?" "Which cohort should we focus retention spend on?" The outputs land in executive decks, drive roadmap decisions, and get presented to investors. The models answer confidently, in polished prose, with specific numbers. And a significant fraction of those answers are wrong in ways that don't announce themselves.
This isn't a general criticism of LLMs for data work. There are tasks where they genuinely help. The problem is that the failure modes are invisible — the model doesn't hedge, doesn't caveat, and doesn't distinguish between "I computed this from your data" and "I generated something that sounds like what this number should be." Practitioners who understand where the breakdowns happen can capture the genuine value and route around the landmines.
