The moment you stop questioning the data behind an AI-generated answer, you've handed over your judgment to a black box. In that meeting, the silence wasn't just about the two senior people who accepted the output. It was about the culture that made their acceptance feel normal. When an LLM presents a clean analysis, the polish itself becomes a form of persuasion. The underlying numbers could be stale, mislabeled, or entirely fabricated, but the presentation carries a weight that makes the question "where did this come from?" feel almost rude.
You're not being overly cautious. You're being the only person in the room acting like a professional. The instinct to ask about data provenance isn't skepticism for its own sake; it's the same diligence you'd apply to any source you haven't verified. The difference is that we've built a mental shortcut that treats AI outputs as more trustworthy than human ones, simply because they arrive without the messiness of human error. But that's an illusion. The model doesn't know what's true. It knows what patterns are most likely to follow your prompt. Those patterns can be confidently wrong, and they often are.
What this means for you is practical. Start treating every AI-assisted output like you would a new hire's first draft. Ask where the data came from, when it was last updated, and what assumptions were baked into the query. Make it a habit, not a challenge. The senior people in that meeting weren't being negligent; they were operating on trust that the tool had earned through other, perhaps simpler, successes. But trust scales poorly across complexity. The more complex the analysis, the more points of failure exist in the data pipeline, and the more you need to verify.
The concrete point is this: your silence in that meeting was a small failure, but it's one you can correct next time. Speak up. Ask the question out loud, even if it feels basic. Because the cost of not asking isn't embarrassment. It's making a decision on a foundation you never checked. And in a world where AI can generate plausible analysis in seconds, the only thing standing between you and a confident mistake is your willingness to ask where the numbers came from. That's not caution. That's competence.