The shortcut most professionals love—feeding a report into a language model and using the headline it spits out—works because it promises to collapse a mountain of detail into a single bite. What it doesn’t tell you is that the model’s condensation is a gamble on relevance: it keeps the parts it deems statistically salient and discards the outliers that frequently hold the strategic edge.

That is why a leading photography giant, when testing its own AI‑drafted product brief, found the model’s version omitted the very feature that later defined a market‑changing camera line. The loss isn’t just a missed bullet point; it reshapes the mental model the team builds around the problem, nudging them toward solutions that never address the core user pain.

When the hidden nuance vanishes, the subsequent brainstorming session spirals around a distorted premise, and the final roadmap inherits that bias. The remedy isn’t to abandon AI, but to turn the summary into a checkpoint, not a conclusion, forcing the human mind to re‑engage with the original texture before moving forward.