More than half of these postings ask for genuine product understanding. For anyone moving from engineering, support or consulting into a commercial role, that is the differentiating asset — and it is worth being specific about what it buys.

• Credibility with the technical evaluator, who is frequently the person who can kill the deal and is rarely the one being sold to.
• You can qualify out faster. Understanding that the product genuinely does not fit saves months, and reps who cannot assess that carry dead pipeline.
• You can carry a conversation without a solutions engineer in early meetings, which is a scheduling advantage and a trust advantage.
• You can name a real weakness, which is the fastest route to being believed on the strengths.

Selling an AI product has a specific difficulty: the buyer cannot verify the claim from a demo, because a demo shows the good case and the product is probabilistic. What the postings and the market reward here is honesty about the quality bar — being able to say what accuracy looks like on their data, what happens when it is wrong, and what the human-review path is. That is the same argument the AI books here make from the engineering side, arriving at the same place: the question is not how accurate it is but what happens when it is wrong.