You Built the AI Feature. Now Sell It to the C-Suite Without Getting Stonewalled You've shipped a brilliant ML feature. The accuracy metrics are solid, the API is clean, and your team is buzzing. Then you present it to the exec team and hit a wall of "let's revisit this next quarter." Sound familiar? The problem isn't your code. It's that you're speaking a different language about risk, accountability, and control. Here's how to bridge that gap without dumbing down your work. The Real Objection Isn't Technical When a senior exec pushes back on AI features, they rarely say what they're actually worried about. They'll talk about "data quality concerns" or "needing more validation," but the underlying fear is simpler: who gets blamed when the AI screws up? This is especially true for executives who built their careers in the 80s and 90s, when accountability meant your signature on a decision. The idea of delegating judgement to a statistical model feels like abdicating responsibility.…