I've been a software and enterprise architect for over twelve years. I've shipped pricing platforms, fraud detection systems, and order management infrastructure at scale — most recently at one of the UK's largest retailers. I say that not to flex, but to explain why I'm writing this post with a specific kind of frustration. Because almost every article I read about AI in enterprise sounds like it was written by someone who has never been paged at 2am because an LLM-backed pricing rule marked 40,000 product lines as zero. So here's what actually happens when you put AI into systems where the decisions have consequences. The staging trap Staging environments lie. They lie about load, they lie about data shape, and — critically for AI systems — they lie about context drift. Context drift is when the world changes between the moment you assembled the input to your model and the moment the model's output takes effect. In a pricing engine, that gap can be milliseconds.…