Unpopular take: most enterprise AI pilots never reach production because they apply generative models to problems that require discriminative ones

After nearly thirty years building distributed systems in financial markets and enterprise data infrastructure, I keep seeing the same pattern. A fraud detection model running continuously, updating from operational data, making millions of auditable decisions daily. Nobody calls it AI. It just works. Has been working for years. Meanwhile the same company has been running a frontier LLM pilot for eighteen months. Still a pilot. Output requires human review on every decision. Governance layer not ready. Auditability question unanswered. No ROI generated. The gap comes down to one mathematical distinction most people skip: Discriminative ML finds θ = argmax P(y | x ; θ) . Your parameters, your data, your operational distribution, continuously updated from your stream.* Generative LLMs find x = argmax P(x | x_prompt ; θ). Someone else's parameters, someone else's corpus, frozen at training time regardless of how many of your events flow through it. These are not two versions of the same thing. Different mathematical objectives, different inference directions, different update mechanisms. Wrote the full argument here including the neuroscience analogy that surprised me, the fine-tuning and open weights rebuttals, and the data sovereignty / CLOUD Act angle for European enterprises: https://medium.com/@dcris19740101/the-enterprise-already-has-ai-it-just-does-not-call-it-that-2f38b2afa3f5 Curious what others are seeing in production environments.