Why 80% of AI projects fail: Stop treating LLMs like SaaS and start treating them like Infrastructure.

You see the stat floating around everywhere: roughly 80% of enterprise AI projects fail in production (the RAND corporation actually puts it at 80.3%). Everyone wants to blame the models, but having watched this unfold where I work, the problem isn't the technology. The problem is the people deploying it. Right now, AI initiatives are being led by smart, well-meaning business people who are completely technically illiterate. They see a vendor demo and think an LLM is a plug-and-play SaaS product. It isn’t. An LLM is complex, unpredictable technology. It needs to be treated with the exact same rigor as your enterprise infrastructure, your firewalls, your switches, and your routers. The business idea might be great, but if the people leading the project don't understand how to build auditable deployment pipelines, manage data workflows, or architect deterministic guardrails, the project is doomed before it even hits production.