AI support should be judged by what happens when it fails not by automation percentages. Reliable systems need more than an LLM and a knowledge base: they need escalation rules, accurate knowledge, observability, human handoffs, and edge-case workflows. The final 10% often demands more engineering than the first 90%. The strongest architecture is hybrid: AI handles predictable requests; humans handle exceptions and high-value interactions. The goal isn’t maximum automation. It’s reliable automation with a safe fallback.