Off-the-shelf AI is enough when a wrong answer is cheap and a human checks the output anyway. It stops being enough the moment the AI touches your real documents, your numbers, or a regulated decision, because a generic model will answer confidently even when it is wrong. A custom build fixes that with retrieval, citations, human approval, and evaluation, so the system either backs up a claim with a source or does not make the claim at all.
That last line is the whole article. Everything below is how you get there, when it is worth it, and how to find out cheaply before you commit a full budget.
Wiring up a chatbot is not building an AI system
Most "AI features" you see are a text box wired to a model API. You type, the model answers, done. That is a demo, and demos are useful. The problem is that a demo has no idea what is true. It was trained on the public internet up to some cutoff date, and it has never seen your lease agreements, your lab results, your cap table, or last month's numbers.
Ask a generic tool a question about your business and it does one of three things: it answers from stale general knowledge, it answers from whatever you happened to paste into the window, or it fills the gap with a plausible invention. The third one is the dangerous one. The model does not signal doubt. A fabricated clause and a real clause come out in the same calm, formatted sentence.
