Why Your AI Gives Wrong Answers About Your Own Company
If two people in your company would answer a question differently, the AI has nothing to be right about.
Your AI can explain a tax treaty and cannot tell you how many customers you have. It writes a decent first draft of anything general, then states something about your own business that is confidently, specifically wrong. Everyone notices. Nobody can explain it, because the obvious suspect, the model, is the one thing that appears to be working.
Why does AI give so many incorrect answers?
For general questions, usually because it is filling gaps. The system produces the most plausible continuation, and when it has no solid ground it produces a plausible one anyway, with the same confident phrasing it uses when it is right.
For questions about your own company, the cause is usually different, and it is the one nobody checks: there was no right answer available to find. If your finance team and your sales team would give different numbers for the same question, the system has nothing to be right about. It picks one, or it blends them, and the result is wrong for at least half the people asking.
Why does AI keep giving me the wrong answers?
Because the thing producing the error has not changed between attempts. Rephrasing a question, adding examples, or switching to a better model all address how the answer gets generated. None of them addresses what the answer is drawn from.
If the underlying facts are contradictory (three systems holding three versions of the same figure, a term two departments use differently, a status field that meant one thing before the migration and another after), then every attempt draws from the same contradictory pile. A better model produces a more articulate version of the same wrong answer.
There is a version of this that is easy to picture. Someone posted a demo on Hacker News under the title your RAG still thinks the old CFO is the CFO. The retrieval worked perfectly. It found a true-sounding sentence, in a real document, that was no longer true.
What should I do if my AI gives a wrong answer?
Trace it before you tune anything. Three questions, in this order.
- Where did that come from? Find the actual source of the claim. If the system cannot tell you, that is the first problem, and it is not a model problem.
- Would two people here answer this differently? Ask two. If they disagree, the AI was never going to be right, and no prompt fixes it.
- When was the source last true? Plenty of wrong answers are old correct answers that nobody retired.
Only if all three come back clean is it worth looking at the model, the prompt or the retrieval settings.
Why does AI give incorrect information about internal data specifically?
Because internal facts are decided, not discovered. What counts as an active customer, which subsidiaries roll into a parent, when revenue is recognized: none of these can be looked up in the world. Somebody decided, or worse, several people decided differently and never reconciled it. General knowledge has been argued out in public over decades. Your company's definitions mostly have not been argued out at all.
The scale of the gap is measurable. In a Dun & Bradstreet survey of 10,000 businesses across 32 countries, 97% of organizations reported active AI initiatives while only 5% said their data was ready to support them.
The fix is not a better model. It is deciding what the system is allowed to treat as true, writing it somewhere the system can reach, and keeping it current, which is a discipline with a name and a method. Start here. If the specific failure you are seeing is answers that were right last quarter, keeping context from going stale is the practical next step.
