Most AI projects run into the same problem: a capable model, a motivated team, and a data set that has accumulated for years without cleanup. The wrong answers usually don't come from the model — they come from the data fed into it.
Teams often assume the fix is "a better model." It almost never is. The model is rarely the problem — the data it receives is.
Here's a rule that's hard to accept but always holds: AI doesn't fix your data. It repeats it. If documents contradict each other, the AI will surface both. If information is outdated, the AI quotes the outdated version. If a file is unreadable, the AI literally cannot process it.
The model is only as good as the data you give it. And most organizations' document collections contain significant noise and duplication.
Common Data Quality Issues
