Most AI initiatives start with platform or prompt training — teaching people. The real starting point is data, because the thing that most needs training is the AI itself.
Data structure is how you train the AI.
Having data doesn’t mean AI can use it. Some structures produce accurate answers; others produce confident garbage. Good structure holds the information you actually need, stays consistent across your datasets so the answers don’t contradict each other, and is shaped so the AI can read it.
The AI is the new hire nobody onboarded.
An AI agent is a brilliant new hire — capable, but brand new to your world. It needs onboarding, and you don’t do that in a classroom. You do it with your data. Orgs skip this because “training” makes them picture people in a room. The one that never got trained is the AI.
I learned this the hard way.
I didn’t know structure was the lever when I started — the answers only became trustworthy once the data was structured and consistent. AI exposed the problem. Now I can tell you the truth I paid for.
So what, now what.
So what: “AI training” that only trains people skips the actual bottleneck — the AI, which you train through data. Now what: before your next AI training, ask whether your data is structured and consistent enough for the AI to answer accurately. If not, that’s where the work is.
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