Semantic Layers - The Foundation for AI-Powered Analytics

With AI, it can go two ways. If you don't use something like a semantic layer to govern what the AI is allowed to reference and to govern how the AI accesses data, you will end up with less operational clarity than you started with because the AI will generate its own version of the truth every time you request it. So if you have something like a semantic layer where the AI has this menu of measures and dimensions that it can choose from and then also can't isn't asked to calculate those, it's just asked to request those. And those requests are much more similar to, like, give me, revenue by market. It's not that dissimilar to asking that question. You know? It's just in JSON. And so you're just forcing that the shape of the question to be in JSON. And so when you do that, you can get better operational clarity because people aren't having to go and redefine those metrics or do it in Excel or something. They just ask for it and they get it. And they what they get is the governed definition of it. And even if there is, like, seventeen different versions of revenue, you get told which version you're given by the AI. And you can say that's not the one I wanted. The one I wanted didn't have depreciation and was based on an operating basis. Right? And it will then go and read all of the descriptions for those metrics and choose the right one, that you want according to closest, like, vector similarity in reality. And that that is so much better. And, you know, you're gonna get clarity, and people aren't gonna invent the truth. They they're gonna use the engineer defined versions of the truth in the semantic way.