It's so easy to start from the top, with the fancy shiny stuff around agentic AI and generative AI. And that's also how most people define AI nowadays.

However, there are other things underneath that need to work.

I think it's super important to really understand the bottom thing here, the data foundation, because that's what everything is built on.

If you don't have that in place, the ownership, the definitions, the data quality, the lineage, then everything above it will break. It will start to hallucinate, and you will make decisions based on unsolid information. And that will just scale things, making it worse and worse.

In history, the consequence was that you could have the wrong figures or the wrong metrics in the dashboard.

With generative AI, it just makes decisions based on whatever data it has. It doesn't ask twice if the definition was the right one. It just uses it to make decisions, and then you have scaled the mess.

So start with the foundations, fix that, and then you can start to implement things.

Everything that needs to be agentic and generative AI should focus on the business problem and the use case. That will steer which technology or method you will use. It's been the same for years, with cloud and ERP and other things as well.

Start by fixing the foundations first.

I've written down how to start on that in 30 days, you find the plan here: https://lnkd.in/dRkqe64E

P.S. Have you ever seen an AI use case that could be solved with something more simple?