Yes, fastest moving teams are focussing more on unified data platforms, strong governance ,workflow-native AI , measurable operational outcomes. Few clear trends are - smaller and specialized models for cost and reliability ,focus on structured , governed environments like Databricks (unity catalog ), Retrieval/content quality, embedded AI workflows . So AI is moving towards data governance and discipline more than AI model race.

Few weeks ago I already wrote an article about it Are we still keeping the same data warehouse modelling standards in 2026 with all this AI?

AI changes the way people interact with data but it does not remove the need for modelling.

A bad data model with AI is still a bad data model.

Actually, it is worse, because AI can make wrong answers look confident.

Because in the AI era, trusted data is not optional.

It is the product.

Great point on modelling standards. The question we keep coming back to in practice is where the medallion layers draw the line - bronze as raw fidelity, silver as governed truth, gold as business ready. That boundary is a modelling decision, not a technical one. AI sitting on top of gold only works if that boundary was drawn correctly in the first place.

Coming from a statistics background, this makes a lot of sense to me. Before any model, we always spend most of the time just cleaning and validating data. the actual modeling is usually the smaller part.
I think that's why the medallion architecture caught my attention when I started learning about DE. It basically forces you to be honest about data quality at each step before moving forward.
One thing I'm still trying to understand as someone new to this: how much of the silver layer work ends up being automated vs decisions that someone has to make manually? That part seems like where things get tricky.