Same data, two jobs
Picture one set of data. A dashboard reads it so a person can decide something. A model reads it so it can decide something. Same rows, same sources. But the architecture around them ends up looking very different.
BI-ready
sources → warehouse → dashboard Data flows one way. A person reads the result. If it refreshes overnight, that's fine.AI-ready
sources → lakehouse → model → action → back into the lakehouse The model acts on the data, and its output flows back in as new data.The way back changes three things
- Freshness. A person can wait for the overnight refresh. A ride-share app matching drivers can't. Once a model is making calls as things happen, "yesterday's data" isn't good enough.
- What you store. Not just tables. Text, images and embeddings, plus everything the model sends back. That's a big reason AI-ready setups lean toward a lakehouse instead of only a warehouse.
- What you track. Where every input came from, and which data the model itself created. This is lineage, and for AI it stops being optional.
The sneaky one: the model learns from itself
A recommender shows people what it thinks they'll like. They click. Next month you retrain on those clicks, and the model learns from its own suggestions.
Nothing broke. No error. But the model is now partly grading its own homework, and its picks can narrow over time without anyone noticing.
Check your own setup
When someone says their data is "ready for AI," these four questions tell you fast:
- Does the model's output get stored as new data, or does it disappear?
- Can you tell which rows the model created, versus what came from the real world?
- How fresh does the data need to be for the decision the model makes?
- Can your storage hold more than tables: text, images, embeddings?
Same data. Different architecture. Which layer do you see teams skip most? Tell me on LinkedIn.
Further reading
The post is an explainer in my own words. These are the classic background pieces if you want to go deeper.