The ELM AI Data Modelling Framework

ou can ask an LLM the following question:
“Create a data model from this business description.”

And it will probably create something that looks remarkably convincing.
That is also the problem.

A plausible-looking model isn't necessarily a good business model.

Where did the concepts come from?
What evidence supports them?
Which concepts were inferred?
Why does a relationship exist?
What hasn't been validated?
Did the AI silently change the model while reviewing it?

These questions matter.

That's one of the reasons Remco created the ELM AI Data Modelling Framework.
Instead of treating AI-assisted modelling as one enormous prompt, the framework separates responsibilities.

🔍 Discover the business concepts.
🧩 Connect them through relationships.
✅ Review what has been created.
💬 Facilitate conversations around uncertainty.
📣 Publish the model into useful outputs.
🔀 Compare & Consolidate different perspectives.

The separation is deliberate.

AI becomes much more useful when we don't just tell it what result we want — but give it a disciplined process for getting there.

That's one of the central ideas behind The ELM AI Data Modelling Framework.

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