The Data Modeler in 2026: A Role in Transition
"Do we actually still need data modelers if AI can take over?" The question came up after a coaching session. I paused. Not because the question was fundamentally wrong — but because it was the wrong question.
AI in the Data Modeling Workflow: How the Hybrid Approach Works in Practice
Amal leans back and looks at her notebook. Three pages filled. Plus four whiteboards covered in post-its from the requirements workshop. Somewhere in all of that are the business objects she needs for the new data model. "Diego," she asks, "how long did it used to take you to figure out what actually needed to be modeled after a workshop like this?" Diego smiles. "Back in the day? Sometimes a week."
Why AI Can't Define Your Business Objects: The Limits of Generic Automation
AI systems require perfectly structured data but cannot create the necessary data models themselves. Why does even the most powerful AI fail to understand what a "customer" or "product" means in a specific company? And why is precisely this definition work the key to success for every AI implementation?



