The AI-Ready Data Trap

AI helps. It can make data easier to access, remove tedious work, explain complex systems, and give more people the ability to ask questions without mastering every technical layer underneath. That usefulness is very real. It is also how AI spreads, becomes normal, and eventually turns into infrastructure that organizations struggle to question or refuse.

This session looks at what data professionals are actually building when they make an organization AI-ready. Whose knowledge gives these systems their apparent intelligence? Who owns the infrastructure? Who captures the productivity gains? What happens to expertise once it has been extracted, standardized, and made available to agents? Who remains accountable when human judgment is hidden inside semantic models, permissions, metrics, and automated decisions?

This is not a talk about using AI correctly or writing better prompts. It is for people who use AI, understand that it can be genuinely useful, and still refuse to treat usefulness as proof that every organization should reorganize itself around it. The question is not only how we prepare our data for AI, but who gains power once we do.