Ai

Everyone wants to talk about AI. Few want to talk about whether their data can actually support it. Here’s the uncomfortable truth: most AI initiatives don’t fail because of model complexity or talent gaps. They fail because the underlying data is incomplete, inconsistent, or trapped in silos nobody wants to touch. Industry estimates suggest data scientists spend anywhere from 50% to 80% of their time just finding, cleaning, and organizing data before any real analysis happens. That’s not a rounding error — it’s a structural problem. ...