Most AI projects don't fail because the model isn't smart enough. They fail because of what we feed them. Gartner predicts that through 2026, 60% of AI projects unsupported by AI-ready data will be abandoned — and as AI agents move from pilots into production, data quality has become the top barrier to scaling AI value. So what does it actually mean for data to be ready for AI? Is there a gap between "clean data" and "AI-ready data"? Spoiler alert: there is — and it's where a surprising number of projects get lost. In this session, we'll look at how AI systems don't catch bad data — they launder it: scaling errors faster, and wrapping them in false confidence. You'll leave with a practical way to think about preprocessing as the gate before AI touches anything that matters. Join this talk to learn more and discuss: - Why AI amplifies data problems instead of absorbing them — and why errors get more expensive once decisions are automated - The difference between clean data and AI-ready data, and why passing quality checks isn't enough - How AI systems fail silently: the script runs, reports success, and the answer is still wrong - Feedback loops from AI-generated data degrading models over time