Many AI systems fail not because the models are bad, but because the system around them delivers insight too late.

This talk examines why real-world AI often underperforms and introduces a framework for building predictive and prescriptive systems that surface problems before they become expensive failures. Drawing from real-world project data, failure analysis, and examples where small errors caused outsized impact, we’ll explore how timing, feedback loops, and system design matter more than model complexity.

This session is aimed at developers, IT professionals, and AI practitioners who want to build systems that actually change decisions.