Session: Truth Before Inference: Fixing “Stupid Data” Before Delivering Enterprise Fail
Your AI projects are built to fail. Despite the massive hype, the industry is hitting a wall because 80% of enterprise AI pilots ignore a fundamental truth: AI cannot create intelligent answers out of stupid data, and undocumented process.
We’ve spent the last year watching companies bolt expensive AI architectures onto existing data sources, only to watch them hallucinate themselves into oblivion. The problem isn’t the models; it’s the Private Data Paradox. Unlike public AI that succeeds on the massive scale and redundancy of the internet, your private data is a grain of sand—unstructured, contradictory, and often flat-out wrong.
In this session, we’re dismantling the “dump and hope” strategy of feeding raw PDFs and conflicting records into vector databases. We’ll stop talking about exceptions and start showing you how to build a verified Source of Truth (SoT), with or without our help.
