Why Enterprise AI Fails: Fixing Dirty Data Before the Black Box | Joe Onisick & Keith Townsend скачать в хорошем качестве

Why Enterprise AI Fails: Fixing Dirty Data Before the Black Box | Joe Onisick & Keith Townsend

Most AI projects aren’t failing because the models are bad. They’re failing because enterprise data isn’t trustworthy. In this CTO Advisor deep dive, Keith Townsend sits down with Joe Onisick, CTO of UnicornIQ, to challenge one of the biggest assumptions in enterprise AI: What if the real bottleneck isn’t inference… but confidence in your own data? We break down: • Why “garbage in, garbage out” is accelerating in the AI era • The hidden cost of fixing problems at the inference layer • What “Zero Trust Data” actually means • Why RAG pipelines amplify dirty unstructured data • The enterprise “black box” problem • Deterministic outputs from non-deterministic systems • Confidence scoring + data provenance • A practical, ROI-aware approach to Human-in-the-Loop • Turning tribal knowledge into structured enterprise truth This conversation isn’t hype. It’s architecture. If you're a CTO, CIO, AI platform owner, or enterprise architect trying to move from AI experiments to production-grade systems — this one’s for you. 🔗 Learn more about UnicornIQ: https://unicornIQ.ai 🔗 More from The CTO Advisor: https://ctoadvisor.com