The Trust Gap: Why Enterprise AI Fails Beyond the Prototype

AI has crossed a tipping point—what once lived in prototypes is now expected to power real business decisions. But as organizations push AI into production, a critical gap is emerging: what works in experimentation often fails under enterprise demands for trust, reliability, and accountability.In this talk, Uri Knorovich explores why today’s AI systems struggle to deliver consistent, trustworthy outcomes. While models have advanced rapidly, the external data layer they rely on remains fragmented, stale, and difficult to verify—leading to inaccurate outputs, rising costs, and limited confidence in production AI.Drawing on real-world examples across AI and enterprise infrastructure, Uri will examine why reasoning alone is insufficient without reliable, real-time data, the risks of black-box retrieval systems, and what enterprises must build to bridge the gap between AI experimentation and production-ready systems.