Most AI systems perform well in controlled demos but fail to scale in production. The gap is not just model quality, it is trust.

This session introduces a practical framework for building trust into AI systems so they can move from promising pilots to reliable, production-ready experiences. We will break down what “trust” actually means in deployed systems, including confidence scoring, risk signals, moderation layers, and operational visibility.

Using a real product journey as a case lens, this talk explores how trust can be designed as a system layer - Connecting model outputs with user safety, governance, and decision-making. We will cover how to instrument trust signals, define escalation paths, and integrate monitoring that goes beyond traditional observability.

Attendees will leave with a clear, actionable framework to design, evaluate, and operationalize trust in their own AI systems - whether they are building consumer products, enterprise tools, or platform-level infrastructure.