Enterprise AI adoption is accelerating—but with it comes a hard question: Do we trust the model’s decisions? In this 18-minute talk, I’ll explore the invisible risks behind automated decision-making in safety-critical and revenue-sensitive environments. Drawing on case studies across manufacturing, telecom, and industrial IoT, I’ll highlight how explainability, traceability, and robust guardrails drive adoption and protect enterprise value.

Attendees will walk away with:

A 3-step framework for operationalizing AI trust
Real-world lessons from building guardrails in on-prem and hybrid systems
Tools and techniques for debugging and explaining inferences at scale
A blueprint for building trust between models, engineers, and executive stakeholders