About

Hear real world examples on how Fortune 500 companies are deploying AI in production. How to ensure trustworthy data use by AI with a data-access control plane. Break down what holds enterprises back from trusting their AI use cases, and how to quantify and communicate key signals of trustworthiness.Introduce three architectural models to measure, inform, and control how AI uses enterprise data to improve data and AI trust. Learn how Bigeye’s AI Trust Platform helps AI teams earn trust in AI applications with their business stakeholders, leadership, and customers.

Takeaways:

• Data quality is the #1 blocker to enterprise AI adoption. 44% of organizations cite it as their biggest challenge, and Gartner predicts 40%+ of agentic AI projects will be canceled by 2027.
• AI turns small data problems into enterprise-scale crises. When agents access and act on flawed data across an organization simultaneously, the impact is exponential.
• Governed AI lets enterprises have speed and safety. Real-world examples from oil & gas, manufacturing, and insurance show that the right approach delivers visibility, control, and competitive advantage.
• Bigeye’s AI Guardian gives teams three ways to operationalize trust. Monitoring observes how AI accesses data, Advising surfaces data quality signals in real time, and Steering controls what data AI can reach via a Data Access Gateway.