Main Conference Agenda 2025

Many AI agent projects stall after promising pilots because they can’t deliver the accuracy businesses need to trust them. A key reason is that enterprise data isn’t just numbers and tables- it comes with strict privacy and governance rules as well as team-specific terms, definitions, and processes. When agents are given access to this data without a system that can enforce governance policies or adapt to evolving definitions in real time, they quickly run into the “accuracy trap”: results that are confidently wrong, non-compliant, or misaligned with how the business actually works. This talk shares lessons from early enterprise deployments on why accuracy debt and weak governance hold back adoption- and how leading teams are building AI agents that respect privacy, adapt to governance, and continuously learn from tribal knowledge to deliver reliable, explainable results.