Research across 101 enterprises reveals that AI organizations face a fundamental trust problem with the business context fed to AI systems. While retrieval-augmented generation (RAG) has become the default approach for providing AI agents with business information, most enterprises lack adequate governance over what data is retrieved and how trustworthy it is. Provider-native retrieval solutions are quietly becoming more common than dedicated vector databases, but this doesn't solve the underlying trust and data quality issues.

The gap centers on data governance: enterprises are building infrastructure to feed AI agents business context faster than they can establish processes to ensure that context is accurate, current, and trustworthy.

What This Means for Your Business

Before expanding AI agent deployments in your organization, establish a data governance framework specifically for AI systems. This means defining data ownership, establishing refresh cadences, creating audit trails for what data was fed to which agents, and implementing quality checks on your context sources. The technology for retrieving data is mature; what's missing is the governance layer that ensures enterprises trust the data their AI systems are operating on.