AI can help organizations search, analyze, summarize, and work with large volumes of enterprise information. In eDiscovery, however, introducing AI is not simply a productivity decision. The data may be sensitive, context matters, and AI-generated conclusions can influence consequential human decisions.

This session examines AI in eDiscovery from an enterprise architecture and governance perspective. Using the eDiscovery workflow as a practical model, we will explore where AI can assist, where human review remains essential, and how organizations can establish clear boundaries for data access, validation, decision rights, and accountability.

Attendees will learn a practical framework for asking critical questions before introducing AI into an eDiscovery workflow: What data should AI be allowed to access? What can it analyze or infer? Which outputs require human validation? Where should AI assistance end and human judgment begin? And ultimately, who is accountable for the outcome?

The session connects eDiscovery, AI governance, data security, and enterprise architecture to help organizations adopt AI while maintaining appropriate human oversight and accountability for sensitive enterprise information.