Today, most enterprise AI helps people summarize, write, and search documents faster. The harder, more valuable work (engineering changes, batch releases, maintenance decisions, or insert industry examples here if a vertical specific audience) depends on knowledge buried across decades of systems, and leaves no room for error/guessing.

This session covers what it takes to bring AI into that work <or, into that work in life sciences, manufacturing, etc>: grounding it in your own knowledge and context, and governing what it can see and do, whether your teams are using it or agents are acting on their behalf.