87% of AI projects stall not because the models fall short, but because enterprises lack the reasoning context needed to turn intelligence into action.  

Enterprise AI is entering a new phase. For decades, systems of record have captured the canonical state of the business—what happened, what exists, and where things stand today. But agents need more than facts. But as AI agents take on increasingly complex decisions, knowing what is true is no longer enough, which is why 87% of AI projects stall as they lack the reasoning context needed to turn intelligence into action. 

This is driving a trillion-dollar shift—from systems of record to systems of context. At the center of this shift are Context Graphs: a living layer that connects entities, events, decisions, and precedent across time, giving agents the context they need to move from answering questions to making informed decisions. 

So, what does meaningful enterprise context really require? How is context structured today? What business impact can it unlock? And why are so many organizations still caught between their ambition for AI and the infrastructure required to make it work? 

Join experts from Everforth Quinnox and a guest speaker from Forrester for a thought-provoking discussion on the rise of the context layer, the convergence of AI models, and the foundations needed to build context-rich enterprise intelligence.