Foundation Capital has identified context graphs as a significant opportunity in AI, highlighting their importance for enterprises struggling to demonstrate ROI from AI investments. Traditional systems capture outcomes, such as closed deals or paid invoices, but context graphs provide insight into the processes behind those outcomes. They reveal how decisions were made and the coordination involved, making visible the nuances of organizational memory. For example, when a discount is approved, context graphs illustrate the discussions and evidence that led to that decision, unlike standard systems that merely log outcomes. Most enterprises face challenges in effectively deploying AI due to a lack of context. AI tools often can't replicate successful workflows because they lack insights into informal decision-making processes. The article notes that many work activities don't have clear decision moments, making it difficult to log and analyze relevant actions. Instead of relying on rigid documentation, organizations need to understand how work flows in practice. Context graphs help identify efficient workflows, allowing companies to automate processes that actually work rather than those that are theoretically efficient on paper. Building context graphs is complex because they require capturing the informal and continuous decision-making that occurs in daily operations. Existing systems, like CRMs and ERPs, focus on outcomes and miss the intricate details of how decisions are made. Analytics platforms often reconstruct data from logs but overlook the real-time coordination that happens outside formal systems. Process mining tools track transactions between systems but fail to account for the communication and informal workflows that influence outcomes. Context graphs can bridge this gap, providing AI agents with the necessary organizational context to operate effectively and reliably.