Tamsin Crossland

RAG can generate answers. Agents can orchestrate workflows. Yet what if the most valuable insight in your organisation isn’t in the documents your models retrieve or the tools your agents call, but in the relationships between your data? Most enterprise systems are not collections of isolated records. They are networks. Customers influence each other. Transactions form chains. Suppliers create dependencies. Devices communicate across infrastructures. Fraud, risk, influence and opportunity often emerge not from individual data points but from patterns across connected entities. Yet many modern AI architectures still treat enterprise data as flat text or tables. RAG retrieves chunks. Agents plan steps. But neither inherently understands the structure of relationships. This is where Graph AI changes the game. Through real-world enterprise examples, this talk will show how Graph AI provides the missing layer for relationship-aware reasoning. The real-world examples will include fraud detection, recommendation systems and knowledge graph–powered AI. As organisations seek to move from generating answers to understanding complex systems, Graph AI represents the next critical step in the enterprise AI stack.