Graph technology is being adopted by enterprises to gain a competitive advantage and drive insightful decisions, particularly in areas such as decision support with knowledge graphs, efficiency of processing with graph accelerated ML, accuracy with connected feature extraction, and transparency into AI decision-making. These technologies are crucial for valuable AI applications, especially in critical business areas like network security, where preventing attacks and securing corporate networks is a top priority. Companies like Fujitsu Laboratories and Larus, a Neo4j partner, are developing new graph AI technologies, such as Deep Tensor, which can automatically extract relevant features from graph data and provide additional explanations for AI-generated findings. By leveraging these technologies, organizations can enhance the efficiency of their business operations by extracting actionable insights from connected data, ultimately driving business advantage through informed decision-making.