Graph technologies have increasingly been integrated with machine learning and artificial intelligence solutions, enabling better predictions, flexibility, and explainability in various applications. By incorporating context into AI models, relevant information can be added to existing data, resulting in improved predictive accuracy. This is particularly evident in the fraud detection and prevention market, where graph algorithms are used to reveal predictive patterns, find unusual behavior, and score influential entities. Additionally, contextual awareness and situational flexibility are crucial for AI systems, enabling them to adapt to new situations, reduce failures, and improve resilience. The use of graph technologies has a broad applicability across various industries, including financial services, software firms, logistics companies, retailers, airlines, telecommunications firms, and hospitality companies. By incorporating context into AI models, robustness and reliability can be enhanced, leading to more accurate predictions, flexibility, and improved decision-making.