Graph technology platforms like Neo4j enhance AI by providing context for improved efficiency through the use of graphs, which offer greater processing power and allow for faster analysis of data connections. Traditional machine learning methods often rely on table-based data storage, leading to resource-intensive processing and challenges in iterative model training. Graphs enable relationships of numerous degrees of separation to be traversed and analyzed quickly at scale, accelerating processes such as filtering and collaborative filtering. This approach also helps address scalability issues by returning only the needed data through simple graph queries. By leveraging graphs, AI can provide context for decision support while reducing computational intensity and human involvement, ultimately enhancing the efficiency of machine learning algorithms.