Graph technology platforms like Neo4j enhance AI by providing context and connections, enabling the extraction of connected features that improve accuracy in predictive models. Relationships within data are often stronger predictors of behavior than traditional input data built from tables, allowing for more efficient analysis and incorporation of relevant information. Connected feature extraction methods use graph algorithms to identify key patterns and structures, such as anomalies in tight communities, which can be used to detect complex behaviors like fraud and money laundering. These features can be engineered or discovered through various approaches, including considering labels or inferred relationships. By leveraging connected features, AI systems become more accurate and easier to explain, making them a valuable tool for various industries.