I ntegrating context to support AI development provides a number of potential benefits for efficient teaming and collaborative task accomplishment for human-robot teams. For military teams in particular, integration of context into AI architectures is essential to facilitate collaboration and successful operation in complex and dynamic environments. Take, for example, when a soldier reports that a hostile threat is in a target area. Given this information, a robot could be expected to change how it navigates to the target environment, make its primary objective enemy detection, and provide guidance for the movements and future actions of both friendly and adversarial human counterparts so that team members can remain undetected. However, a human teammate's interpretation of the robot's behaviors is directly influenced by the robot's ability to adequately communicate reasoning for its own previous and current actions. Otherwise, its behavior may appear ambiguous or incorrect from a human perspective. Therefore, the robot needs to understand both how context will or could affect its own decisions as well as how it could affect team members' decisions. Integrating contextual understanding allows shared situation awareness and shared mental model development, improves joint decision making and categorization of data, provides better processing times, and enhances learning both online and offline for the team.