It is difficult to imagine a society that does not utilize teams. At the same time, teams need to evolve to meet today’s challenges of the ever-increasing proliferation of data and complexity. It may be useful to add artificial intelligent (AI) agents to team up with humans. Then, as AI agents are integrated into the team, the first study asks what roles can AI agents take? The first study investigates this issue by asking whether an AI agent can take the role of a facilitator and in turn, improve planning outcomes by facilitating team processes. Results indicate that the human facilitator was significantly better than the AI facilitator at reducing cognitive biases such as groupthink, anchoring, and information pooling, as well as increasing decision quality and score. Additionally, participants viewed the AI facilitator negatively and ignored its inputs compared to the human facilitator. Yet, participants in the AI Facilitator condition performed significantly better than participants in the No Facilitator condition, illustrating that having an AI facilitator was better than having no facilitator at all. The second study explores whether artificial social intelligence (ASI) agents can take the role of advisors and subsequently improve team processes and mission outcome during a simulated search-and-rescue mission. The results of this study indicate that although ASI advisors can successfully advise teams, they also use a significantly greater number of taskwork interventions than teamwork interventions. Additionally, this study served to identify what the ASI advisors got right compared to the human advisor and vice versa. Implications and future directions are discussed.

Intelligence analysts’ work has become progressively complex due to increasing security threats and data availability. In order to study “big” data exploration within the intelligence domain the intelligence analyst task was abstracted and replicated in a laboratory (controlled environment). Participants used a computer interface and movie database to determine the opening weekend gross movie earnings of three pre-selected movies. Data consisted of Twitter tweets and predictive models. These data were displayed in various formats such as graphs, charts, and text. Participants used these data to make their predictions. It was expected that teams (a team is a group with members who have different specialties and who work interdependently) would outperform individuals and groups. That is, teams would be significantly better at predicting “Opening Weekend Gross” than individuals or groups. Results indicated that teams outperformed individuals and groups in the first prediction, under performed in the second prediction, and performed better than individuals in the third prediction (but not better than groups). Insights and future directions are discussed.