The authors investigate the kinds of difficulties that the Army might encounter as it attempts to pair humans with artificial intelligence (AI) algorithms to accomplish specific warfighting tasks. They make recommendations to address how these potential obstacles can best be overcome and ensure that the Army effectively creates AI systems that will integrate well with the soldiers who must interact with them.

One Team, One Fight

Volume I, Insights on Human-Machine Integration for the U.S. Army

Jonathan P. Wong, Alexander C. Hou, Michael Miller, Katie A. Wilson, Emily Lathrop, Sydney Kessler, Sam Wallace, Emily Yoder

ResearchPublished Jun 2, 2025

Cover: One Team, One Fight
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Includes One Team, One Fight: Volume II, Ground Combat Narratives of Human-Machine Integration for the U.S. Army

Advances in artificial intelligence (AI), machine learning, and robotics have raised the possibility that the profession of arms will soon include integrating human soldiers with AI-enabled machines and applications as part of the collective whole. Machines and software applications enabled by AI are starting to demonstrate capabilities that are relevant to military settings, such as moving autonomously through complex urban traffic and creating startlingly humanlike and interesting derivative works through large language models.

However, this does not mean that such developments can be implemented in military settings smoothly. The practice of building cohesive small units is no easy endeavor. The best small units cohere to the point where one soldier recognizes the silhouette and gait of another in the dark of a patrol base in an instant. The best staffs internalize their commander's style and specific needs over time. Integrating humans and machines in military contexts will likely draw from civilian parallels but will also require substantial contextualization.

In this report, the authors investigate the kinds of difficulties that the Army might encounter as it attempts to pair humans with AI algorithms to accomplish specific warfighting tasks. They make recommendations to address how these potential obstacles can best be overcome and ensure that the Army effectively creates AI systems that will integrate well with the soldiers who must interact with them.

Key Findings

  • Efforts to increase trust by designing AI interfaces that explain AI decisions (explainable AI) or prompt humans to think more carefully about AI inputs (cognitive forcing functions) are less effective than expected. This inability to increase human trust is especially critical because trust in AI is a key concern for the Army.
  • The underlying ideas of design, signaling, and mental models remain promising, especially when they are instantiated over time to help humans build familiarity with machines.
  • The integration of humans and machines in the Army in the realm of ground combat planning and execution is likely to be slower than integration into the commercial sector.
  • Efforts to integrate humans and machines in the Army are predominantly focused on making the machine fit the human, rather than the human adapting to the machine.

Recommendations

  • Once machines are fielded to units, the Army should integrate such machines like new soldiers.
  • The Army should prepare for variation in how AI-enabled machines and applications perform based on the human dynamics of the unit they are integrated into.
  • The Army should monitor how machines gain and maintain trust.
  • The human must remain the dominant partner in human-machine integrated units.

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Wong, Jonathan P., Alexander C. Hou, Michael Miller, Katie A. Wilson, Emily Lathrop, Sydney Kessler, Sam Wallace, and Emily Yoder, One Team, One Fight: Volume I, Insights on Human-Machine Integration for the U.S. Army. Santa Monica, CA: RAND Corporation, 2025. https://www.rand.org/pubs/research_reports/RRA2764-1.html.
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The research described in this report was sponsored by U.S. Army Futures Command and conducted by the Strategy, Doctrine, and Resources Program within RAND Arroyo Center.

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