As part of the Department of the Air Force (DAF)’s efforts to incorporate artificial intelligence (AI) technology, RAND researchers mapped the DAF’s sense-making process, identified its most significant challenges, assessed how AI capabilities could address these challenges, and proposed a suite of AI capability options, along with guidance on how to best implement them. This approach can be applied broadly to any sense-making organization.

Improving Sense-Making with Artificial Intelligence

Kristin Warren, Lance Menthe, Bridget R. Kane, Sydney Kessler, Mary Lee, Domenique Lumpkin, Don Snyder, Li Ang Zhang, Aleksandr Esparza Hartunian, Shawn Konetzki

ResearchPublished Mar 31, 2025

It is widely expected that artificial intelligence (AI) will play a critical role in future military operations. As part of the Department of the Air Force (DAF)’s efforts to incorporate emerging technology into modern warfare operations, RAND researchers were tasked with studying the data, technologies, processes, and policies that the DAF will need to enable effective sense-making in the next decade. To advance the understanding of how these elements intersect with the current state of technology, RAND researchers identified challenges in the current sense-making processes and opportunities to overcome them. The effort was to focus on how sense-making occurs—where, with what, and by whom—with a particular emphasis on how information from multiple intelligence domains can be fused to find, fix, and track targets.

In this report, RAND researchers identify the most significant sense-making challenges facing the DAF and assess how AI capabilities could address these challenges. RAND researchers also provide insights for adoption through a comparative AI adoption schema and conduct a systematic examination of risk on a notional AI system, showcasing requisite considerations on how best to implement these insights. AI capabilities and DAF sense-making processes are not simple. Syncing these processes requires careful consideration of how decisionmakers will use these methods and how they are integrated into the larger intelligence cycle.

Key Findings

  • Datasets and knowledge representations need to be carefully curated. High-quality datasets must be built with care and must also be associated with the right metadata to support object-based production and subsequent algorithm development.
  • Analysts can and should anticipate AI failure modes. Understanding the limits of an AI system’s training data or knowledge representation will help analysts anticipate errors and use the AI systems more effectively.
  • Expert systems can play an important role. Older forms of AI remain relevant.
  • The DAF should pave the way for disruptive adoption. Disruptive AI will be needed later, but early adoption of nondisruptive AI can help prepare the DAF for greater change.

Recommendations

  • The DAF should follow a shared road map for developing sense-making capabilities. DAF intelligence, surveillance, and reconnaissance (ISR) wings and their U.S. Space Force counterparts should work with the DAF Chief Data and AI Officer (CDAO) to develop a set of shared priorities for AI integration into sense-making.
  • The DAF should anticipate risks early. All DAF sense-making organizations should conduct risk assessments, such as social, technological, operational, political, economic, and sustainability analysis, for AI tools they propose. The DAF Chief Information Officer should ensure responsible AI-related tasks are executed for the sense-making domain.
  • The DAF should respect tool fatigue sentiments. DAF ISR wings and Air Operations Centers should be selective in developing and adopting AI-powered tools for sense-making. They should prioritize those that fit into existing workflows and require them to lessen the demand for training support.
  • The DAF should mitigate skill atrophy. The DAF CDAO should develop a plan to mitigate potential atrophy of sense-making skills resulting from AI adoption, which could include developing datasets to help train analysts and help them recognize useful data in the wild.

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Document Details

  • Copyright: RAND Corporation
  • Availability: Available
  • Year: 2025
  • Print Format: Paperback
  • Paperback Pages: 77
  • Paperback Price: $36.00
  • Paperback ISBN/EAN: 1-9774-1499-0
  • DOI: https://doi.org/10.7249/RRA3152-1
  • Document Number: RR-A3152-1

Citation

RAND Style Manual

Warren, Kristin, Lance Menthe, Bridget R. Kane, Sydney Kessler, Mary Lee, Domenique Lumpkin, Don Snyder, Li Ang Zhang, Aleksandr Esparza Hartunian, and Shawn Konetzki, Improving Sense-Making with Artificial Intelligence, RAND Corporation, RR-A3152-1, 2025. As of April 8, 2025: https://www.rand.org/pubs/research_reports/RRA3152-1.html

Chicago Manual of Style

Warren, Kristin, Lance Menthe, Bridget R. Kane, Sydney Kessler, Mary Lee, Domenique Lumpkin, Don Snyder, Li Ang Zhang, Aleksandr Esparza Hartunian, and Shawn Konetzki, Improving Sense-Making with Artificial Intelligence. Santa Monica, CA: RAND Corporation, 2025. https://www.rand.org/pubs/research_reports/RRA3152-1.html. Also available in print form.
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This research was commissioned by the Air Force Chief Data and AI Office (SAF/CND) and conducted within the Force Modernization and Employment Program of RAND Project AIR FORCE.

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