Executive Summary
Humans will always make final decisions on what to shoot and what not to shoot and when to shoot. But advanced AI tools can turn processes that used to take hours and sometimes even days into seconds.– Admiral Brad Cooper, on X (formerly Twitter), March 11, 20261
Several years ago, the U.S. Army’s XVIII Airborne Corps demonstrated the power of Maven Smart System (MSS) to accelerate targeting processes, accomplishing with a 20-person team what in 2003 had required a team of 2,000.2 Since then, the U.S. military has embraced AI-enabled decision support systems (AI-DSS) such as MSS at Combatant Command headquarters around the globe. Media reports indicate that MSS is a key enabler of the sustained high-tempo targeting process displayed in Operation Epic Fury.3
The military applications of AI-DSS—software systems that use a variety of AI tools to ingest, generate, share, and act upon information—can help with much more than just targeting processes. Nearly any problem that deals with large volumes of disparate kinds of data as part of a decision process that requires interpreting that data, assessing the situation, making decisions, and implementing those decisions (and often repeats those steps in a regular cycle) can benefit from AI-DSS tools.
Public discussions of AI-DSS have relied on mostly vague notions of how these systems might be applied in military processes. We address this gap here with two case studies of operational-level military processes where the application of an appropriate AI-DSS tool could bring improvements on par with what XVIII Airborne Corps achieved for the fires process. One case study examines applying AI-DSS to the resupply of ammunition to Army fires units; the other focuses on applying AI-DSS to the processes that drive the Joint Air Tasking Cycle (JATC). In both cases, AI-DSS could enable these processes to be performed faster, more flexibly, and with less manpower while maintaining quality and human judgement. In the case of the JATC, AI-DSS offers the opportunity to revisit the decades-old 72-hour timeline for deliberate planning of daily air sorties. Both potential applications come with new risks that must be addressed, even as old risks are retired.
These case studies reveal that the primary obstacle to implementing suitable AI-DSS capabilities in these cases is not software development, but rather accessing the data needed by such tools. Much of the required data is only available at individual units, often in unstructured formats such as PowerPoint slides or unit-unique spreadsheets. Overcoming the bureaucratic hurdles to make such data digitally available to AI-DSS needs to be the first priority in any scaling plan.
Recommendations from these case studies are:
- Make needed data digitally available to AI-DSS. The top-level departmental support for efforts to break through any bureaucratic or cultural barriers has never been stronger—now is the time to act. The Chief Digital and Artificial Intelligence Office (CDAO) has been given authority and tasking on this issue by Secretary Hegseth’s artificial intelligence memorandum of January 9, 2026.4
- Examine the best practices that the XVIII Airborne Corps and others are using—including their DevSecOps-style development approach combining operators and software engineers and adopting/adapting them. There is no need to reinvent this wheel. CDAO should collect and promulgate these lessons. CDAO should also resource data and evaluation support teams at the Combatant Commands to augment their AI-DSS development and deployment efforts at the edge. These teams should be staffed with personnel who have the necessary expertise and/or experience to bring to bear the lessons learned and best practices acquired across industry, academia, and the joint force.
- Deploy these capabilities to command centers during real-world operations to develop warfighting best practices, accelerate the growth of a community of experienced users, and provide operational feedback to software engineers.
As the case studies presented here suggest, these steps would improve some of our most important Army and joint fires-related processes and could responsibly accelerate the development and adoption of AI-enabled capabilities across the U.S. military.
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Beyond Targeting- U.S. Central Command (@CENTCOM), “Update from CENTCOM Commander on Operation Epic Fury,” X (formerly Twitter), March 11, 2026, https://x.com/CENTCOM/status/2031700131687379148?s=20.
- Emelia Probasco, “Building the Tech Coalition” (Center for Security and Emerging Technology, August 2024), https://cset.georgetown.edu/publication/building-the-tech-coalition/.
- Admiral Brad Cooper, “US Strikes 5,500 Targets in Iran | Operation Epic Fury Update,” Defense Now, YouTube, March 11, 2026, https://www.youtube.com/watch?v=5l-05hAtkWM.
- Secretary Pete Hegseth, “Accelerating America’s Military AI Dominance,” (Secretary of War, January 9, 2026), https://media.defense.gov/2026/Jan/12/2003855671/-1/-1/0/ARTIFICIAL-INTELLIGENCE-STRATEGY-FOR-THE-DEPARTMENT-OF-WAR.PDF.