Artificial Intelligence in Warplans and Contemporary AOC Capabilities

As the military seeks to modernize its command and control nodes and operational planning processes, the integration of emerging technologies is critical. Specifically, what off-the-shelf Artificial Intelligence capability could be quickly incorporated into the Air Operations Center (AOC)? Furthermore, looking at the broader planning ecosystem, what is the impact of artificial intelligence or intelligent automation in the development of real-time generated war plans?

• AU Library Libguide - Military Applications of Artificial Intelligence
   
• Beckett, Lt. Col. Gary P., "Leveraging Artificial Intelligence and Automatic Target Recognition to Accelerate Deliberate Targeting," AWC PSP, 2020, 25 pgs.
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  • Keith addresses this by analyzing how automated decision-making agents in Cold War computer simulations, such as TEMPER and RSAS, struggled to seamlessly align with actual national security objectives. He argues that while artificial intelligence possesses the potential to drastically increase the speed and quality of strategic force structure and employment decisions, introducing intelligent automation into war planning poses severe risks of misalignment, complexity, and opacity. By evaluating historical models, he demonstrates that when AI agents generate plans or strategies, government users and sponsors frequently lack the ability to interpret, control, or understand the underlying decision-making algorithms, ultimately leading to situations where the machine's simulated war aims fundamentally diverge from human strategic intent.
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• Ball, Justin M., "Artificial Intelligence: Implications for the Cyberspace Domain," AF Global College, 2025, 40 pgs. 
• Bojanić, Maj. Oliver, "Advancing Tactical Command and Control: A Comparative Case Study Analysis in Harnessing Artificial Intelligence for Enhanced Efficiency and Optimization," GCPME thesis, 2023, 71 pgs. 
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• Coble, Capt. Krysta, "Artificial Intelligence-Empowered Pathways: Leveraging AI to Establish an Immersive Learning Framework for Rapid Training of Offensive Cyberspace Operators," SOS AUAR, 2021, 14 pgs. 
• ​Courtoy, Capt. Jason C., "Three Things Leaders Need to Know before Investing in Artificial Intelligence," SOS AUAR, 2020, 9 pgs.
   
• Daoud, Maj. Randi, "Algorithms in the Armed Forces: Emerging Technologies and Their Impact on the Battlefield," GCPME thesis, 2024, 34 pgs. 
• Donoho, Maj. Rachel, "Connecting Everything to the Network Won't Make Warfighting Easier," AF Fellows paper (Idaho National Laboratory), 2024, 3 pgs. 
• Garey, Brian M., "AI, The Race to Global Power: Exploring How AI Will Influence Air Warfare," AFGC thesis, 2024, 36 pgs. 
• Goss, Michelle, "Artificial Intelligence and Government Procurement: Preparing Contracting Officers for AI-Powered Acquisitions," Air Force Global College thesis, 2025, 50 pgs.  
• Harding, Emily, Col. Matthew Strohmeyer and Mackenzie Richardson, "From Data to Insight: Making Sense out of Data Collected in the Gray Zone," CSIS brief, AFF paper, 2021, 17 pgs. 
• Hagardt, Lt. Col. Benjamin, "Artificial Intelligence and Agile Combat Employment," GCPME thesis, subsequently published in Military Review (May-June 2024).  Original paper.
   
• Kerrigan, Maj. James, "From Theoretical to Practical: Moving Artificial Intelligence (AI) into the Warfighter's Toolkit," GCPME thesis, 2024, 55 pgs. 
• Ogle, Lt. Col. Kevin M., "Autonomous Air Operations Center Continuation Training," AWC PSP, 2020, 23 pgs.
• Place, Maj. Lee D., "There's an App for That:  Harnessing the Full Potential of the Electronic Flight Bag," GCPME thesis, 2020, 41 pgs.
• Pridotkas, Jake, "Enabling Squadron Level Artificial Intelligence: Getting Started with AI in 2021," SOS AUAR, 2021, 7 pgs. 
• Rogers, Capt. Ilya K., "Data Annotation with DBSCAN and GMM Unsupervised Clustering using Flow Cytometry Slides," SOS AUAR 2021, 9 pgs. 
• Wilcoxon, Chloe, "BBP On Artificial Intelligence Aided Electronic Warfare," SOS AUAR, 2023, 5 pgs. 
• Wofford, Maj. Sara, "Enhancing Joint Operations and Training through Large Language Models," ACSC PACAF Research, 2024, 19 pgs. 
• Yae, Capt. Jung H., "BBP on Accelerating mission Data Reprogramming Process Using Machine Learning," SOS AUAR, 2024, 2 pgs.