How do Army commanders and staff integrate AI into decision-making processes and battle command systems while still enabling mission command? What risks does AI introduce into the trust between senior and subordinate commanders? How does AI improve or degrade a disciplined initiative?
- Albanese, Capt. Stephanie, "Restructuring Air Force Artificial Intelligence/Machine Learning Efforts to Enable Short & Long Term Decision Making Advantage," SOS AUAR, 2025.
- Addresses how AI integration risks degrading disciplined initiative and the traditional model of mission command. The paper notes that as AI provides real-time situational awareness and data simultaneously to all echelons of command, it may significantly diminish the need for "mission-orientated leadership". Because senior commanders will have access to the exact same information required to make rapid decisions as their forward-deployed subordinates, there is a severe risk that the philosophy of mission command will be pushed into the background and sacrificed on the "altar of speed and efficiency". Consequently, the introduction of AI threatens to bypass subordinate initiative, requiring the military to fundamentally reassess leadership concepts to ensure a proper symbiosis between humans and machines rather than relying on top-down micromanagement.
- Catt, Capt. Nathanial, "Calibrating Trust in Military AI Systems," SOS AUAR, 2021.
- Explores the risks AI introduces regarding trust and the execution of a commander's intent. The paper defines trust in military AI as a warfighter’s belief that the system will successfully help them achieve "the Commander’s intent in situations characterized by risk". The introduction of AI risks severely degrading operations if subordinates either over-trust an unreliable system (misuse) or under-trust a highly reliable system (disuse). To mitigate this risk and maintain a disciplined execution of the mission, Catt emphasizes the necessity of Explainable AI (XAI); by allowing subordinates to query why an AI system reached a specific conclusion, operators can apply their own domain expertise to rationalize the outputs, ensuring the AI's recommendations genuinely align with the senior commander's overall intent before taking lethal action.
- Corrado, Maj. Salvatore A., "Communicating in a Degraded Environment: Command and Control during Contested Operations," AFGC thesis, 2025.
- Explains how the overwhelming amount of data provided by advanced command and control systems can actively degrade disciplined initiative and erode trust between command echelons. While mission command is designed to empower subordinate decision-making, flexibility, and "disciplined initiative" to achieve the commander's intent, information-heavy C2 environments often lead senior leaders to struggle with micromanagement because they possess so much real-time battlefield data. When tactical-level commanders exercise initiative that deviates slightly from expectations, strategic-level commanders observing the data feeds feel compelled to intervene directly; this constant oversight inherently erodes mutual trust and desensitizes subordinate commanders to making decentralized decisions.
- Dalrymple, Dash, "Applying Ethics in an Artificial Intelligence Arms Race," AFGC thesis, 2024.
- Explores how AI influences mission command and disciplined initiative by examining the ethical boundaries of human-machine teaming (HMT) in the decision-making process. The author notes that as the military shifts toward dispersed operations and Mission Command, junior officers will be expected to operate autonomously and rely on commander's intent when disconnected from higher headquarters. While AI can accelerate the OODA loop, it introduces significant risks to trust and disciplined initiative because AI lacks human reasoning and cannot comprehend subjective principles like military necessity or proportionality. By delegating decisions to AI, the military risks removing the ethical "checks-and-balances" normally provided by human subordinates who can refuse an unethical order, thereby degrading disciplined initiative. To mitigate this, the paper recommends programming AI with conservative ethical frameworks and implementing formal ethical training for human operators.
- Holloway, Maj. Minnenne and Maj. Bridget Pantaleon, "Harnessing Disruption: Defense Leaders' Insights on Leading through Emerging Technologies," AF Fellows, 2024.
- This research addresses the prompt by analyzing how defense leaders integrate Artificial Intelligence Augmented Decision Making (AIADM) through the doctrinal lens of mission command. The authors assert that AI improves disciplined initiative by processing massive datasets to forecast enemy movements and recommend optimal courses of action, thereby empowering decentralized execution at the tactical edge. However, the paper warns that AI introduces significant risks to the trust between command echelons if the algorithms suffer from data bias, lack interoperability, or "drift" from their engineered functions over time. To preserve the core tenets of mission command—specifically building mutual trust, accepting prudent risk, and exercising disciplined initiative—the authors emphasize that AI must be treated as a tool to augment rather than replace human judgment. Commanders must provide clear intent and maintain strict human oversight so that subordinates do not blindly rely on machine outputs, ensuring that technological integration enhances rather than subverts accountability.
- McDonald, Lt. Col. Brough, "Mission--Go Win: Codifying Mission-Type Orders for USAF Joint All-Domain Operations," AFGC thesis, 2025.
- Addresses the integration of AI into battle command systems by examining the role of Mission-Type Orders (MTOs) within the Joint All-Domain Command and Control (JADC2) framework. The paper argues that integrating advanced AI-driven decision-support systems into operational planning can actually enhance decentralized decision-making, a core tenet of mission command. By providing a common operating picture, AI allows subordinate commanders to operate independently while maintaining shared real-time situational awareness. However, the author cautions that the effectiveness of MTOs depends on managing the psychological and cognitive demands of autonomy, noting that the tension between autonomy and centralized control in multi-domain environments requires robust frameworks to prevent AI from degrading the commander's intent and disciplined initiative.
- McLamb, Capt. Elizabeth E., "Integrating Artificial Intelligence to Joint All-Domain Command and Control for the 2030 Fight," SOS AUAR, 2020.
- McLamb explores how integrating AI into battle command systems—specifically JADC2 and the Advanced Battle Management System (ABMS)—requires a cultural shift toward decentralized mission command to be effective. The paper explains that AI improves disciplined initiative by utilizing machine learning to rapidly generate and evaluate thousands of courses of action, allowing subordinate battle managers to seize the operational advantage faster than human capacity alone. However, to avoid degrading this initiative, the military must resist the temptation of centralized control that advanced networks provide; instead, senior commanders must explicitly delegate authorities and empower operators to execute "mission type orders". The author highlights that trust is maintained when operators are rigorously educated on the biases, data gaps, and vulnerabilities of AI algorithms, ensuring that subordinates can safely identify and accept risk at the lowest echelons while operating "on-the-loop" to fulfill the commander's intent.
- McLaughlin, Lt. Col. Patrick B., "Mission Command and Contested Logistics: Navigating the Agility vs. Control Dilemma in Great Power Competition," AF Fellows (Fletcher), 2025.
- Evaluates the integration of AI into battle command systems by warning against using AI as a technological crutch that bypasses the human elements of mission command. The paper argues that while new command and control doctrines seek to leverage AI/ML through JADC2 to expedite decision-making and manage the volatility, uncertainty, complexity, and ambiguity (VUCA) of modern conflict, this approach risks creating a "commitment-resource gap". The author asserts that AI cannot replace the deliberate, slower process of building a mission command culture centered on trust, experience, and disciplined initiative. To truly improve disciplined initiative, the military must not solely rely on AI dashboards but must empower tactical commanders and lower-ranking personnel to independently employ distributed control, deliberately injecting chaos into training to build mutual trust between echelons.
- North, Matthew, "Can Machines Have Ethics?" AFGC thesis, 2025.
- North answers the prompt by examining the Army’s integration of AI into tactical battle command systems, specifically highlighting the Tactical Intelligence Targeting Access Node (TITAN) as a prime example of an AI ground station filtering massive datasets to notify soldiers of critical targets. The paper argues that while AI drastically improves decision-making speed, it introduces severe risks to disciplined initiative and moral trust if soldiers default to accepting algorithmic recommendations without applying human judgment. Because AI lacks the capacity to weigh ethical nuances—such as collateral damage risks, historical significance, or the potential for conflict escalation—commanders cannot entrust AI with free rein over military targeting systems. To ensure mission command remains intact and legally compliant, North concludes that commanders must mandate human oversight in the targeting chain and rigorously train soldiers to critically evaluate AI outputs, ensuring that algorithmic bias does not degrade the ethical standards and accountability expected of human operators.
- Sletten, Maj. Wayne T., "Maximizing AI's Potential: Strategies for Swift CJADC2 Integration by US Military Commanders," AFGC thesis, 2024.
- Analyzes how the U.S. military intends to integrate AI within the CJADC2 architecture to reduce human workloads and expedite decision-making. The paper notes that while AI manages the "sense" and "make sense" portions by analyzing massive data pools, the "act" phase still relies heavily on mission command at all levels of operation. AI introduces risks to trust and accountability between commanders primarily through the "Black Box Problem," wherein the opacity of AI algorithms prevents human operators from understanding how the system turned input data into a strategic recommendation. To maintain trust and disciplined initiative, the paper emphasizes that military leaders must undergo specialized AI training to understand the intersection of AI capabilities and operations, thereby building a foundational trust in the system prior to operational deployment.
- Steele, Lt. Col. Eric D., "Aerial Robotic Swarms and Joint All-Domain Command and Control," AWC SSP, 2020.
- Answers how commanders can integrate AI into battle command systems while still enabling mission command by delegating authorities through advanced algorithms. Steele suggests that in complex scenarios involving AI technologies like aerial robotic swarms, commanders can execute mission command by issuing mission-type orders in the form of "pre-programmed mission profiles or AI-enhanced algorithms". AI systems can rapidly fuse data across domains to develop tactical recommendations in seconds, allowing the senior commander to efficiently allocate assets and issue these digital mission-type orders. Once deployed, the AI-enabled systems allow tactical commanders to use decentralized authority to maneuver their forces and act upon the commander's intent independently, neutralizing targets without requiring constant communications or direction from higher echelons
- Taylor, LTC Patrick, "Leadership, Trust and the Changing Character of War," AWC RTF, 2025.
- Answers the query by exploring how trust—the foundation of mission command and disciplined initiative—is challenged by the introduction of AI-enabled platforms. The author notes that because AI technology is highly complex and lacks transparency, service members struggle to understand how the machine makes decisions, leading to a breakdown in "human-machine teaming" trust. The paper highlights that this opacity introduces significant risks: having too little trust leads to the underutilization of AI, while too much trust in untested systems can lead to overreliance, friendly fire, or unintended escalation. To maintain disciplined initiative, the author argues that the military must implement systems engineering approaches to "build trust into the system" allowing operators to "trust but verify," while also providing specific education on human-machine trust before employment.
- Yoon, Capt. Esther, "JADC2 Will Not Win the Conflict: Rethinking C2 in a China-Taiwan Scenario," SOS AUAR 2021.
- Argues that the current pursuit of AI-enabled, all-domain networks threatens to degrade disciplined initiative by reinforcing a rigidly hierarchical, top-down decision-making structure. Yoon observes that advanced C2 systems often strive to link all platforms directly to a "centralized operation center," which diffuses responsibility among large staffs and makes individuals highly risk-averse, thereby discouraging independent tactical action. To properly integrate AI and technology while enabling mission command, Yoon asserts that the military must stop consolidating management and instead jointly structure its forces to operate semi-autonomously using mission-type orders in distributed environments.