Judgment Day: Human Leadership is AI’s Greatest Check
By Master Sgt. Raymond T. Fain
Sergeants Major Course
March 6, 2026
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Artificial intelligence (AI) and machine learning (ML) are reshaping how the Army fights, trains, and sustains itself. These technologies already support information retrieval, pattern detection, and predictive analysis across commercial and government sectors.
The Army continues to field AI enabled tools such as CamoGPT, an Army trained language model that provides Soldiers with secure, mission focused information across Non-classified Internet Protocol Router Network (NIPRNet non-sensitive data) and Secret Internet Protocol Router Network (SIPRNet sensitive data) (Department of the Army, 2024a).
As these capabilities mature, they will influence every warfighting function and reshape expectations placed on leaders at all echelons. Integrating AI and ML into Army operations will transform NCO role, positioning them as critical leaders who interpret machine outputs, advise commanders, and ensure that AI enabled insights complement doctrine and operational judgment.
Interpreting AI and ML Outputs
AI enabled systems process operational data faster than any staff section. They identify trends, forecast outcomes, and highlight anomalies that staff might otherwise spend hours detecting. Yet algorithms cannot grasp a commander’s intent, context, or second and third order effects.
NCOs bridge this gap by interpreting outputs, assessing operational relevance, and ensuring those insights guide tactical and operational decisions (Department of the Army, 2024b).
For example, ML forecasting models may detect surges in ammunition consumption, spikes in equipment failures, or shifts in task organization requirements. Algorithms can flag these trends, but NCOs determine whether they represent genuine operational issues, data anomalies, or mission changes.
To perform this role effectively, NCOs study how AI models operate. They do not need coding expertise; instead, they apply concepts such as confidence intervals, model accuracy, and environmental factors that influence predictions. An NCO who understands why a model produced a specific output can evaluate its reliability and relevance to the mission variables, which include mission, enemy, terrain and weather, troops and support available, time available, and civilian considerations (METT TC) outlined in Field Manual (FM) 5 0, Planning Orders and Production (Department of the Army, 2024b).
Explainable AI tools, such as SHapley Additive exPlanations (SHAP), a game-theoretic approach used to explain machine learning model output by quantifying the contribution of each feature to a prediction, highlight which variables drive predictions (Lundberg & Lee, 2017).
By mastering these skills, NCOs transform AI outputs into operationally useful insights rather than abstract data products. This interpretive responsibility positions NCOs to advise commanders on applying AI enabled insights while ensuring recommendations remain consistent with doctrine and operational judgment (Department of the Army, 2019).
Advising Commanders with AI-Enabled Insights
Commanders depend on NCOs to deliver assessments grounded in experience, discipline, and doctrine. As AI tools become routine across warfighting functions, NCOs expand this advisory role by translating machine generated outputs into clear, mission relevant recommendations.
AI models may forecast outcomes, but commanders make the decisions.
NCOs add value by comparing AI recommendations against current conditions, the mission variables, and the commander’s desired end state (Department of the Army, 2024b).
For example, an AI system may suggest a course of action based on sensor data and historical patterns, but NCOs identify factors the system cannot account for, such as terrain restrictions, morale issues, or degraded communications.
AI accelerates commanders’ decision cycle, but NCOs ensure that speed does not replace judgment. They communicate whether a model’s recommendation supports or conflicts with doctrinal principles in Army Doctrine Publication (ADP) 6 0, Mission Command: Command and Control of Army Forces (Department of the Army, 2019), whether the data remains current, and whether the recommendation carries risks commanders must weigh before acting.
NCOs also safeguard truth in reporting. AI models depend on clean, accurate data, and when reporting breaks down, predictions degrade. NCOs enforce standards in data entry, maintenance reporting, training records, and readiness updates, consistent with FM 6 0, Commander and Staff Organization and Operations, guidance on staff processes (Department of the Army, 2022). This stewardship protects the quality of future AI outputs.
Through these actions, NCOs strengthen commander trust in AI enabled information. That trust forms the foundation for the next responsibility: ensuring AI integration aligns with Army Values, doctrine, and ethical decision-making.
Ensuring Ethical and Doctrinal Alignment
AI supports decision-making, but the Army fights based on doctrine, values, and disciplined initiative. NCOs ensure that AI enabled tools reinforce, not replace, these foundations.
Leaders must understand how technology shapes operations and apply ethical reasoning when AI recommendations influence decisions. Because AI systems can amplify bad data, reinforce bias, or generate outputs that contradict the commander’s intent, NCOs guard against these risks by scrutinizing recommendations through the lens of doctrine and the Army Ethic (Department of the Army, 2019).
They also ensure systems comply with the Department of Defense’s Ethical Principles for Artificial Intelligence, which emphasize responsibility, equity, reliability, and governability (Department of Defense, 2020).
NCOs teach Soldiers how to use AI tools correctly, guide them to apply professional judgment, and help them recognize the limits of automated systems. They check whether the data remains current, whether the model reflects the operational environment, and whether recommendations align with rules of engagement and outlined mission requirements (Department of the Army, 2019).
Conclusion
AI and ML reshape how the Army makes decisions, allocates resources, and fights across multi domain operations. These tools accelerate analysis, improve forecasting, and strengthen situational awareness.
Their real value, however, depends on the NCO corps’ leadership, judgment, and discipline. Army operations will transform only when NCOs interpret machine outputs with precision, advise commanders with doctrinal clarity, and safeguard ethics during implementation.
When NCOs fulfill these responsibilities, AI becomes a force multiplier that strengthens — not replaces — human decision-making.
As the Army fields new AI enabled tools, NCOs must lead the integration. Their leadership determines how units build trust in AI outputs, sustain readiness, and adapt to contested environments.
The NCO Corps remains the backbone of the Army, and AI and ML will reinforce that truth only when NCOs drive the transformation with discipline, expertise, and purpose.
References
Department of the Army. (2019). Mission command (ADP 6-0). https://armypubs.army.mil/epubs/DR_pubs/DR_a/ARN34403-ADP_6-0-000-WEB-3.pdf
Department of Defense. (2020). DoD data strategy. https://dodcio.defense.gov/Data/
Department of the Army. (2022). Commander and staff organization and operations (FM 6-0). https://rdl.train.army.mil/catalog-ws/view/100.ATSC/2DDE6089-23E5-4345-8E9E-7BCD5BDF45C8-1399555122246/fm6_0.pdf
Department of the Army. (2024a). CamoGPT account access instructions. https://cybercoe.army.mil/Portals/131/Documents/Instructions%20for%20Obtaining%20a%20CamoGPT%20Account%20and%20access%20to%20CCoE%20Workspace%20plus%20FAQs.pdf
Department of the Army. (2024b). Planning and orders production (FM 5-0). https://armypubs.army.mil/epubs/DR_pubs/DR_a/ARN44590-FM_5-0-001-WEB-3.pdf
Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30. https://proceedings.neurips.cc/paper_files/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf
Master Sgt. Raymond T. Fain is a student at the Sergeants Major Academy (SGM-A), Fort Bliss, Texas. His research work is in artificial intelligence and machine learning, applied to complex operational environments. He hopes to bring his leadership experience as a senior leader in the Army into a technical position to effect change and create effective AI/ML solutions. Fain earned a Bachelor of Science degree in information technology, with a focus on programming.
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