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Dec. 17, 2025

Mission Command’s Asymmetric Advantage Through AI-Driven Data Management

Sorin Adam Matei and Kyle Parris Reed
©2025 Sorin Adam Matei and Kyle Parris Reed

ABSTRACT: Artificial intelligence can optimize mission command by condensing multisource field data that ascends the decision chain while distilling concise, decision-quality guidance to the tactical edge. Diverging from existing publications, this article positions information asymmetry as a defining pillar of mission command rather than a limitation. This article presents a condensation-distillation framework that manages complexity through data condensation, AI-driven distillation, and conceptual metrics to assess asymmetric information flows. Drawing on military doctrine, algorithmic-warfare literature, and current modernization programs, military practitioners will engage with a systems-thinking perspective, revealing how AI-enabled command and control can enhance decision clarity and reinforce the intent of mission command.

Keywords: information asymmetry, mission command, command and control, artificial intelligence, data management

 

Night falls east of Bakhmut as Captain Mykola Shevchenko, brigade-level intelligence officer, studies a clean tactical display from his forward position with no clutter, just enemy markers with threat levels and confidence scores. Behind the interface, Ukraine’s Vezha and Avengers platform, a battlefield analysis system, has already processed terabytes of drone footage and tagged thousands of targets earlier that day. Using computer vision and past engagement data, the Vezha and Avengers suite filters threats and automatically updates to reflect potential targets. At higher echelons, analysts leveraging the suite to detect a broader pattern as enemy vehicles massing in ravines signal a likely flank. This AI-derived insight, shaped through reporting, real-time imagery, and time-series overlays, informs brigade-level adjustments.1

But for Shevchenko, only one alert matters: A suspected armored column halts three kilometers north. Intermittent heat signatures confirm dismounts. The AI suggests this information may indicate the presence of an artillery staging point based on logistical patterns. Without needing full feeds or layered reports, Shevchenko requests a strike through the targeting module within the DELTA combat system. Moments later, the ravine flashes white. At headquarters, analysts continue parsing the full battlespace. But Shevchenko needs only mission-critical information in the moment. This deliberate asymmetry, where AI manages volume and delivers tailored insight, embodies modern mission command: precise, responsive, and unburdened by data overload.

The advent of AI in military affairs heralds profound enhancements of war-fighting capacity, with mission command poised to reap significant benefits. By design, mission command enables subordinate leaders to execute operations in a decentralized manner, guided by their respective commanders’ strategic intent. This command methodology fosters agility and responsiveness, which are essential in fluid battlespaces. Since mission command demands quick responses in conditions of uncertainty, information management is vital in this context. Drawing on their work for the FORCES Initiative at Purdue University, Bradford Witt and Sorin Adam Matei observe, in a theoretical model dedicated to mission-command requirements, that managing information asymmetry within command and control is central to mission command.2 AI-driven systems provide a pathway toward superior information handling within mission command. Thoughtfully integrated, AI can reshape decision making at every echelon by concentrating the vast amount of information that suffuses any modern battlefield while preserving the autonomy of frontline commanders. This article examines the intersection of AI and mission command and (more broadly) the role of AI within command and control. The technology can manage complexity through data condensation and distillation, and by using conceptual metrics to assess asymmetric information flows. This condensation-distillation framework’s ability to significantly optimize information asymmetry enhances mission command, helping secure a sustainable strategic advantage.

Foundations of Mission Command

Current US military doctrine positions mission command squarely on a foundation of agile problem-solving and sound professional judgment. As articulated in Army Doctrine Publication (ADP) 6-0, mission command constitutes “the Army’s approach to command and control that empowers subordinate decision making and decentralized execution appropriate to the situation.”3 Notably, higher command’s intent is the mechanism that establishes guardrails for the agility of decentralized decision making, thus sustaining the necessary synchronization multidomain operations demand.

Although mission command necessitates synchronization, it diverges from more centralized models, predicated on three interrelated pillars. First, mission command presupposes the highest standards of unit readiness and proficiency. Second, mission command demands all decisionmakers, from squad leaders to senior officers, share a common ethos and tactical frameworks. As ADP 6-0 emphasizes, effective mission command “requires tactically and technically competent commanders, staffs, and subordinates operating in an environment of mutual trust and shared understanding.”4 Under this paradigm, any successive leader must immediately apprehend the mission’s purpose and chart the subsequent course of action without disruption.

Last, the essence of mission command transcends mere preparedness: It resides in deliberately structured information asymmetry. Witt and Matei highlight that optimal command and control hinges on uneven information density or abundant, detailed data flowing upward from the field, contrasted with concise, intent-focused directives flowing downward.5 Only by sustaining this calibrated imbalance can mission command fully realize its promise of agility and responsiveness in warfare.

Information Asymmetry in Practice

Information asymmetry as a concept typically refers to the unequal distribution of or access to information. The presence of information asymmetry is often seen as a disadvantage. Naturally, one assumes everyone with the same or abundant exposure to information will produce better results. But Martin J. Eppler and Jeanne Mengis, researchers in organizational communication at the Università della Svizzera italiana, highlight the quality of individual and group decisions or reasoning correlates curvilinearly with the amount of information individuals or groups receive.6 Receiving more information increases the quality of the decision to a certain point, after which receiving more information decreases the quality of decision making. Accordingly, information asymmetry should be viewed from a new perspective. Does a given actor need or want to present all available information to a decisionmaker? Or would selectively curating information relevant to the decisionmaker enable better outcomes? Information asymmetry, as this article suggests, is the advantage needed to accelerate decision making and enable more effective kill chains. This concept could prove its ability to mitigate the effects of information overload.

Within the paradigm of mission command, field units will naturally generate a deluge of data that ascend the decision chain. At the same time, the feedback returned to tactical formations must remain timely and selectively curated. Despite its critical importance, this information economy has been governed by ingrained practice rather than codified doctrine and has lacked sufficient governance by specialized information-management tools. The rise of the digital battlefield and Joint All-Domain Command and Control necessitates the formal alignment of mission command’s information needs with emerging technologies.

The US Army’s military decision-making process is a form of organized information asymmetry that orchestrates data to enable effective decision making. The process begins with the receipt of a mission from higher headquarters, which leads to mission analysis, where critical information is filtered to highlight relevant threats for subordinates’ immediate planning needs. As planners develop and analyze multiple courses of action, they can request and digest curated information feeds, allowing for more focused and refined outputs. As the process progresses and leads to the production of orders, only essential tasks and instructions are communicated, minimizing cognitive overload and expediting operational execution.

Despite the military decision-making process’s asymmetric intent, the process often suffers from a lack of intelligent information-management tools that can effectively compose and present data. Manual data collection and filtering drains staff members’ time, disparate systems create stovepipes that delay information sharing, and static briefs struggle to keep pace with dynamic battlefield conditions. Planners manually reconcile and reformat numerous spreadsheets and documents, increasing the risk of human error and data latency. For example, by not using AI / machine learning (ML) to identify emerging patterns and deliver real-time updates faster, the military decision-making process struggles to cope with the sheer volume and velocity of data.

Regardless of echelon, data heterogeneity and a lack of optimized asymmetry affect commanders and their staff members who contend with a formidable array of information requirements: tracking friendly and adversarial dispositions, evaluating environmental factors, and orchestrating intricate planning cycles. Commanders must employ information-management systems capable of ingesting vast datasets, prioritizing by relevance, and presenting actionable intelligence, which Anthony King, chair of war studies at the University of Warwick in the United Kingdom, highlights in his study of modern divisional headquarters.7 These capabilities are indispensable to maintaining situational awareness and making judicious, timely decisions.

As the Army transitions to multidomain operations, cultivating optimal information asymmetry becomes increasingly vital. As ADP 3-0 underscores, commanders at every level rely on the command-and-control war-fighting function and its enabling systems to synchronize and converge combat power effectively.8 Given the time-sensitive nature of synchronization and convergence, command-and-control systems must implement AI and ML tools thoughtfully. The Department of Defense further affirmed this trajectory in the Joint All-Domain Command and Control strategy, advocating for accelerating a commander’s observe, orient, decide, act (OODA) cycle relative to an adversary.9 Deliberately configured information asymmetry inherently supports the department’s objective.

Artificial Intelligence–Enhanced Mission Command

Built on these doctrinal foundations, mission command is currently bound for meaningful integration with AI on current and future battlefields. The AI technology enables the cultivation of optimal information flows through intelligent detection, storage, fusion, and decision support. If implemented thoughtfully, this workflow produces curated awareness, accelerates adaptive decision-making cycles, and further empowers decentralized commanders while maintaining strategic intent. Ferenc Fazekas, a professor of military strategy at the Ludovika University of Public Service, argues that the current technological race will enable both sides to wage multidomain, accelerated warfare, in which well-implemented, AI-enabled systems could help create operational advantages and preserve the initiative where traditional decision-making models would falter.10

But recognizing AI’s potential is not the same as using it effectively. Expanding sensor networks and AI-driven analytics can deliver unprecedented battlefield visibility to analysts and place this visibility directly into the hands of senior commanders. That very transparency can tempt leaders to hover over every tactical decision, risking the erosion of mission command’s decentralized agility in favor of micromanagement, especially as adversaries intensify their AI-driven warfare strategies.11 Robert J. Sparrow and Adam Henschke also caution in their Parameters article regarding manned and unmanned teaming, automation bias—the tendency of people to overtrust AI (especially if AI has proven itself to be reliable)—could invert traditional command structures, effectively placing AI systems in the de facto role of commanding human teams.12 All these considerations are valid, yet the intent is not to bolt AI on as an afterthought, surrender all control to algorithms, or allow a slow creep toward centralized command. Instead, the goal is to fine-tune AI’s analytical capabilities to create information advantages at every level and sustain decentralized initiative through careful operational integration. A key feature of AI-enabled systems is the ability to collect, discern, and impart the right amount of information for each situation asymmetrically. More information should be collected from the field than is sent back.

Imagine computer vision enabling real-time terrain analysis for platoon leaders, agentic AI refining options for brigade staff, and predictive analytics tools guiding theater-level logistics. Each echelon could more easily digest and exploit information tailored to its function. This deliberate calibration would foster resilient decision-making ecosystems where decentralized actors excel within their spheres, unencumbered by the burden of managing overwhelming data streams and redundant reporting.

In addition, substantial evidence indicates that AI-enabled command and control has the potential to illuminate friction points, identify enemy dispositions, propose optimal firing solutions, and provide timely decision support. This technological framework is more than a tool set; it becomes a digital extension of a commander’s staff, accelerating and enriching decision making across the battlespace.

Current Initiatives in Command and Control

The rapidly maturing technology of AI-enabled command and control is not absent from the US Army’s and its allies’ arsenals. Increased experimentation with AI is laying the groundwork for its integration into command and control through a suite of innovative initiatives. These efforts highlight how AI and intelligent data management can shape ideal information flows and empower decentralized decision making across every echelon by refining vast, complex data streams into timely, actionable insights. But because information asymmetry remains an implicit effect rather than a defined objective with measurable benchmarks, each program still holds room for enhancement, implementation, and increased adoption.

Next Generation Command and Control

Next Generation Command and Control (NGC2) is the Army’s premier modernization effort, designed to reimagine command and control through a modular, open ecosystem that unites hardware, software, and applications atop a shared, integrated data layer. The NGC2 effort’s design breaks down stovepiped systems, forging a unified environment in which all war-fighting functions converge into a fluid, accessible stream of information.13 Instead of conducting cumbersome, manual transfers between disjointed systems, NGC2 organizes immense information flows into a coherent operational picture. Project Convergence Capstone 5 showed promise, as NGC2 boosted communication speeds, accelerated the operational tempo, and sharpened the quality of decision making.14 The true innovation of NGC2 lies in its ability to structure information and collapse disparate inputs into a harmonized data environment. This command-and-control architecture ensures commanders across the force, whether vertically or laterally positioned, gain immediate access to the insights most relevant to their mission sets.

Maven Smart System

The Maven Smart System (MSS), refined through XVIII Airborne Corps’ Scarlet Dragon exercises, showcases AI’s transformational potential, particularly in targeting. As publicly reported, processing satellite imagery for strike recommendations once took 12 hours and the task now takes less than a minute.15 This improvement is thanks to rapid-fire iterations of software development. MSS enables this speed by employing machine-assisted object detection within a technology known as Broad Area Surveillance-Targeting. “[Broad Area Surveillance-Targeting] algorithms fuse data from multiple sensors and platforms to bring analysts and operators a priority based, in-depth assessment of the enemy systems present within the Commander’s Area of Responsibility (AOR).”16 MSS significantly streamlines the targeting process by automating the flow of data, from image analysis to decision making and poststrike assessment, reducing errors and manual inefficiencies.17

Army Intelligence Data Platform

The Army Intelligence Data Platform (AIDP) is a shared analytic environment that provides a platform through which to transform battlefield data from raw inputs into relationally structured intelligence, fueling threat detection, mission planning, and rapid discovery. As publicly reported, the platform’s deployment across prioritized theaters within just one year signals its growing indispensability in gaining an information edge.18

AIDP is built to evolve with future big-data analytics and AI/ML iterations. But AIDP’s current automation capabilities help maintain a comprehensive operational picture by continuously gathering and processing data to enable more responsive information handling during multidomain operations.19

Ukraine’s DELTA System

Although US capabilities continue to mature, Ukraine’s DELTA system provides compelling evidence of operational, AI-enabled command and control, with proven interoperability with US and NATO systems. The DELTA system has evolved from a simple digital map into a comprehensive, cloud-based ecosystem serving the entire Ukrainian military.20 The system’s AI capabilities (such as its Avengers application) automatically detect enemy equipment by analyzing thousands of simultaneous video streams, while the Vezha platform processes footage from unmanned aerial vehicles, allowing analysts to identify and classify numerous reconnaissance objects daily.21 These capabilities parallel MSS’s targeting efficiency but operate at an operational scale across an active front. The DELTA system’s most significant validation came during NATO’s Coalition Warrior Interoperability eXploration, eXperimentation, eXamination, eXercise in July 2024, where the system successfully demonstrated compatibility with 15 different command-and-control systems from 10 countries.22 As NGC2, MSS, and AIDP continue being developed, the DELTA system offers tangible evidence integrated, AI-enabled, multidomain command-and-control systems can deliver decisive advantages while maintaining real-time coordination with allies and partners. This interoperability demonstrates the AI-enabled, asymmetric mission-command approach presented in this article may be an excellent solution for fusing heterogeneous data streams generated through joint and combined operations with allies.

Together, these systems represent more than incremental upgrades. These systems mark a strategic evolution toward AI-powered mission command, where data are refined into operational knowledge. These initiatives cultivate the decision advantage needed in today’s information-saturated conflicts by compressing decision timelines and synchronizing efforts across modern battlefields. But optimal information asymmetry has yet to be emphasized doctrinally and achieved technically. AI/ML workflows can be configured to embody a deliberate process of data condensation and distillation, further establishing asymmetric information flows as the gold standard of mission command.

The Condensation-Distillation Framework for Mission Command

Traditional command structures, designed for simpler information environments, often buckle under the weight of excessive data, leading to decision paralysis, delays, and overdependence on higher headquarters. This situation arises in part because command posts typically harness a small fraction of the data they consume meaningfully. Staff members have the means to track logistical and operational needs in near real time. But much of that data is poorly captured and is not often fed into efficient workflows to advance operations most effectively. By contrast, AI-enhanced mission command offers a way forward through a condensation-distillation framework. This model offers a deliberate and structured approach to creating an information advantage across multidomain operations, while remaining true to the core principles of mission command.

Condensation: Structuring Raw Data for an Operational Edge

Condensation in the context of this article simply means converting high-volume, unstructured datasets into structured, relational representations that feed and power real-time decision support. Condensation is how data become operationally useful. In modern-day AI workflows, condensation is not optional. Condensation is essential for the speed of analysis to provide expedient and meaningful insights.23

Modern database infrastructure can facilitate faster insights by identifying relationships, patterns, and semantic meaning based on unstructured data.24 Graph and vector databases are examples of this modern infrastructure. Graph databases facilitate the mapping of dynamic relationships between people, places, events, and resources, which can aid in surfacing hidden enemy networks and managing logistics, for example.25 Graph databases offer profound potential for analytical work, especially within the intelligence war-fighting function. Vector databases can complement graph-based systems by facilitating the semantic linking of unstructured data across various media formats. These links create contextualized embeddings, which enable more profound discovery than keyword matching. Together, these technologies can generate a double-condensation effect, accelerating the transition from raw data to situational understanding, which is essential in supporting decisionmakers. Current and emerging computer-vision techniques also play a significant role, particularly in the vast amount of imagery collected by unmanned aerial vehicles and satellites. Techniques such as semantic segmentation and automated labeling enable the extraction of critical battlefield features—including roads, waterways, and structures—without requiring human intervention.26 MSS is a striking example. By embedding computer vision into its targeting loop, MSS slashes the time required to analyze imagery from hours to mere minutes, directly boosting the speed and initiative on which mission command depends.27

But condensation alone does not close the loop. Structured data still must be transformed into mission-relevant insight. This step is where distillation comes in, refining condensed inputs into decision-grade outputs. Intelligent agents or software that autonomously perform tasks previously handled by humans can use condensed data to offer operational recommendations, analyze courses of action, or forecast logistical constraints.28

This condensation-distillation pipeline aligns directly with the principle of information asymmetry. Whereas units at all levels ingest vast, rich data streams, AI helps condense and distill them, returning only the data needed to support intent; not overload it. As a result, units can facilitate slimmed-down operational orders, agile maneuver planning, and increased autonomy, which are especially crucial at the tactical edge. These advantages are magnified in degraded, denied, intermittent, and limited environments, where bandwidth constraints and contested networks hinder the movement of large amounts of data. In such contexts, AI-enabled data curation and prioritization ensure decision making remains viable, preserving operational tempo.

Distillation: Refining Structured Data into a Decision Advantage

If condensation gives data form and order, distillation gives data meaning and direction. Here, the promise of information asymmetry truly manifests: Upstream abundance becomes downstream clarity, empowering commanders to act without drowning in noise. In its most refined state, distillation could be an AI agent that continuously scans condensed data pools, extracts key patterns, and surfaces predictive insights and recommendations to enhance decision quality further. Modern computational infrastructure, data engineering, and machine learning make this possible and scalable at the pace of war.

Instead of staff officers and analysts manually combing through copious amounts of data in search of meaning and recommendations, distillation tools do the curating, surfacing only the information that matters, predicated on a commander’s critical information requirement (CCIR). This process creates the foundation for wise decision making and directly supports the timeless truth from the Center for Army Lessons Learned: Operational success demands timely and effective decisions based upon applying judgment to available information and knowledge.29

Functions of AI-Enabled Distillation

The following list outlines how distillation might take shape in practice.

  • Answering operational queries. Machine-assisted workflows can persistently respond to CCIRs and battlefield questions, such as: Where are the enemy’s weakest concentrations? Machine-assisted workflows respond with contextually grounded, real-time insights, rather than generic answers. These workflows are conceptually similar to Salesforce Einstein, which is used in industry to predict customer behavior based on current and historical sales data.30

  • Constructing courses of action. By modeling factors like risk, timing, and resource consumption, AI agents can propose ranked courses of action. This process mimics the next-best-action logic in commercial AI and supports mission command by offering options, not directives.31 As Thom Hawkins and Alexander Kott observe, decision aids can expand a commander’s decision space and still defer final judgment to the human, enabling more innovative, less constrained options in fast-moving scenarios.32

  • Summarizing into actionable narratives. Instead of flooding commanders with dozens of isolated reports, distillation packages complexity into concise recommendations. Rather than receiving 40 updates about bridge collapses, a battalion commander may see this message: Primary main supply route compromised; alternate route via objective blue recommended within four hours. The clarity is immediate, and the decision is easier. Data storytelling, an emerging field of data distillation, should be an integral part of this process.33

Condensation-Distillation Framework in Practice

As an example of the condensation-distillation framework in practice, Gilles Desclaux, retired French Army general; Damien Marion, AI and cognitive systems researcher at Thales Group; and Bernard Claverie, founder of the École Nationale Supérieure de Cognitique in Bordeaux, France, created a machine-aided process called the Augmented Near real-Time Instrument for Critical Information Processing and Evaluation.34 Commanders and staff would naturally begin the process by defining three to five CCIRs that focus all intelligence collection and analysis on the questions most likely to affect decision quality.35 Multiple automated data feeds, such as videos, reports, and open-source information, are normalized and stored for further analysis. Further, CCIRs are broken down into subcomponents where simple information (for example, the detection of bridge damage) would use rule-based engines to activate alerts, whereas more complex information (for example, changes in an enemy’s force posture) would leverage human-machine teams for decision making and CCIR refinement.36

In the Augmented Near real-Time Instrument for Critical Information Processing and Evaluation, all active CCIR alerts feed into a single-pane dashboard where commanders and staff review evidence and select recommended courses of action. Human review is built in as a feature to assess alert accuracy, adjust thresholds, and recalibrate as needed.37 This concept of a persistent loop system enabled by a condensation-distillation framework continuously refines the decision space, ensuring the commanders’ highest priorities, in concert with strategic intent, remain central within their operational environments. Benjamin Jensen and Matthew Strohmeyer coined a more comprehensive yet similar process, “the Adaptive Staff,” in which AI agents and human facilitators work in tandem to generate, refine, and adjust plans based on real-time data. Through this process, AI and humans link intelligence fusion, operational planning, logistics coordination, and fires management to ensure holistic and expedient decision making.38

As the Department of Defense’s Joint All-Domain Command and Control strategy noted, success now hinges on optimizing “the availability and use of information to ensure that the commander’s information and decision cycle operates faster relative to adversary abilities.”39 As highlighted in the Augmented Near real-Time Instrument for Critical Information Processing and Evaluation model, the condensation-distillation framework reaches its full potential only when its outputs feed directly into a commander’s digital view of the battlespace, often referred to as the common operational picture (COP), where curated insights can be translated into actionable awareness.

Common Operational Pictures and Information Asymmetry

To preserve mission command’s information asymmetry, tomorrow’s COP must embed automated condensation-distillation pipelines.

  • Condensation gathers raw inputs from all facets of the operational environment, structuring the data into relational and semantic databases.

  • Distillation transforms structured data into tailored visualizations and recommendations that speak for themselves.

Through this pipeline, every commander, from squad leader to theater commander, receives the required view, with strategic patterns at the top and pinpointed insights at the edge. Information flows remain deliberately uneven: Tactical teams see only the information that enables rapid initiative within intent, and higher headquarters access the full depth of the battlespace. This vision aligns with the emerging doctrine that asserts information serves both as a weapon and a resource in modern warfare.40 AI-driven COPs let friendly forces wield information more effectively, accelerating decision cycles, lightening cognitive loads, and amplifying initiative across dispersed operations.

Doctrine defines the COP as “a single display of relevant information within a commander’s area of interest tailored to the user’s requirements and based on common data and information shared by more than one command (ADP 6-0).”41 That definition endures. But the way data is ingested, filtered, and presented in an AI-enhanced battlefield must evolve, particularly the art of calibrating information asymmetry through the COP. All too often, legacy COPs buckle under raw or poorly curated feeds. As sensors proliferate and communications multiply, decisionmakers can find themselves buried, not emboldened. Without disciplined condensation and smart distillation, the COP becomes a monument to paralysis rather than an engine of decision advantage. Simply layering more data onto the same screen does not sharpen decision making; it dulls it. The most effective COPs mirror reality’s complexity yet remain immediately actionable. The COPs’ overlays should intuitively suggest next moves, much like how a well-designed flight instrument guides a pilot.

Managing Complexity: AI as a Staff Digital Twin

Ultimately, AI should serve as a sophisticated digital counterpart that continuously gathers, analyzes, and highlights the most essential information to enhance human decision making and should not serve as a substitute for military commanders. The usage of AI-enabled digital tools to support human decision making does not occur without consequence, bringing significant strategic implications that underscore the critical importance of maintaining human involvement.42 The aim is to promote quicker, more intelligent, and streamlined channels of critical insights that drive decisive action. In this framework, human commanders retain the ultimate authority and accountability, while AI expertly manages the flow of information, ensuring mission command remains effective and focused. More importantly, applying digital staff, such as AI agents, can reduce the work of numerous analysts and traditional information-processing methods. Whereas AI agents should not replace the essential, higher-level staff officers called to advise on vital, life-and-death decisions, AI agents can reduce the number of support personnel these officers require.43 The payoffs are more than informational. As the Russia-Ukraine War shows, bloated staffs make headquarters vulnerable in modern-day warfare. Undoubtedly, successful militaries will be those that optimize the roles, responsibilities, and interdependencies between humans and intelligent machines without becoming subordinate to the technology.44

To capitalize on this evolution, the Army should also consider operationalizing a disciplined approach to measuring how information flows, is distilled, and drives outcomes in real time. Designing a framework that deliberately assesses information asymmetry could help build trust and confidence within the human-machine-team construct. This evaluation would also enhance decision-making agility through meaningful feedback loops.

Information Asymmetry: From Concept to Measurables

To unlock AI-augmented mission command’s full potential, condensation and distillation demand constant scrutiny and fine-tuning. It is vital to measure the speed, precision, and tactical impact of information flows. Only then can commanders ensure information asymmetry remains a force multiplier rather than a vulnerability.

The core challenges of information asymmetry are the volume of data and unwieldy workflows. Unfiltered torrents of sensor feeds and legacy processes can push decisionmakers toward missteps. The true opportunity lies in wielding asymmetrical information workflows with purpose, streamlining the path from collection to insight. Two keystones of success are trustworthy analysis and distribution speed: Delivering the right information expediently can signal a level of mastery within the battlespace’s information dimension.

Translating information asymmetry into actionable metrics means applying both qualitative and quantitative gauges across the condensation-distillation pipeline. This concept can be more easily understood within the well-known OODA loop.

  • Data density (observe). Tracks how many sensor events, signals, or inputs arrive per minute. Higher density signals broad situational awareness but also raises processing demands.

  • Fusion latency (orient). Measures the time required to transform raw inputs into decision-ready data. Lower latency means commanders receive structured insights with minimal lag.

  • Decision-cycle compression (decide). Captures how quickly information travels from detection to orientation, recommendation, order issuance, and action. Time-stamping each leg of the OODA loop highlights bottlenecks and enables systems to be recalibrated, thereby allowing more effective information management. For example, trials conducted by systems-engineering students during a United States Military Academy academic simulation demonstrated AI decision aids reduced the sensor-to-shooter interval from 11.0 minutes to 7.7 minutes—nearly a 30 percent compression of the OODA cycle.45

  • User trust (act). Provides a qualitative yardstick reflecting commanders’ confidence in AI outputs and commanders’ willingness to act on those outputs. Behavioral analytics that track how often recommendations are adopted provide real-time feedback, enabling continual refinement.

By operationalizing these types of conceptual metrics, the Army could further cultivate a culture of data-driven adaptation. Condensation and distillation thus become a living feedback loop, ensuring AI-enabled workflows evolve in lockstep with the ever-shifting demands of multidomain operations.

Implementation Challenges

The integration of emerging technologies continues to challenge the Army’s agility. Alex Miller, chief technical officer for the Army, candidly admits, “We had built up a lot of technical debt and process debt. As technology evolved and as commercial industry really got into the edge processing game and data analytics and cloud, we had processes in place that didn’t allow us to change fast.”46 Thomas W. Spahr also notes leaders should recognize AI systems demand ongoing maintenance, which requires the dedicated time of analysts and engineers to remain effective.47 The Army should scale efforts like the XVIII Airborne Corps’ Scarlet Dragon by continuing to embed software engineers directly into operational problem sets, fostering real-time collaboration between coders and war fighters. Paired with smarter and more accelerated acquisition, these measures can shorten development cycles, retire legacy constraints, and rapidly transition commercial innovations into the hands of soldiers. Over time, reducing both technical and processual debt in this way will rebuild institutional agility and sustain a culture of continuous transformation.

The implementation of AI-enhanced mission command also faces challenges beyond technical considerations. A healthy dose of “if it is not broken, don’t fix it” proves useful in any organization, military or otherwise. But the inherent conservatism bred by familiarity will push back against AI and automated information management. Legacy command-and-control architectures and institutional resistance to machine-in-the-loop planning must be considered when adopting AI tools in command and control. Yet, as Michael S. Farmer posits, human cognition without augmentation will not survive warfare’s evolving character.48 The alternative to AI-enabled mission command is irrelevance. Harnessing AI means enhancing, not replacing, human judgment.

Training and education present additional challenges. Mission-command philosophy emphasizes trust and initiative: qualities that may be compromised if soldiers perceive AI tools as undermining their judgment or autonomy. Comprehensive training programs must demonstrate how AI enhances rather than replaces human decision making. Integrating AI education into professional military education pipelines and even daily garrison operations would drive greater confidence and trust in AI.

The integration of AI into mission command also raises questions about developing doctrine. Although well-established, mission-command doctrine will need to be updated to incorporate information-asymmetry concepts and AI-enabled decision support explicitly. For example, ADP 6-0 could benefit from expanded guidance on optimizing information flows in AI-enhanced environments.

Conclusion

By deliberately optimizing information asymmetry (the core characteristic that distinguishes mission command from other command styles), the Army can transform operations across all echelons. Configuring a condensation and distillation framework offers a structured approach to achieving this transformation. By condensing unstructured data into structured, intelligent repositories and distilling that information into decision-quality insights, AI-enabled systems can empower decentralized decision making without sacrificing unity of effort.

The framework proposes a persistent feedback loop, where AI continuously condenses and distills data in real time to answer CCIRs. This dynamic flow allows command posts to adapt quickly to evolving conditions within their operational environments, which prevents mission command from stagnating under the weight of common planning-cycle inefficiencies. Emerging systems such as NGC2, AIDP, and MSS already demonstrate properly curated information flows enhance adaptability, speed, and initiative under complex, contested conditions.

The condensation-distillation framework also transforms AI from an analytic add-on to a digital twin of the staff, anticipating operational needs, aligning recommendations with the commander’s style, and sustaining tempo when human bandwidth is strained. The framework elevates the necessity of data structuring and storytelling, especially due to the criticality of information as a dynamic of combat power.

All things considered, technical innovation alone will not guarantee success. Organizational culture, doctrine, training, and leadership development must evolve in parallel. Soldiers and commanders must build confidence in AI-enhanced systems by understanding how those systems amplify mission command’s foundational principles rather than replacing human judgment. Doctrine should incorporate explicit guidance on managing information flows and cultivating optimal information asymmetry across echelons. Implementation must continue to address system-integration challenges, data governance, network resiliency, and the ethical dimensions of AI-supported decision making.

Maintaining mission command’s agility demands embracing AI as an enabler of disciplined initiative and operational advantage, as an invisible extension of leadership, not as a replacement for it. Preserving the spirit of mission command in an AI-driven world will not happen by chance; it will happen through the deliberate mastery of information asymmetry—from the first byte to the final bite into the enemy’s defenses.

 
 

Disclaimer: All systems, scenarios, and technologies discussed are drawn from open-source, academic, or conceptual materials. This article does not contain or imply classified operational data.

 
 

Sorin Adam Matei
Dr. Sorin Adam Matei is the associate dean of research and graduate education in the College of Liberal Arts at Purdue University, a professor of communication in the Brian Lamb School of Communication at Purdue University, and the director of the FORCES Initiative dedicated to defense technology studies. Matei holds a PhD from the University of Southern California with a focus on human-computer interaction, a master of arts degree in international relations from Tufts University, and a bachelor of arts degree in history and philosophy from the University of Bucharest. His primary research interests are interdisciplinary, including human-technology and AI interaction with defense applications.

Kyle P. Reed
Kyle P. Reed is a US Army military intelligence warrant officer and doctoral student in Purdue University’s doctor of technology program, whose research examines human-AI collaboration in military decision making. Reed holds a master of science degree in information technology management and a bachelor of science degree in information technology from Western Governors University. His primary research interests include cognitive engineering, systems thinking, and socio-technical integration in military and defense contexts.

 
 

Endnotes

  1. 1. This fictional scenario is illustrative and refers to generalized present or near-future capabilities that are publicly reported.
  2. 2. Bradford Witt and Sorin Matei, “Mission Modeling for Commanders: Improved Operational Effectiveness Through the Use of Measurable Proxy Variables,” Military Review (March–April 2023): 35–42; and “FORCES Initiative: Strategy, Security, and Social Systems,” Purdue University, n.d., accessed August 23, 2025, https://www.cla.purdue.edu/research/forces-initiative/index.html.
  3. 3. Headquarters, Department of the Army (HQDA), Mission Command: Command and Control of Army Forces, Army Doctrine Publication (ADP) 6-0 (HQDA, July 2019).
  4. 4. HQDA, Mission Command.
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