Capt Sarah Harm hunched over her console, eyes darting between screens as the operation entered its critical phase. Three separate radar tracks showed unidentified aircraft approaching from the northwest, while SIGINT had detected encrypted communications consistent with adversary coordination patterns. Satellite imagery from two hours ago showed what might be mobile surface-to-air missile systems in the area, but cloud cover made confirmation impossible.
“Director, I’m tracking multiple potential threats, but can’t confirm correlation,” she reported to the battle director. “Request permission to re-task ISR assets for confirmation.”
“Negative,” came the reply. “All ISR platforms are committed to higher priority taskings.”
Harm’s mind raced through possibilities. Were the radar tracks commercial traffic with misconfigured transponders? Were the communications just routine border patrol operations? Or was this the leading edge of something more dangerous? With dozens of blue force assets in her area of responsibility, the wrong call could be catastrophic.
She made her best assessment: “Assess low probability of threat. Will continue monitoring.” She flagged the lead track in the formation and continued her scan pattern.
What Harm couldn’t know was that an adversary formation was indeed assembling, using a deception technique specifically designed to appear benign when viewed through fragmented intelligence. By the time the pattern became clear to human analysts ninety minutes later, the adversary would have established a dominant position, threatening a high-value long-range target deep in friendly territory…
Now imagine the same scenario with AI-augmented battle management:
As Harm monitored her lane, an AI correlation engine continuously processed all available data streams. Within seconds of the third radar track appearing, the system identified a statistical pattern matching adversary pre-strike positioning. Simultaneously, it correlated the SIGINT emissions with the radar tracks, noting timing consistencies that suggested coordinated movement despite appearing unrelated.
The AI assistant flagged Harm’s display: “ALERT: 87% probability of coordinated adversary formation. Pattern matches REDWOOD tactical signature.”
The system displayed three courses of action:
1. Immediate defensive repositioning of blue forces
2. Targeted ISR taskings to specific coordinates for confirmation
3. Limited electronic warfare response to probe adversary intentions
Harm quickly evaluated the AI’s assessment against her commander’s intent and mission parameters. The pattern recognition made sense, it was connecting dots across data sources that would take a human analyst hours to correlate. She selected option 2, adding specific parameters based on her tactical knowledge.
Within minutes, confirmation arrived. Harm then authorized a precise, coordinated response that neutralized the threat to the long-range target while minimizing force commitment. What could have been a major tactical surprise became a textbook interdiction.
Introduction
This isn’t a theoretical scenario. It’s the reality our air battle managers face daily, even in operational environments. We entrust these life-or-death decisions to human minds alone, despite AI capabilities proven to collapse decision cycles from minutes to seconds. It’s time we stop treating AI as future potential and start deliberately planning AI integration into our air battle management models now.
To meet the demands of modern air battle management, we must rethink traditional models by clearly distinguishing which decisions require human judgment, which can be delegated to artificial intelligence, and how human-machine collaboration should operate throughout the decision-making process.
The Cognitive Battlefield Reality
Today’s multi domain operations create an information tsunami that overwhelms even our most experienced air battle managers, a challenge likely to increase as air battle managers work to exploit an ever-growing array of sensors and communications pathways. They simultaneously process heterogeneous data streams from radars, satellites, and electronic warfare sensors while attempting to prioritize threats based on incomplete information. They manage complex coordination requirements across platforms, domains, and coalition partners while adapting to degraded communications. All this occurs under the constant pressure of adversary actions designed specifically to disrupt this decision cycle.
Human cognitive limits aren’t a capability deficiency; they’re a biological reality. When our air battle managers’ observe, orient, decide, and act (OODA) loops stretch under this load, the consequences are immediate: missed intercepts, suboptimal targeting, and potentially, mission failure or fratricide. The complexity of modern warfare has outpaced our traditional air battle management models, which rely too heavily on human processing power alone.
Our current planning frameworks were designed for an era when information moved more slowly and decision spaces were more constrained. Today’s air battle managers operate in environments where the volume, velocity, and variety of information exceed human capacity. We need air battle management models that explicitly incorporate AI as a core component, not as an optional enhancement.
Field-Ready AI Solutions
The technology to address these challenges isn’t theoretical. INDOPACOM has field-tested AI systems that can fuse multiple sources of intelligence in real time, predicting adversary maneuvers by analyzing tactical patterns at scale. These capabilities were demonstrated in recent Pacific focused exercises where AI agents identified threat formations 3.5 minutes faster than human analysts, a lifetime in contested environments.
These aren’t lab demonstrations. The Air Force’s Angry Kitten electronic warfare program has already demonstrated how AI/ML can rapidly adapt to unknown signals and develop countermeasures in seconds, rather than months. The Army’s Project Convergence has demonstrated AI-enabled targeting that reduced sensor-to-shooter timelines from 20 minutes to 20 seconds. Similar capabilities could transform how air battle managers orchestrate complex operations.
What’s missing is not the technology, but the open-minded thinking needed to incorporate these capabilities into our air battle management doctrine and processes. Our current models treat AI as an add-on rather than a fundamental component of the decision architecture. This approach guarantees suboptimal implementation and underutilization of these powerful tools.
Transforming Decision Architecture
By integrating AI into battle management philosophies, like OODA, human-machine teaming can shatter the decision chains that currently slow response times. Instead of a relay race where information passes sequentially through planning and execution phases, this creates a continuous, adaptive decision network. This requires air battle management models that explicitly define the human-machine teaming relationship at each decision point. Examples of these models exist in the gaming industry and studies like AlphaStar.
Air Battle managers augmented with AI ”Mission Monitor(AIMM)”, which AFRL is developing, and classified tools the NRO has developed allow optimized courses of action based on real-time battlespace modeling. Rather than drowning in data, they would focus on high-order decisions while AI handles routine coordination tasks. The result: a common operational picture that updates in seconds, not cycles, and a decision advantage that translates directly to combat effectiveness. Intelligence platforms like Thresher and FADE, correlated with real-time COA development powered by AI, with a human battle management team, are programs that can be integrated today, and could be an integration that could exist tomorrow if acquisition pathways leverage ready systems, instead of creating them from scratch.
This transformation demands more than simply purchasing AI tools. It requires fundamentally rethinking our air battle management models to specify which decisions benefit from human judgment, which can be delegated to AI, and how the interaction between human and machine intelligence should function. The battle management teams must trust that AI is a tool in the toolkit, not the decision maker itself, but rather the correlator and parser of high-fidelity data needed to make better, faster decisions.
Strategic Imperative
While the joint forces deliberate, our adversaries are moving forward. China’s Military Civil Fusion strategy explicitly prioritizes AI integration into command and control systems. Russia’s combat experiences have accelerated its development of automated decision support tools for battlefield commanders. Both nations are developing battle management models that assume AI augmentation from the ground up, not as an afterthought.
The primary barriers to our implementation, data architecture limitations, and institutional resistance, are surmountable. The Air Force’s Advanced Battle Management System has already demonstrated the technical framework needed. What’s missing is the institutional will to prioritize AI-integrated air battle management models as an immediate operational requirement rather than a future capability.
Every month the joint force delays is a month air battle managers remain cognitively overmatched by the complexity of modern warfare. That gap will inevitably cost lives and missions when competition turns to conflict. The longer the wait to develop AI-integrated air battle management models, the more difficult it becomes to close this capability gap.
The Path Forward
To maintain decision superiority, the Secretary of the Air Force for Acquisitions (SAF/AQ and SAF/SQ) need something more revolutionary to match the speed of the necessary operational battle management changes. That revolutionary change can come in the form of creating a Materiel Leader (ML) with the authorities to go after small, rapid fielding capes based off a User Needs Statement (UNS) that will not require all the typical AQ processes. There is a pressing need to develop an air battle manager training curriculum that promotes open thinking about human-machine teaming and establishes trust in AI as a vital tool for analyzing complex data. Most importantly, the USAF must develop new air battle management models that explicitly define the role of AI in the decision cycle, from information gathering to execution.
These models should specify how AI will assist in pattern recognition across disparate data sources, how it will generate and evaluate courses of action, and how it will monitor execution to identify necessary adjustments. The AI models should define clear handoffs between machine and human decision-making, with appropriate trust-building measures to ensure air battle managers can confidently rely on AI-generated recommendations while maintaining ultimate decision authority.
The technology exists. The need is clear. The only question is whether the USAF and US Navy Air Wings will act with the urgency this moment demands. Air battle managers need AI-powered decision superiority not next fiscal year, not after the next requirements review – yesterday. This requires air battle management models designed from the ground up with AI integration as a core principle, not an afterthought, and a culture that trusts AI as a critical tool for processing the high-fidelity data our warfighters need to succeed.
Nacho is a Senior Systems Architect at BlueSky Innovators, leveraging 26 years of expertise in Battle Management, Advanced Programs Munitions Maintenance, and Joint All Domain Strategy. His career spans tactical precision and strategic foresight, shaping the future of integrated fires and command and control. Beyond the battlefield, Nacho is the founder of Aces Eights Group (AEG), a nonprofit born from the chaos of Kabul’s fall. AEG transforms crisis into coordinated action, empowering veteran-led teams to execute humanitarian missions across Afghanistan, Ukraine, and beyond. Through sub-organizations like Heart of an Ace and the Ukraine NGO Coordination Network, Nacho’s leadership fuels resilience, mental wellness, and community-driven crisis response. His work bridges high-tech innovation with human-centered service—organizing chaos, building networks, and inspiring others to thrive through adversity. Learn more at Aces Eights Group: www.aceseights.org
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