Lessons from Ukraine: Data, AI, and Electromagnetic Warfare

Original broadcast 9/17/25

Presented by Carahsoft

The conflict in Ukraine has become a proving ground for modern military technologies, offering real-time lessons about the centrality of the electromagnetic spectrum (EMS) in 21st-century warfare. David May, Senior Cyber Intelligence Advisor at the U.S. Army Cyber Center of Excellence, and CW5 Travis Ysen, Senior Technology Advisor at the U.S. Army Training and Doctrine Command, shared how those lessons are shaping Army thinking and priorities.

May and Ysen agree on one critical observation: electromagnetic warfare has become a decisive factor on the battlefield. In Ukraine, adversaries have successfully disrupted precision-guided munitions, jammed GPS signals, degraded communications, and targeted military assets with low-cost, mass-produced EW equipment. These capabilities, once thought to be the domain of advanced militaries, are now widely accessible, posing significant challenges for U.S. and allied forces.

The speed of adaptation is perhaps the most striking lesson. In the EMS, adversary tactics and techniques change in weeks, not years. Ysen stresses that this requires a fundamental shift in how the Army approaches reprogramming. Instead of waiting months or years to update systems, the Army must be able to reprogram sensors, countermeasures, and software in near real-time. That means designing platforms and architectures with flexibility at their core, capable of ingesting new data, updating algorithms, and pushing changes across the force rapidly.

This speed imperative extends to interoperability across the Department of Defense and with coalition partners. As Ysen explains, joint operations demand common standards and seamless data sharing. Without the ability to share refined, usable data at the right time and place, decision-makers face cognitive overload. The challenge is not just collecting vast amounts of raw data but ensuring it is processed, prioritized, and delivered in actionable form.

May elaborates on the role of data in enabling rapid reprogramming. He describes the Army’s shift toward a platform-agnostic approach, where software and algorithms can operate across disparate systems. To make this possible, the Army is participating in efforts like the RF Data Pilot to identify and establish common data standards for sensors and effectors. By standardizing how data is generated, shared, and acted upon, the Army can ensure that updates and countermeasures can be deployed quickly across platforms and services.

Artificial intelligence and machine learning are emerging as critical enablers of this vision. May points out that AI can accelerate reprogramming cycles, reducing timelines from weeks to hours or even minutes. AI can automatically characterize signals, identify anomalies, and suggest countermeasures, allowing operators to respond more quickly to adversary adaptations. For example, when a radar suddenly shifts frequencies or a communications system begins hopping channels, AI can flag the change and recommend immediate adjustments.

However, both leaders caution against overreliance on AI. Ysen emphasizes the need for a “trust but verify” approach, where AI-generated recommendations are validated by trained operators. Trust in AI systems will take time to build, as soldiers and commanders must see consistent, accurate results before relying on them fully. Moreover, AI requires vast quantities of high-quality data to train effectively, and building that repository will take years of investment and collection.

Talent is another pressing issue. May notes that the Army must recruit and retain highly skilled personnel who can work with both EW systems and advanced AI tools. These individuals are in high demand across the private sector, making it a challenge to ensure the Army has the expertise it needs. Without a strong human foundation, even the most advanced technologies will fall short.

Edge computing also plays a vital role in making EMS operations effective. Bandwidth on the battlefield is limited, and transmitting raw data across networks is impractical. By processing and refining data at the edge—close to where it is collected—the Army can reduce the burden on networks and provide decision-makers with usable insights faster. Refined data, not raw data, should move across the force, ensuring that commanders are not paralyzed by information overload.

The overarching lesson from Ukraine is clear: electromagnetic warfare is not a supporting capability—it is a central domain of conflict. U.S. forces must be prepared to adapt as quickly as adversaries, leveraging flexible systems, common data standards, AI-enabled analysis, and edge computing to maintain an advantage.

For May and Ysen, the path forward requires a balance of technology, data, and people. Technology must be designed for rapid reprogramming. Data must be standardized, trusted, and refined. People must be trained, trusted, and empowered to validate and act on machine recommendations. Together, these elements will ensure that the Army can compete and win in the contested electromagnetic spectrum.

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