Central to meeting the complexities of JADC2? Artificial intelligence – C4ISRNet

The concept of Joint All Domain Command-and-Control (JADC2) remains a nascent one, with clear doctrines yet to be defined and tested. However, no matter how these are shaped it is apparent that two key requirements must be addressed: speed of action, and the ability to process and analyze vast volumes of complex data that could not have been perceived of in the past.

The capabilities inherent in fifth generation aircraft, such as the F-35, exemplify the data management challenges that advanced systems bring and which must be addressed in multi-domain operations. The aircraft is as much a flying sensor as it is a combat platform, and the diversity and volume of data that it can collect places a significant burden on militaries if they are to benefit from it in a meaningful way. When factoring in the speed at which a conflict with a near-peer will be conducted and that it will extend beyond the traditional domains, there is a genuine risk that commanders could be overwhelmed by the data that needs to be processed in order to affect a winning outcome.

While significantly scaling up manpower could be one solution, the complexity of the data and speed of action required necessitates a step-change in capabilities in the command and control domain. It is here that potentially game-changing benefits can be brought through leveraging artificial intelligence.

JADC2 demands a comprehensive, dynamic, and near-real time common operating picture (COP) and AI can certainly aid in speeding-up decision making and defining parameters. AI can automate filtering and configuration based on prior experience. Beyond this, however, it promises the ability to examine command decisions and learn what should be done to achieve mission goals, automatically proposing and ranking actions.

Central to the utility of AI will be the availability of robust data, and the successful application of machine learning (ML) will be dependent on this. Machine learning has already proven its worth in anomaly detection and track correlation, taking this to the next level it will also be able to provide early warning of enemy actions. AI has the potential to recognize when an adversary is preparing their forces for a particular action and in a particular area, for example, by analyzing troop movements, aircraft sorties, and training activity. The technology could, in theory, automatically alert commanders, propose a course of action, and ultimately task units. The application of natural language understanding could even enable intelligence reports to be generated from disparate data.

AI also has a clear application in supporting resource-to-task management, such as in composing an air tasking order. Understanding which assets are available and best placed to complete a task is a significant challenge in a theatre-wide conflict, if AI can reach across all domains it will be able to appraise commanders of the most suitable resources to employ, including those that might not have been apparent with manpower alone. AI will also be able to quickly alert commanders and even automatically adjust orders as a mission unfolds or new intelligence emerges, for example, in editing an air tasking order to optimize the deployment of assets.

The utility of AI in enabling JADC2 is apparent. What is less clear, however, is how the best AI capabilities should be developed and fielded to ensure maximum affect across all services and domains. The need for a man in the loop is essential and the application of AI does not imply a change to autonomous systems and robotic warfare, but AI support must be regarded as trustworthy by operators and commanders.

A cohesive approach is essential in developing AI for JADC2 and services must consider themselves to be customers and suppliers of one another. Capabilities cannot be developed in silos. If services are not cognizant of the needs of a combined force there will inevitably be capability gaps and disconnects in the command structure and processes. This challenge is complicated further when considering the nature of operations, where the coalition is the norm.

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The design of the core C2 systems employed for JADC2 is also a key consideration. The need for information sharing at speed and the ability to draw on a wide range of sources are crucial. Inherent in their design must be open architectures that enable new applications to be quickly developed and integrated, along with seamless interoperation between forces. Standards-driven designs are a must and it is essential that systems are not stovepiped and can reach not only across services, but the theatre of operation as a whole. Security issues will exist but must be overcome and not constitute a barrier for information and data sharing between domains.

Ensuring that partners have the requisite AI capabilities and access to relevant data is another hurdle. While disparities in capabilities is not a new issue, in the context of JADC2 where speed of action will be critical this is magnified.

There are many technological, doctrinal, and operational factors to consider in the implementation of AI, what is clear, however, is that the technology promises the ability to greatly shorten the OODA loop and bring a step change in C2 functionality. In a conflict with a near peer, AI will be a necessity rather than a luxury.

Retired Maj. Gen. Henrik Rboe Dam is the former head of the Royal Danish Air Force and the air domain adviser at Systematic.

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Central to meeting the complexities of JADC2? Artificial intelligence - C4ISRNet