Background. The USAF Advanced Battle Management Systems (ABMS) Cloud Based Command and Control (CBC2) program represents one of the operationally consequential legacy modernization efforts underway in the DAF. Legacy tactical C2 systems were architected around a limited number of fixed data sources, creating brittle, difficult-to-scale workflows that constrained the speed and quality of decisions available to C2 operators executing Find, Fix, Track, and Target (F2T2) kill chain processes. Because legacy systems each provided only a partial operational picture, C2 operators were forced to manually integrate information across multiple disconnected systems to develop a Common Operational Picture (COP) - adding time, cognitive burden, and risk of error at precisely the moments when speed and accuracy matter most.

Situation. CBC2 is radically changing this paradigm by migrating C2 capabilities into a cloud-native environment built on a microservice-based architecture designed to ingest any data set at scale. This approach enables new data sources and Artificial Intelligence/Machine Learning (AI/ML) capabilities to be layered on continuously without re-engineering the underlying platform, eliminating the brittleness of legacy systems and establishing a foundation for continuous capability delivery. Critically, CBC2's architecture is not simply modernizing the kill chain, it is laying the technical groundwork for kill web operations, where any sensor can cue any shooter across domains, decision making is distributed, and the loss of any single node does not degrade overall mission performance. This positions CBC2 as a direct enabler of Joint All-Domain Command and Control (JADC2) concepts as they continue to mature.

Presentation Overview. This presentation details the technical and methodological approach CBC2 is taking to retire legacy technical debt while simultaneously increasing mission readiness across four interconnected lines of effort. The presentation will walk attendees through the CBC2 modernization journey - from an assessment of legacy system limitations, through the cloud-native migration strategy, to the application of Model-Based Systems Engineering (MBSE) to formally model F2T2 operational workflows. Attendees will see how digital models of warfighter activities and decision points are being used to drive requirements for AI-enabled human-machine teaming microservices, identifying the precise insertion points where AI can deliver the greatest decision advantage, reduce cognitive burden, and compress kill chain timelines.

The presentation will also highlight two additional AI-enabled capabilities that are accelerating CBC2's development and assessment pipeline. The Modeling & Simulation (M&S) scenario simulation capability harnesses AI-driven agents to dynamically generate complex operational conditions, enabling rapid exploration of diverse mission threads and uncovering emergent behaviors with far greater fidelity than traditional modeling alone - providing richer, data-informed insights that improve readiness and accelerate decision advantage. Complementing this, the Refiner AI-enabled test automation framework modernizes and accelerates CBC2's validation pipeline by automating Software Acceptance Testing (SAT) and Air Force System Integration Testing (AFSIT) workflows, generating repeatable headless tests, and integrating across CBC2 applications, reducing manual testing burden while improving accuracy, coverage, and the speed at which reliable capabilities are delivered to the warfighter.

The presentation concludes by demonstrating how these elements: cloud-native architecture, MBSE-driven AI requirements, scenario simulation, and intelligent test automation, work together as a coherent modernization strategy rather than a collection of disconnected technology upgrades.

Benefits. The CBC2 approach delivers benefits that span operational performance, technical scalability, and long-term program sustainability. For the warfighter, the most tangible benefit is a measurable improvement in decision advantage: C2 operators gain access to a richer, more timely, and more analytically integrated Common Operational Picture at the moment of decision, enabling faster and more accurate F2T2 outcomes. The use of MBSE to model kill chain workflows ensures that AI is not inserted arbitrarily, but is precisely targeted at the decision points where it delivers the greatest operational return on investment. AI-driven scenario simulation through M&S strengthens planning and assessment by generating high-fidelity operational conditions that expose emergent behaviors and stress-test capabilities before they reach the warfighter. The Refiner test automation framework compounds these gains by enabling development teams to iterate faster, detect defects earlier, and deliver more reliable capabilities across the enterprise, directly increasing mission readiness and reducing the cost and time of validation. From a program management perspective, the microservice architecture creates a scalable and modular foundation that allows new capabilities to be developed, tested, and fielded independently, reducing the risk that any single component failure degrades overall system performance. Collectively, these benefits position CBC2 as a replicable model for how the DAF can approach legacy modernization not as a one-time infrastructure refresh, but as a strategic capability investment that continuously compounds operational and technical value, and that evolves the force toward the networked, distributed kill web architecture that modern multi-domain operations demand.