Katie Fisher, Chief Engineer
The next generation of defense systems will not excel in modern warfare through hardware alone, but rather by how quickly and confidently we can design, validate, and deploy it. As mission complexity increases and multidomain coordination becomes the norm, traditional engineering approaches are unable to support today’s complex system of systems. The conflict in Ukraine has shown that using artificial intelligence (AI) in unmanned systems is a viable solution to reduce direct warfighter involvement while enhancing and increasing combat effectiveness.
Programs like Golden Dome, the proposed U.S. Department of Defense (DoD) latest AI-enabled command-and-control missile-defense system, reflect this new reality. Designed to fuse data across land, sea, air, space, and cyber, the Golden Dome initiative is not just a technical upgrade, it’s a blueprint for how AI is being woven directly into the command fabric of modern defense. And it’s not alone.
The U.S. Air Force’s AI Accelerator and Center of Excellence are doubling down on the same idea: If the industry and the military want faster threat assessment and tighter integration across systems, AI must be adopted from the start, which means reengineering the way we build defense systems, not just the way we use them. It’s no longer viewed as a future-facing advantage or a “nice to have” application, but AI is a necessary capability that must extend across both the systems and the engineering processes that shape them.
Along those same lines, the Air Force’s modernization of the B-52, known as the B-52 Commercial Engine Replacement Program (CERP), has integrated AI-enabled systems engineering to place an upgraded engine into the aircraft, enhance its capabilities, and ultimately extend its service life.
The foundational elements of systems engineering – requirements, models, documentation – have historically been created through slow, manual processes that no longer keep pace with the speed of need. AI-enabled model-based systems engineering (MBSE) changes that.
When embedded into digital engineering environments, AI acts as a force multiplier by automating time-consuming, error-prone tasks and helping engineers make better decisions earlier. AI does this by identifying relevant information within unstructured data; converting it into structured, reusable models; and preserving traceability throughout the development lifecycle. The resulting digital artifacts exhibit quality, consistency, and enhanced traceability to ground truth, all of which augment engineering activities spanning the life cycle of the system.
For example, with generative AI tools, engineers can generate structured models from unstructured inputs in a matter of hours, not only accelerating the early design phase, but also surfacing information that may not be immediately obvious in raw source material. Retrieval-augmented generation coupled with automation produces intrinsic digital threads to source data. With tighter links to those inputs and far less reliance on rework, teams will have a reduced manual workload while also securing greater confidence in the validation of their artifacts.
This aspect is especially critical as firm-fixed-price contracts and accelerated acquisition timelines become the norm. When there’s no time (or budget) to rework foundational missteps, getting it right the first time becomes nonnegotiable. AI-enabled MBSE gives teams stronger technical baselines, tighter feedback loops, and intelligent tools that reveal misalignments before they spiral into bigger problems.
The industry is already seeing these benefits play out in programs operating across both classified and unclassified domains. AI-supported workflows are streamlining reviews, enhancing model fidelity, and identifying potential design flaws earlier in the life cycle. They’re also helping programs stay aligned with evolving compliance requirements like the National Institute of Standards and Technology (NIST) and Cybersecurity Maturity Model Certification (CMMC) frameworks, a feat that’s increasingly difficult to manage manually.
Let’s face it: While AI is doing more to manage those complexities, AI-enabled engineering environments aren’t meant to displace human expertise. They are designed to enhance it by reducing bottlenecks and surfacing information earlier, giving engineers more space to focus on more high-impact and strategic decisions. Human judgment remains central but can now be enhanced by fast, transparent AI systems.
As defense systems grow more complex and missions more time-sensitive, integrating AI into the engineering process is the foundation that will enable programs like Golden Dome to succeed not just in concept, but in real-world execution. AI will soon extend beyond design to support every phase of development, and by embracing these tools now, the defense community can build faster, smarter, and with greater confidence in every phase of the mission, as the evolution continues.
