How AI accelerates defense production

A new defense aircraft is ready, but the software update is missing. The result: the operational start is delayed. Scenarios like this are not an exception in the defense industry but symptomatic of a structural problem: the systems are digital, but the production processes often are not. Fragmented IT, manual processes, and lack of data integration slow down production. Speed has long since become a security-relevant resource. Artificial intelligence (AI) can help build the necessary digital bridges - and systematically accelerate defense capability.

Production reality in the defense sector: between complexity and speed

About the author:

Arian van Hülsen is director solutions consulting A&D & global AI champion at PTC and AI speaker at the Bitkom Academy. His focus is on the integration of AI systems in safety-critical industrial environments.

In the defense industry, companies are under double pressure. On one hand, highly complex, safety-critical, and regulatory demanding systems must be developed, manufactured, and certified. Development cycles are long, changes are strictly regulated, approval processes are formalized and internationally coordinated. Even minor technical adjustments entail extensive testing and approval steps.

On the other hand, expectations for delivery capability and response speed are rising significantly. Multinational programs like FCAS or MGCS, as well as national upgrade and modernization projects, demand shorter cycle times, reliable scheduling, and greater adaptability to changing requirements. Production capacities must scale faster, variants must be mastered, and supply chains must be kept stable.

In practice, however, these requirements often encounter established IT landscapes, isolated engineering and manufacturing systems, and a lack of digital standards. Manual handovers, limited data availability, and restricted transparency lead to friction losses and delays along the value chain. The result is a structural tension: A defense industry that would be technologically capable but loses speed due to its own digital bottlenecks.

Ai as an industrial enabler

In this context, ai is evolving from a mere optimization tool to a central factor of industrial acceleration. However, its benefits depend less on individual algorithms and more on the digital structure in which it is embedded. For ai to be effective along the product lifecycle, continuous platforms are needed: application lifecycle management (alm) for software-based functions, product lifecycle management (plm) for mechanical and mechatronic components, and the intelligent product lifecycle (ipl) as an overarching framework.

This connects development, production, and operational information in a consistent system landscape, creating the basis for scalable, integrated processes. Instead of isolated individual solutions, a digital foundation emerges on which information can be structured and decisions prepared across disciplinary boundaries. Only on this basis can ai be meaningfully embedded in industrial processes and used throughout the entire lifecycle:

1. Predictive maintenance in manufacturing

Ai-supported real-time analyses help to detect critical machine conditions early and prevent failures. This reduces downtime, improves production planning, and increases availability. Especially in highly specialized production lines, such as the manufacture of electronic subsystems or special drives, even the smallest interruptions can become costly. Ai-based predictive maintenance solutions provide the necessary transparency and enable early intervention before a failure occurs. For multinational defense projects like mgcs or nato drone programs, this ensures that production cycles are maintained and delivery commitments become reliable.

2. Simulation and validation with digital twins

Testing safety-critical systems is costly and time-consuming. Long before physical prototypes exist, digital twins in combination with ai already enable virtual test runs and automated validations. Especially in defense production, where every design error can have fatal consequences, simulation-supported development processes are a real gain in time and safety. The validation of functions is carried out digitally, and compliance requirements such as do-178c or iso 15288 can be integrated into the process at an early stage. This not only reduces risks but also creates a digital basis for approval and maturity decisions along the entire supply chain.

3. Automated approval & compliance

AI agents automatically capture and document relevant engineering data, creating an auditable basis for certifications. This accelerates approval and reduces regulatory risks. Instead of manually documented development processes, traceable digital artifacts are created, which can be provided to regulatory authorities in a structured form. In combination with model-based systems engineering (MBSE), a continuous proof of development work is created.

4. Change impact prediction in engineering

In large programs like FCAS, MGCS, or NATO drone and air defense projects, changes to individual components can have far-reaching consequences. AI simulates potential impacts of technical adjustments in real-time and helps avoid iterations. This affects not only technical feasibility but also safety approvals, supply chain planning, and cost calculation. For example, if a new function is added to a software module for a flight control system, AI automatically analyzes which subsystems are affected, whether new tests are necessary, and whether approval requirements need to be re-examined. By using AI, changes can be prioritized based on risk and implemented securely within the project - a decisive advantage in dynamic programs with many participants and high regulatory complexity.

5. Interoperability through data harmonization and standardization

In multinational programs, system compatibility is crucial: AI supports the harmonization of different data sources and interfaces. This is an important prerequisite for the digital flow of information between partners. Only when semantically consistent data models are available can systems-of-systems like FCAS or MGCS be developed and operated across countries and industrial contexts. At the same time, AI can also help to not only comply with international standards like ISO 15288 (systems engineering), ISO 27001 (information security), EN 9100 (quality management), or DO-178C (safety-critical software) but also to implement them efficiently through automated proof, intelligent data classification, and continuous compliance checks.

Digital resilience requires architecture and speed

The question is no longer whether AI will become relevant in the defense industry, but how quickly companies will integrate it into their core processes. Those who work with isolated tools and proprietary data models today risk exclusion from multinational programs. Companies should therefore gradually pursue an IPL strategy: with interoperable platforms, digital traceability, and model-based engineering.

Especially for specialized suppliers and medium-sized production companies, a strategic window of opportunity is opening up: Those who now invest in standardized, auditable data flows and the integration of AI strengthen their position in multinational supply chains. AI, ALM, PLM, and especially the intelligent product lifecycle form the cornerstones for a resilient, scalable, and auditable defense production.