Digital engineering is now a mandated standard across the Department of War and contractors. Every acquisition program is required to integrate digital engineering practices into system development and lifecycle management. This mandate is a fundamental shift in defense innovation and impacts the design, build, and sustainability of critical assets.
As part of compliance, AI-enhanced digital twins have quickly become embedded in design and operational phases. These virtual replicas of physical systems include real-time data and machine learning (ML) algorithms. By shifting to a more predictive posture, program managers can cut time, costs, and risk across the entire lifecycle. Instead of waiting for cost overruns or product failures to occur, you can now anticipate problems and handle them before they snowball.
Secretary of Defense Pete Hegseth underscored the urgency of this change during his confirmation hearings. “The Pentagon often builds entire systems without first using a digital design,” he said, “which means you build prototypes and then scrap them and start over again. No private sector business could survive doing business that way.”
This method produces significant delays and cost overruns, which are no longer acceptable in today’s rapidly evolving environment. For manufacturers striving for military innovation, digital twins, AI/ML, and model-based systems engineering (MBSE) have become essential for driving efficiency, readiness, and resilience.
The ongoing advancement of defense manufacturing relies on a more sophisticated technology stack than ever before. Digital twins now integrate data ingestion from sensors, PLCs, and SCADA systems, physics-based and data-driven models, ML pipelines, and decision orchestration across design, production, and sustainment. This is not an incremental change; it is a comprehensive reimagining of the manufacturing process.
“Advancements in these technologies lead to faster product development and reduced costs, ultimately supporting improved acquisition decisions and outcomes, even into sustainment,” said Deputy Secretary of Defense Stephen Feinberg.
By embedding intelligence directly into the digital twin, the DoD can move more quickly from design concepts to validated production plans, reducing rework and ensuring more efficient resource allocation.
AI and ML are already embedded in many defense workflows. Deloitte’s 2025 Aerospace and Defense Industry Outlook reports that 81% of industry respondents are already using or plan to use AI/ML technology, adding leverage at every stage of the manufacturing lifecycle.
Accelerating Line Changes and Tolerance Studies
ML-augmented models enable engineers to validate tooling and run tolerance studies before the first piece of metal is cut. This reduces costly mistakes and accelerates production start-up.
Enabling Parallel Planning
With production digital twins, design and manufacturing teams no longer work in sequence. Instead, engineers can do design work and manufacturing planning simultaneously, identifying risks early and eliminating unnecessary delays.
Optimizing Cycle Times and Work-in-Progress Flow
Digital twins optimized with ML can simulate different production scenarios to improve cycle times and manage work-in-progress flow, creating smoother operations.
Mitigating Supply Chain Risks
Beyond design, digital twins used throughout the manufacturing process reduce inventory risk, supplier fragility, and the need for “what-if” rerouting of parts. This predictive capability ensures that disruptions in the supply chain don’t compromise mission readiness.
Real-world defense programs offer powerful examples of how digital twins and ML are already reshaping outcomes. Here are a few examples of projects in action.
Digital twins are the virtual representations of physical systems, while model-based systems engineering (MBSE) serves as a single source of truth, linking requirements, configurations, interfaces, and verification to the digital twin and digital thread.
The synergy is powerful:
Together, MBSE and digital twins make sure each stage of system development is in sync.
The future of military innovation is the convergence of digital twins, AI, ML, and MBSE into unified ecosystems. Moving forward, expect to see even greater adoption and synchronization.
Converging Digital Twins Across Domains
Today, twins exist for design, manufacturing, and sustainment. Tomorrow, these will be fully converged, offering end-to-end visibility and control.
Network and Mission-Level Digital Twins
Beyond individual platforms, digital twins will model entire missions and networks, enabling commanders to assess readiness, test scenarios, and optimize force deployment before operations begin. Digital twins can simulate endless what-if scenarios to help commanders increase battlefield and mission awareness, enabling them to make better decisions faster.
Expanding Use of Additive Manufacturing
With digital twins, additive manufacturing can be validated more quickly, reducing risk in deploying 3D-printed components and enabling the on-demand production of critical parts in the field. These advancements enhance acquisition outcomes and ensure that US forces remain agile in today's rapidly evolving environment, where technology is advancing rapidly.
Digital twins and AI are central to the future of defense manufacturing. By providing real-time foresight and control, Department of War program leaders and contractors can reduce costs, mitigate risks, and strengthen resiliency.
Digital twins and AI bring real-time foresight and control into defense manufacturing. Whether building new systems or sustaining legacy platforms, these technologies empower DoD leaders to reduce cost, mitigate risk, and ensure production resilience at scale. Contact us for help with the strategic integration of advanced technologies and methods to streamline and optimize the development, maintenance, and operation of systems and infrastructures.