The defense industrial base (DIB) is under pressure from multiple angles. Stockpiles are being depleted due to expenditures in the conflict with Iran. Initiatives such as Golden Dome are large-scale projects that require specialized and complicated materials and components. And ongoing supply chain shortages threaten to hamstring all of these efforts.
“The reality we’re facing is that the traditional way we build military platforms – where it takes years, sometimes a decade, to move a system from a whiteboard to operational deployment – is a strategic liability,” said Ana Garcia Olson, Managing Director, Navy & Marine Corps Business Lead, Accenture Federal Services.
To help speed up design, development and manufacturing processes, the DIB is looking for new tools – with the one showing the most potential being artificial intelligence (AI) to speed up processes, streamline workflows and churn through massive amounts of data to enable better decision making.
“When you look at the immediate constraints, it almost always comes down to capacity,” said Amy Bahrani, Managing Director, AI and Data, Defense Industrial Base Lead, Accenture Federal Services. “It’s the material constraints, space constraints, and workforce shortages putting pressure on the system from every angle. To break through that pressure, we must stop treating the DIB as a reactive or even passive supply chain function.”
The digital DIB
Manufacturing processes such as digital twins, modeling and robotic automation are not new, and are ubiquitous across the DIB. However, what is new is how AI is enabling these processes to speed up design and manufacturing.
Designers and engineers can quickly identify problems in designs and correct them in a virtual setting instead of having to rebuild a system or component on a physical model. Then, once optimal configurations are identified, they can be built and tested in the real world.
“When you can securely share cross-domain data and run high-performance simulations, you unlock the ability to field advanced military technology at commercial speed and industrial scale,” said Bahrani. “That is how you build a resilient ecosystem where industry partners can see their innovations reaching the frontline faster.”
Putting this into practice requires secure, cloud-based data infrastructure to allow for sharing and distribution of information. Data is only as good as its accessibility, so being able to share it across design or manufacturing sites and with other members of teams is key to ensuring everyone is working from the same single source of information.
The role of people in these processes matters, as well. While AI can reduce much of the workload, it still requires humans to make final decisions and review the data being produced to ensure it is feasible and reliable. AI models depend on good data to reach their full potential, and having humans as a check throughout the process – not just at the end – is needed to ensure that the information being produced is useful.
“It’s not enough to drop information into data lakes or insert a human into a legacy business process at discrete points,” said Olson. “The most effective AI-powered operations are redesigned from the ground up with humans in the lead. You need real-time data to give leaders decision advantage, but humans must be positioned from the start to steer the system, not just validate a machine’s work.”
Speeding up the supply chain
Supply chains are only as strong as their weakest link, which has been repeatedly proven to be true over the last few years. Whether it’s shortages of materials such as rare earth minerals or computer chips, personnel shortages or disruptions such as the ongoing battle over accessing the Strait of Hormuz, supply chains can be dangerously fragile.
Surges in demand due to materiel support for Ukraine or expenditures of munitions in Iran have put added strain on supply chains, and meeting both current and future needs will require a rapid ramp-up of not just manufacturing, but also of sourcing materials and components needed to build munitions such as air-and-missile-defense (AMD) interceptors. Component shortages and rising costs are only complicating the equation, as massive capital expenditures are needed to both increase manufacturing capacity and obtain the necessary materials.
In a recent report by Accenture, the company identified three main reasons for a widening gap between demand and delivery:
- Supply chains built for peacetime are buckling under surge demand;
- Military requirements (e.g., autonomy and counter-drone capabilities) are evolving faster than industrial processes can respond;
- Order volumes are exceeding current delivery capacity in every major defense system category.
All of these constraints can slow production, and consolidation within suppliers and more competitors entering the defense market are contributing, as well, said Bahrani.
“You might have a bottleneck where there are four applications that all have a dual-use product, and it might be for applications across four different OEMs,” she said. “But you’re relying on one company or a small set of companies to be able to feed that supply chain, so that can be extremely hard.”
Reinforcing supply chains is an area where AI can help, as well. AI tools can map supply chains to trace materials and components, as well as identify potential bottlenecks and alternate suppliers. By speeding up the identification of possible disruptions, companies within the DIB can avoid or mitigate them, cutting down on the risk of a supply chain issue bringing production to a halt.
People power
While AI is a powerful tool, it can’t do everything – nor should it. People will continue to play a lead role in the DIB at every step of the way. But how people are doing their jobs will change, and new skillsets will be needed. Familiarity with digital tools is non-negotiable. That may require retraining or a different way of thinking for how to prepare people for manufacturing or design roles within the DIB, and overhauling that preparation is something companies must be focused on to be successful as the role of AI only continues to grow.
In some cases, that means companies must adapt to the tech savviness of their employees and not the other way around.
“The younger generation entering the workforce grew up in a real-time data environment,” said Olson. “They are incredibly sophisticated with all things digital. If they step onto a submarine or a shipyard floor and find themselves cut off from basic data access, they are underutilized. We must give them the intuitive, modern tools that match their aptitudes, so they are empowered to solve problems on the fly.”
An employee entering the workforce now has likely been using tools such as tablets and smartphones their entire lives, both in their personal lives and in education. That shortens the learning curve for using those tools in a manufacturing or design environment. But to take full advantage of that, companies need to be focused on providing digital tools that employees can use to be efficient instead of being forced to go through traditional training or use paper documentation.
“If a technician encounters an error code they’ve never seen on an assembly line, they shouldn’t have to halt production or track down a supervisor,” said Bahrani. “We are deploying AI-powered systems that ingest standard operating procedures, documentation, and blueprints in real time. The technician can ask a question, get the exact fix surfaced instantly, and keep the line moving.”
The big picture
The DIB finds itself at a critical point, as demand signals are rapidly changing, new tools are disrupting long-standing processes and new competitors flood the market. To meet these challenges, companies must be willing to embrace technologies such as AI to be faster, agile, and more efficient. Doing so will be critical to ensuring that warfighters have the tools they need to succeed not only today, but in the rapidly changing landscape that tomorrow will bring.
“We need both government and industry to accelerate their own internal operations into an ‘Intelligent Enterprise’ so they can collectively support a true ‘National Enterprise,’” said Bahrani. “That means driving end-to-end data integration with agentic AI across the entire product lifecycle – from initial ideation and design through engineering, manufacturing, and field service.”