The gap between demand and delivery is widening. AI can help close it.

Shaping the future of military logistics, DLA Director Lt. Gen. Mark Simerly and DLA Troop Support Commander Brig. Gen. Sean Kelly met with senior leaders from across DLA Troop Support's four supply chains. Their strategic conversation centered on modernization and integrating artificial intelligence to improve the efficiency and responsiveness of DLA's support to the Warfighter. (Photo by Kendall Swank/Defense Logistics Agency.)

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.”

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.

On August 25-27, 2026, the Office of the Assistant Secretary of War for Industrial Base Policy hosted the inaugural Defense Industrial Base Accelerator (DIBX). Warfighters, industry leaders, and allied partners met together in Philadelphia in pursuit of a defense investment revolution. (Courtesy photo.)

“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.

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 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.”

为了共同塑造军事后勤的未来，国防后勤局局长马克·西默利中将和国防后勤局部队支援司令肖恩·凯利准将与国防后勤局部队支援部门四大供应链的高级领导举行了会晤。他们的战略对话围绕现代化和人工智能的整合展开，旨在提高国防后勤局对作战人员支援的效率和响应速度。（肯德尔·斯旺克/国防后勤局摄）

国防工业基地（DIB）正面临多重压力。由于与伊朗的冲突，国防储备正在消耗殆尽。诸如“金穹顶”之类的项目规模庞大，需要专业且复杂的材料和部件。而持续的供应链短缺也威胁着所有这些项目的进展。

埃森哲联邦服务公司海军和海军陆战队业务主管总经理安娜·加西亚·奥尔森表示：“我们面临的现实是，我们建造军事平台的传统方式——将一个系统从白板设计到投入作战部署需要数年，有时甚至十年——是一种战略负担。”

为了帮助加快设计、开发和制造流程，DIB 正在寻找新的工具——其中最有潜力的工具是人工智能 (AI)，它可以加快流程、简化工作流程并处理大量数据，从而实现更好的决策。

埃森哲联邦服务公司人工智能与数据董事总经理、国防工业基地负责人艾米·巴赫拉尼表示：“当你审视眼前的制约因素时，几乎总是产能不足。物资短缺、空间限制和劳动力短缺从各个方面给整个系统带来了压力。为了突破这种压力，我们必须停止将国防工业基地视为被动的供应链环节。”

数字孪生、建模和机器人自动化等制造工艺并非新生事物，它们在数字工业基地（DIB）中已得到广泛应用。然而，人工智能如何赋能这些工艺以加速设计和制造，才是真正的新变化。

设计师和工程师可以快速识别设计中的问题，并在虚拟环境中进行修正，而无需在物理模型上重建系统或组件。之后，一旦确定了最佳配置，就可以在现实世界中进行构建和测试。

2026年8月25日至27日，美国陆军部工业基础政策助理部长办公室主办了首届国防工业基础加速器（DIBX）会议。来自作战人员、行业领袖和盟国伙伴齐聚费城，共同探讨国防投资变革。（图片由相关机构提供。）

巴赫拉尼表示：“当能够安全地共享跨领域数据并运行高性能模拟时，就能以商业速度和工业规模部署先进的军事技术。这才能构建一个具有韧性的生态系统，让行业合作伙伴的创新成果更快地应用于前线。”

要将这一切付诸实践，需要安全可靠的云端数据基础设施，以实现信息的共享和分发。数据的价值取决于其可访问性，因此，能够在设计或制造基地之间以及与团队其他成员共享数据，是确保每个人都基于同一信息源开展工作的关键。

在这些过程中，人的作用同样至关重要。虽然人工智能可以大大减轻工作量，但仍然需要人类做出最终决策，并审核生成的数据，以确保其可行性和可靠性。人工智能模型需要高质量的数据才能充分发挥其潜力，因此，在整个过程中（而不仅仅是在最后阶段）都需要人类进行审核，以确保生成的信息真正有用。

奥尔森表示：“仅仅将信息存入数据湖或在传统业务流程的个别环节安排人员参与是不够的。最有效的AI驱动型运营是从根本上重新设计，并由人主导。你需要实时数据来帮助领导者做出决策，但必须从一开始就安排人来引导系统，而不仅仅是验证机器的工作。”

加快供应链

供应链的强度取决于其最薄弱的环节，过去几年已经反复证明了这一点。无论是稀土矿物或计算机芯片等原材料短缺、人员短缺，还是像持续不断的霍尔木兹海峡通行权争夺战这样的中断，供应链都可能极其脆弱，存在安全隐患。

由于对乌克兰的物资援助或对伊朗的弹药采购，需求激增，给供应链带来了更大的压力。为了满足当前和未来的需求，不仅需要迅速提高生产能力，还需要快速采购制造弹药（例如防空反导拦截器）所需的材料和零部件。零部件短缺和成本上涨使情况更加复杂，因为需要巨额资本支出来提高生产能力和获取必要的材料。

埃森哲公司最近的一份报告指出，需求与交付之间差距不断扩大的主要原因有三点：

为和平时期建立的供应链在激增的需求面前不堪重负；

军事需求（例如自主性和反无人机能力）的发展速度超过了工业流程的响应速度；

在所有主要国防系统类别中，订单量均已超过目前的交付能力。

巴赫拉尼表示，所有这些限制都会减缓生产速度，供应商内部的整合以及更多竞争对手进入国防市场也是造成这种情况的原因。

她表示：“你可能会遇到瓶颈，比如有四个应用场景都使用双用途产品，而且这些应用场景可能来自四个不同的原始设备制造商（OEM）。但你却依赖一家或几家公司来维持供应链的运转，这会非常困难。”

强化供应链也是人工智能可以发挥作用的领域。人工智能工具可以绘制供应链图，追踪原材料和零部件，并识别潜在的瓶颈和备选供应商。通过加快识别潜在的供应链中断，DIB（迪拜工业园区）内的企业可以避免或减轻这些中断的影响，从而降低供应链问题导致生产停滞的风险。

人工智能固然强大，但它并非万能，也不应该如此。在数字基础设施建设的各个阶段，人仍将发挥主导作用。但人们的工作方式将会发生改变，也需要新的技能。

熟悉数字化工具是必不可少的。这可能需要对员工进行再培训，或者需要转变思路，才能让他们胜任数字基础设施建设（DIB）中的制造或设计岗位。随着人工智能的作用日益增强，企业必须重视并改进这种培训方式，才能取得成功。

在某些情况下，这意味着公司必须适应员工的技术水平，而不是反过来。

奥尔森说：“进入职场的年轻一代成长于实时数据环境中。他们精通各种数字化技术。如果他们踏入潜艇或造船厂的车间，却发现自己无法访问基本数据，他们的才能就无法得到充分发挥。我们必须为他们提供符合其能力的直观、现代化的工具，让他们能够随时解决问题。”

如今进入职场的员工很可能从小就使用平板电脑和智能手机等工具，无论是在个人生活还是学习中。这缩短了他们在制造或设计环境中使用这些工具的学习曲线。但要充分利用这一优势，企业需要专注于提供员工能够高效使用的数字化工具，而不是强迫他们接受传统培训或使用纸质文档。

“如果技术人员在装配线上遇到从未见过的错误代码，他们不应该被迫停止生产或到处找主管，”巴赫拉尼说。“我们正在部署人工智能系统，该系统可以实时接收标准操作规程、文档和蓝图。技术人员可以提出问题，立即获得准确的解决方案，从而保证生产线继续运转。”

国防工业基地 (DIB) 正处于关键时刻，需求信号瞬息万变，新工具颠覆了长期沿用的流程，新的竞争对手也涌入市场。为了应对这些挑战，企业必须勇于拥抱人工智能等技术，从而更快、更灵活、更高效。这样做对于确保作战人员拥有所需的工具至关重要，不仅能够帮助他们在当下取得成功，也能让他们在未来瞬息万变的环境中立于不败之地。

巴赫拉尼表示：“我们需要政府和企业加快内部运营转型，成为‘智能企业’，从而共同支持真正的‘国家企业’。这意味着要推动端到端的数据集成，并在整个产品生命周期中应用智能人工智能——从最初的概念构思和设计，到工程、制造和现场服务。”