Smart Arsenal: The Role of AI in Military Asset Management

The military can leverage AI to deliver timely, accurate data and make decisions that enhance operational readiness and effectiveness.

In brief

• GenAI enhances the military's ability to act swiftly and effectively, strengthening readiness across various situations.
• Federal agencies have established policies and frameworks to govern the development, testing and integration of AI applications.
• Preparing for the future of AI in asset management requires a multifaceted approach that combines education, experience and a supportive innovation culture.

Imagine a forward operating base that autonomously receives essential equipment and medication, driven by an algorithm finely tuned to anticipate the demand for medical supplies. In the high-stakes world of contested operational environments, effective asset management is not just a logistical challenge — it directly impacts the performance and safety of those on the ground.

For decades, traditional approaches to military asset management have been stymied by an array of antiquated systems. These systems are often siloed, cumbersome and plagued by inaccuracies, relying on outdated data that fails to reflect the dynamic needs of modern warfare. The Department of Defense (DoD) has historically been hampered by a lack of advanced analytics and predictive modeling capabilities, tools that are crucial for making swift, informed decisions in the field. This fragmentation does more than slow down critical operations; it complicates the already intricate process of ensuring audit readiness. The ripple effects of these inefficiencies extend beyond the battlefield, making it difficult for the military to uphold accountability and maintain a robust financial management posture.

Foreign governments are rapidly adopting new technologies, including artificial intelligence (AI), potentially undermining our status as the world’s preeminent fighting force. AI, which has recently become a vital tool in health care, finance and transportation, holds transformative potential for military asset management. A fully integrated, AI-driven system could enhance the military’s accuracy, precision and efficiency, significantly boosting financial management and operational readiness.

As the complexities of modern warfare evolve, so too must the tools and systems that support our armed forces. Identifying and articulating clear use cases for AI applications is crucial in this evolution, as it enables targeted development and effective integration of these technologies. The integration of advanced analytics and predictive models could signal a new era for military logistics, where the ability to foresee and respond to needs in real time ensures that our troops are better equipped and prepared for the challenges they face. By focusing on specific, actionable use cases, the military can leverage AI to deliver timely, accurate data and make informed decisions that enhance operational readiness and effectiveness.

Understanding AI in federal asset management

AI will become invaluable to asset management due to its capacity to enhance asset allocation and limit waste. Machine learning (ML) models are particularly beneficial as they enable computers to learn from data, progressively improving performance. This adaptability is crucial for decision-makers who can leverage AI and ML to implement predictive analytics, forecasting future needs based on both historical data and ongoing trends. Predicting maintenance on military assets is a particularly attractive use case for this approach.

Furthermore, AI can streamline inventory management in federal asset pools by predicting demand surges and optimizing stock levels, thus balancing instances of shortages and overstock situations. Document intelligence aids this process by accurately interpreting inventory documents and historical usage reports, ensuring that relevant information is considered in predictive models. Additionally, AI-driven algorithms can enhance logistical planning by optimizing routes and schedules for transportation assets, which is critical for promoting timely delivery and operational readiness. Document intelligence supports these efforts by providing real-time analysis of logistical documents, such as shipping manifests and transportation schedules, further refining the planning and execution processes.

Incorporating these approaches into AI-driven asset management not only improves operational efficiency but also enhances compliance and audit processes. By ensuring that information is accurately processed and analyzed, organizations can maintain better records, make more informed decisions, demonstrate better control to auditors, and uphold higher standards of accountability.

Looking ahead, the potential applications of GenAI in military asset management are vast. GenAI can be employed for detailed scenario planning, where AI generates multiple outcomes based on varying conditions to aid in strategic decision-making. Additionally, it can be used to develop comprehensive military strategies, tailoring approaches that dynamically adjust to changing battlefield conditions. Furthermore, GenAI has the capability to revolutionize training and simulation environments by creating highly realistic and adaptable scenarios that prepare military personnel for a wide range of operational situations.

These innovations are not just about technological advancement; they are crucial for improving operational readiness. By promoting rapid adaptation and response in dynamic environments, GenAI enhances the military's ability to act swiftly and effectively, strengthening readiness across various situations. This fusion of AI with military asset management opens new avenues for tactical and strategic superiority, fundamentally reshaping how military operations are conducted in the digital age.

Responsible AI is especially important in asset management

While AI for military asset management holds significant potential, establishing and adhering to responsible AI principles is critical. AI systems, along with their inputs and operations, can be opaque, posing unique challenges to governance and oversight — not just in the public sector. To address these challenges, federal agencies have established policies and frameworks to govern the development, testing and integration of AI applications.

In 2020, the Department of Defense (DoD) released its five ethical AI principles, designed to guide the development and deployment of military AI capabilities. An AI-driven military asset management system must adhere to these principles to ensure ethical and effective operations.²

By following these ethical principles, an AI-driven military asset management system can not only limit decision-making biases but also promote processes that are interpretable and justifiable. This enhances trust in AI systems, facilitates smoother integration with existing operations, and supports compliance with regulatory and audit requirements. Responsible AI ensures that technological advancements contribute positively to military effectiveness and operational readiness while upholding ethical standards.

Technical challenges and industry support

Implementing AI for military asset management presents many technical challenges that necessitate strong partnerships with industry leaders. As the DoD moves forward with AI adoption, it encounters substantial obstacles in two primary areas: integrating AI with legacy systems and infrastructure, and scaling AI solutions to meet the vast scope of military operations. Overcoming these hurdles is crucial to fully leveraging AI’s potential.

Integrating AI with existing legacy systems is a formidable task: military infrastructure, often built decades ago, was not designed with modern AI technologies in mind. This creates compatibility issues that require sophisticated solutions to promote better integration. For instance, connecting advanced AI algorithms with outdated or extremely custom databases or communication networks demands custom interfaces and middleware, which can demand significant time and resources.³

Further, the DoD faces the challenge of scaling AI solutions across its extensive and diverse operations in many geographical parts of the world. Unlike private sector companies, which often operate within relatively uniform environments, the military's operational landscape is vast and varied, encompassing everything from logistics and supply chains to battlefield operations. Scaling AI to operate effectively across such diverse settings, compounded with a suite of legacy systems, requires robust, adaptable algorithms capable of handling the complexity and unpredictability inherent in military contexts.

Industry partners can facilitate knowledge transfer and technological innovation, accelerating the DoD’s AI capabilities. By engaging the private sector's experience with large-scale AI implementations, the DoD can adopt more efficient methods for data processing, ML model training, and real-time analytics. This collaborative approach not only enhances the DoD's technical proficiency but also ensures that AI systems are developed with a focus on security, ethics and governance.

Evidently, the successful implementation of AI in military asset management hinges on overcoming significant technical challenges. Trusted industry partnerships are essential to navigate these complexities, providing the DoD with the expertise and resources necessary to integrate and scale AI solutions effectively. Through such collaborations, the DoD can harness the potential of AI, driving innovation and improving operational efficiency across its broad landscape.

Preparing for the future of AI in asset management

As AI technologies continue to evolve, the potential military applications will expand significantly. To stay ahead of emerging trends, such as AI in the cloud, the DoD should proactively equip military personnel with the necessary knowledge and tools to foster success. This preparation involves implementing comprehensive AI training programs, hosting workshops, and developing competency frameworks specifically tailored for financial management roles.

AI training programs are essential for building a knowledgeable workforce capable of leveraging advanced technologies. By offering specialized courses and certifications in AI and ML, the DoD can cultivate a workforce that is well-versed in the latest developments and applications. These programs should cover a wide range of topics, including data analytics, predictive modeling, and AI ethics, providing a robust foundation for military personnel to apply AI effectively in their roles.

Workshops and hands-on training sessions further enhance this education by providing practical experience with AI tools and techniques. These interactive sessions allow personnel to work directly with AI systems, gaining valuable insights into their operation and potential applications. Workshops can also facilitate collaboration and knowledge sharing, fostering a community of practice where personnel can learn from each other’s experiences and challenges.

In addition to formal training, developing competency frameworks is crucial for standardizing AI knowledge and skills across the DoD. These frameworks can define the specific competencies required for various financial management roles, promoting consistent proficiency across DoD personnel. Competency frameworks also help to identify skill gaps and guide professional development efforts, ensuring that the workforce remains adept at integrating AI into asset management processes.

Equally important is fostering a culture of innovation within the DoD. Establishing innovation labs and AI centers of excellence can serve as hubs for experimentation and development. These centers can bring together experts from various fields to collaborate on cutting-edge AI projects, driving technological advancements and operational improvements. Innovation labs provide a test environment for new ideas and for refining AI applications before they are deployed on a larger scale.

Moreover, promoting a culture of continuous improvement and adaptability is essential. Encouraging personnel to stay informed about the latest AI trends and advancements will help the DoD remain agile and responsive to technological changes. Regularly updating training programs and competency frameworks ensures that the workforce’s skills remain relevant and up to date, allowing the DoD to keep pace with other services, counterparts and countries.

Preparing for the future of AI in asset management requires a multifaceted approach that combines education, practical experience, and a supportive innovation culture. By investing in training programs, workshops, and competency frameworks, and fostering innovation through dedicated centers, the DoD can ensure that its personnel are ready to harness the full potential of AI. This proactive preparation will enable the DoD to stay ahead of technological trends, maintain its operational edge, and enhance the effectiveness of its asset management processes and equipment.

Conclusion

A key approach in this modernization is the identification and articulation of specific use cases for AI, which provide clear direction for development and integration efforts. By focusing on targeted applications, the military can leverage advanced analytics and machine learning to anticipate logistical needs, improve data accuracy, and streamline operations. The collaboration between the Joint Artificial Intelligence Center and various military branches exemplifies the practical benefits of adopting AI, such as enhancing maintenance accuracy and operational efficiency. However, the successful implementation of AI hinges on a commitment to responsible AI principles, rigorous training, and robust partnerships with industry leaders. Preparing for future challenges involves a multifaceted approach that emphasizes education, innovation, and ethical standards.

The views reflected in this article are the views of the author(s) and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.

Emma Rhodes, Senior, Ernst & Young, LLP, co-authored this article.