COLLABORATION ON INTEGRATION
Soldiers and Army civilians collaborate to integrate AI with continuous process improvement, enhancing operational efficiency and decision-making across the force. (Image by Denise Kovalevich, Office of the Army Chief Information Officer)
Integrating AI with continuous process improvement in the U.S. Army.
by Charles T. Brandon III, DBA
The U.S. Army is continuously evolving to enhance its operational efficiency, reduce costs and improve decision-making processes across the enterprise. One strategy to achieve this mission is through the integration of artificial intelligence (AI). By automating tasks, analyzing vast datasets and providing predictive insights, AI has the potential to streamline workflows, reduce redundancies and enhance readiness.
However, it is crucial to recognize that AI and automation can only be effective if the underlying processes have been initially optimized through formal process improvement (PI) methodologies. These practices, which seek to reduce costs and processing time, while increasing quality from the perspective of both the organization and its customers, are instrumental in ensuring that strategic objectives are met with greater precision.
In today’s AI-era, it is critical to understand that PI methods and tools are no longer limited to yellow, green or black belts working manufacturing or transactional PI projects; anyone in the AI space needs to be acutely aware of the power of its tools and critical touch points in deploying effective AI. PI methods should be used as a way to lower or offset the cost of AI deployment through reduction of waste, process lead time and internal process costs.
The following explores the potential applications of AI and PI within the U.S. Army by examining specific use cases and addressing potential challenges and ethical considerations.
PROCESS IMPROVEMENT TOOLS AND METHODS
AI holds transformative potential across Army operations—from logistics and maintenance to intelligence and cybersecurity—but its success depends on first establishing strong, efficient processes through continuous process improvement (CPI).
For example, in logistics and warehousing, AI can forecast demand and automate procurement, yet maximum benefits are realized only after streamlining workflows using tools like define, measure, analyze, improve and control, and well as lean tools. These foundational efforts help eliminate waste and ensure efficiency before AI is introduced. Similarly, predictive maintenance and supply chain risk management become more effective when supported by standardized procedures and analyses such as failure modes and effects analysis.
CONTINUOUS PROGRESSION
A visual progression of AI integration in Army process improvement—from co-pilot tools enhancing personal productivity to agentic AI enabling autonomous, dynamic operations—demonstrating how continuous process optimization supports increasingly advanced capabilities. (Image by the author)
AI could also revolutionize training by creating realistic simulations, providing personalized feedback and automating administrative tasks, leading to more effective training programs at a lower cost. However, maximum potential will be discovered when AI-driven personalization and simulation is paired with programs like Voice of the Customer feedback and Design of Experiments that ensure accurate and useful scenarios. Intelligence and decision-making processes also gain value from AI when data is first organized and standardized, resulting in better data quality and more actionable insights.
Meanwhile, AI enhances threat detection, anomaly recognition and report generation only when built on optimized analytic workflows. In personnel management, AI aids recruitment and career development, but success hinges on prior PIs using stakeholder analysis and roadmap tools like swim-lane maps. Predictive analytics can assess readiness across health, training and performance metrics but should be preceded by improvements using tools like Kano analysis to ensure maximum benefit.
Finally, AI is becoming a crucial tool in strengthening cybersecurity, with capabilities like detecting intrusions, analyzing malware and assessing vulnerabilities. However, its effectiveness relies on prior PIs that establish strong baseline practices in network security and incident response. By integrating AI with PI methods—such as mistake proofing for intrusion detection, statistical control for malware analysis and Design for Six Sigma for vulnerability assessments—the Army can significantly enhance its cybersecurity defenses, ensuring more efficient, accurate and long-lasting protection against threats.
Together, these examples demonstrate how combining AI with CPI could lead to more reliable, cost-effective and sustainable outcomes across defense operations.
CHALLENGES AND ETHICAL CONSIDERATIONS
AI has the capability to revolutionize modern warfare by offering enhanced decision-making, automation and operational efficiency. However, these advancements also introduce significant challenges and ethical concerns if not properly addressed. AI development and deployment in the Army must be grounded in several foundational principles to ensure effectiveness, fairness and accountability. For instance, data quality and availability are critical, as AI algorithms depend on large volumes of high-quality information for training and operation. This requires the implementation of strong data governance frameworks, the definition of clear data quality metrics and ongoing efforts such as data profiling, cleansing and the use of AI tools for continuous monitoring and validation.
Interpretability and transparency are also essential. Understanding how AI systems reach their conclusions is crucial for building trust and ensuring responsible use. The problem of “black box” AI, where decision-making processes are unclear or opaque, can be especially concerning in high-stakes situations. To address this issue, the use of simpler, more interpretable models—such as decision trees, linear regression and rule-based systems—should be prioritized, along with explainable AI techniques and feature importance analysis to enhance transparency.
MAPPING A PLAN
A draft process map sits on a desk in the U.S. Army Financial Management Command headquarters in Indianapolis, Indiana, on May 13, 2019. U.S. Army Financial Management Command’s Business Process Management directorate completed a three-year mission of documenting and standardizing all of the Army’s business processes impacting financial statements on October 1, 2020. (Photo by Mark R.W. Orders-Woempner, U.S. Army)
Bias and fairness present another major challenge, as AI systems can inherit and amplify biases present in the training data. Addressing this requires careful curation of diverse datasets, implementation of bias detection tools and the establishment of mechanisms for continuous monitoring and human oversight to safeguard against unfair or discriminatory outcomes. Finally, ethical considerations in autonomous systems must be carefully managed. As AI evolves, the possibility of autonomous weapons systems introduces serious ethical concerns regarding human control and accountability. Ensuring fairness, transparency and human oversight throughout the AI life cycle is essential, along with enforcing robust data protection protocols, security measures and continuous auditing to uphold ethical standards in all applications.
CONCLUSION
AI has the potential to revolutionize the U.S. Army by improving efficiency, enhancing decision-making and strengthening national security. However, it is essential to recognize that AI and automation can only be effective if the underlying processes have been optimized through formal PI methodologies. By addressing the challenges and ethical considerations associated with AI implementation, the Army can harness the full potential of this transformative technology to maintain its competitive edge. Strategic investment in AI research and development, coupled with a focus on responsible AI development and deployment, will be critical for realizing the full benefits of AI-powered PI within the U.S. Army.
For more information, contact the author at charles.t.brandon.civ@army.mil.
CHARLES T. BRANDON III, DBA, is the Army director for business process improvement and reengineering in the Office of the Chief Information Officer at the Pentagon in Washington, D.C. He holds a DBA in quality systems management from the National Graduate School of Quality Management, an M.S. in information technology from the American InterContinental University and a B.S. in economics from Alabama Agricultural and Mechanical University. The views expressed are his own and do not necessarily represent the opinions of the U.S. Army or DOD.
