O P T I M I Z I N G MILITARY EFFICIENCY
Integrating AI with continuous process improvement in the U.S. Army.
by Charles T. Brandon III, DBA T
he 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) methodol- ogies. Tese 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 work- ing manufacturing or transactional PI projects; anyone in the AI space needs to be acutely aware of the power of its tools and crit- ical 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.
Te 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.
32 Army AL&T Magazine Summer 2025
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, as well as lean tools. Tese foundational efforts help eliminate waste and ensure effi- ciency before AI is introduced. Similarly, predictive maintenance and supply chain risk management become more effective when supported by standardized procedures and analyses such as fail- ure modes and effects analysis.
AI could also revolutionize training by creating realistic simu- lations, providing personalized feedback and automating administrative tasks, leading to more effective training programs at a lower cost. However, maximum potential will be discov- ered 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. Intel- ligence 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.
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