OPTIMIZING MILITARY EFFICIENCY
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)
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 improve- ments using tools like Kano analysis to ensure maximum benefit.
Finally, AI is becoming a crucial tool in strengthening cyber- security, with capabilities like detecting intrusions, analyzing malware and assessing vulnerabilities. However, its effective- ness 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 detec- tion, statistical control for malware analysis and Design for Six Sigma for vulnerability assessments—the Army can significantly enhance its cybersecurity defenses, ensuring more efficient, accu- rate 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.
34 Army AL&T Magazine Summer 2025
CHALLENGES AND ETHICAL CONSIDERATIONS AI has the capability to revolutionize modern warfare by offer- ing enhanced decision-making, automation and operational efficiency. However, these advancements also introduce signifi- cant 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. Tis requires the implementation of strong data governance frameworks, the definition of clear data quality metrics and ongoing efforts such
Ethical considerations in autonomous systems must be carefully managed.
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