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.


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72  |  Page 73  |  Page 74  |  Page 75  |  Page 76  |  Page 77  |  Page 78  |  Page 79  |  Page 80  |  Page 81  |  Page 82  |  Page 83  |  Page 84  |  Page 85  |  Page 86  |  Page 87  |  Page 88  |  Page 89  |  Page 90  |  Page 91  |  Page 92  |  Page 93  |  Page 94  |  Page 95  |  Page 96  |  Page 97  |  Page 98  |  Page 99  |  Page 100