Artificial Intelligence/Machine Learning

​The United States currently faces an “unprecedented” military buildup by China and increasing air and missile threats that demand a robust deterrent. However, the current inventory of long-range strike weapons is dominated by high-cost, low-volume systems or aging strategic assets like the Minuteman III, which dates back to the 1960s. The lack of a cost-effective, ground-launched payload delivery system prevents the Joint Force from achieving producibility at scale, leaving the U.S. vulnerable in high-intensity, multi-domain environments where high-end munitions would be rapidly depleted. The objective of this topic is

Develop an AI-enabled predictive logistics software solution, to be used at the Brigade level and below, capable of operating in contested environments, providing real-time tracking, predictive analysis, and AI-generated Courses of Action (COAs) to optimize logistical flow and facilitate mission success.  

The aim of this topic is to harness advanced artificial intelligence/machine-learning (AI/ML) technologies to enhance The Integrated Personnel and Pay System- Army (IPPS-A) by achieving the following objectives: 

Human Resources/Pay Data Quality Enhancement: Ensure high data integrity and reliability and develop generative AI or ML solutions to identify and rectify data inaccuracies, inconsistencies, and complete missing information. 
HR/Pay Anomaly Detection and Prevention: Implement AI-driven mechanisms to detect anomalies, prevent data duplication, and ensure ongoing data quality improvement, adapting to evolving operational needs.