An AI-Based Conceptual Framework to Improve Program Management of Complex Systems

With evolving technologies, changing requirements, and limited budgets, governments and industries need to consider new methodologies to help streamline program lifecycle management, from cradle to grave, to ensure that projects are delivered on time, on budget, and to the expected performance standards. Traditional approaches fail to adequately address the added complexities of system of systems’ programs, such as integration, interoperability, and variable lifecycle of subcomponents. The objective of this study is to assess and address the research question—can a new acquisition approach be designed to address and improve program lifecycle management of complex systems? A comparison study, using the modified Delphi method, of this new approach to the traditional legacy systems engineering approaches of the agile framework, evolutionary acquisition, and modular open systems approach was conducted to validate the conceptual framework. The genetic acquisition strategy establishes an improved methodology to embrace uncertainty while focusing not only on the quantitative technology deployment but also on the system's qualitative user adoption, thereby reducing the risk to achieve cost, schedule, and performance objectives of a complex program. This ensures that any implemented technology remains “best-in-class” throughout the lifecycle of a program to address the gaps and historical failures identified through qualitative research.