AI-Driven Technology Management Frameworks for Automating Complex Engineering Systems

Abstract

This study develops a theoretically grounded framework explaining how artificial intelligence (AI)-driven technology management capabilities enable decision automation, governance enhancement, and performance optimization in complex engineering systems. The research resolves prevailing ambiguities regarding the distinction between engineering task automation and managerial decision automation.Grounded in recent reinterpretations of Systems Theory, Control Theory, the Resource-Based View (RBV), and Socio-Technical Systems (STS) Theory, the study employs conceptual synthesis followed by a rigorous quantitative research design. AI-driven technology management capability (AI-TMC) is conceptualized as a formative higher-order construct comprising technical infrastructure, governance routines, and adaptive learning. The research design utilizes Partial Least Squares Structural Equation Modeling (PLS-SEM) with a target sample of 350–400 engineering managers and system architects across two temporal waves to mitigate common method bias.