A Conceptual Framework for Ai-Driven Industrial Management: Integrating Dynamic Capabilities and Organizational Readiness

Abstract

Artificial intelligence (AI) is increasingly positioned as a central lever of industrial management, yet a large share of AI initiatives in manufacturing and operations settings fail to move beyond pilot stage. Existing explanations draw mainly on technology-adoption models, which treat AI implementation as a discrete adoption decision rather than as an ongoing organizational process. This conceptual paper addresses that limitation by integrating dynamic capabilities theory with the construct of organizational readiness for change to explain how industrial firms convert AI investment into sustained managerial and operational advantage. Following an integrative conceptual-article approach, the paper compares dynamic capabilities theory against competing lenses (the resource-based view, the technology-organization-environment framework, and technology-acceptance models), justifies an integrative choice, and develops a framework in which the microfoundations of dynamic capabilities (sensing, seizing, and transforming) operate through organizational readiness (change valence, change efficacy, and change commitment) to shape AI-driven industrial management capability and, in turn, operational and managerial performance outcomes. Six theory-grounded propositions are advanced, alongside a construct-definition table and a conceptual model. The framework contributes to the literature by repositioning organizational readiness as a dynamic, renewable capacity embedded within the seizing and transforming microfoundations rather than as a static precondition for adoption, and by offering industrial managers a process-based diagnostic for sequencing AI capability development. Limitations, boundary conditions, and an agenda for empirical validation, including multi-case and survey-based tests, are discussed.