This chapter finalizes the empirical design of the artificial intelligence (AI)-enabled decision-support model by summarizing key components, outlining the methodology, and highlighting theoretical, methodological, and practical contributions. The book examines the integration of AI in decision-making within the socio-technical context of Industry 4.0, with a focus on balancing social and technical objectives. Actor-Network Theory (ANT) was applied to examine interactions between human and non-human actors through problematization, interessement, and enrollment phases. The elaborated action design research (eADR) approach was innovatively adapted through multiple diagnostic iterations and a pre-implementation phase, resulting in the creation of the _DecisionArtifact, _SocialArtifact, and _DataSetArtifact. These were integrated into the _DesignModelArtifact and validated to produce the final _ValidatedModelArtifact. The study contributes to theory by demonstrating the adaptability of ANT to AI decision-making, methodologically advancing eADR processes, and practically enhancing managerial decision-making through user inclusion, technology adoption, and transparency in AI models. Despite acknowledging potential technological and contextual limitations, the research promotes future studies on the application of ANT across various industries and the exploration of more advanced AI model implementations. The Validated Model offers a robust and adaptable framework for diverse AI decision-support contexts.