This chapter concludes the diagnostics phase and assesses the programmability of the _DecisionArtifact within the context of the artificial intelligence (AI) decision-support model. Building on the socio-technical concepts from the previous chapter, this iteration aligns with action design research’s (ADR) principles, combining practice-inspired realities with theory-based research. It emphasizes a systematic approach to AI-enabled decision-making, evaluating programmability across structured, semi-structured, and non-programmable decision types to highlight the need for a robust dataset. A literature-based dataset design framework was introduced in the first of two elaborated action design research (eADR) cycles, transforming the _DecisionArtifact into a dataset framework, enabling discussions with industry professionals on relevance, usability, and quality. The chapter demonstrates, in the context of the actor-network theory (ANT), the dataset’s effectiveness in bridging the gap between the _DecisionArtifact and an AI environment. Evaluating the dataset on accuracy, completeness, and methodology validated its suitability for programming decision rules. The dataset was refined in the second eADR cycle, guided by industry insights, establishing it as the _DataSetArtifact.