This chapter presents the first diagnostic iteration per the elaborated action design research (eADR) approach and aims to identify and refine key performance indicators (KPIs). The first cycle involved discussions with mid-level managers to identify relevant KPIs across primary and support functions, classifying them into leading and lagging metrics. The second cycle refined and validated these metrics through validation with senior management, ensuring strategic alignment and relevance. A structured decision tree framework was developed to map the performance metrics to organizational decision variables, incorporating a thematic analysis approach. The findings underscore the need for a dynamic artificial intelligence (AI) decision-support model that considers both internal and external performance drivers. The final output, the _DecisionArtifact, consolidates these elements into a strategic framework and advances actor-network theory (ANT) interessement progression by identifying key actor roles and their influence in shaping the AI decision-support model.