In today’s data-driven business landscape, effective and transparent decision making becomes relevant to maintain a competitive advantage over the competition, especially in customer service in B2B environments. This study presents a methodological framework that integrates Explainable Artificial Intelligence (XAI), C-means clustering, and the Analytic Hierarchical Process (AHP) to improve strategic decision making in business environments. The framework addresses the need to obtain interpretable information from predictions based on machine learning processes and the prioritization of key factors for decision making. C-means clustering enables flexible customer segmentation based on interaction patterns, while XAI provides transparency into model outputs, allowing support teams to understand the factors influencing each recommendation. The AHP is then applied to prioritize criteria within each customer segment, aligning support actions with organizational goals. Tested with real customer interaction data, this integrated approach proved effective in accurately segmenting customers, predicting support needs, and optimizing resource allocation. The combined use of XAI and the AHP ensures that business decisions are data-driven, interpretable, and aligned with the company’s strategic objectives, making this framework relevant for companies seeking to improve their customer service in complex B2B contexts. Future research will explore the application of the proposed model in different business processes.