RT Journal Article
T1 A methodological framework for business decisions with explainable AI and the analytic hierarchical process
A1 Marín Díaz, Gabriel
A1 Gómez Medina, Raquel
A1 Aijón Jiménez, José Alberto
AB 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.
PB MDPI
SN 2227-9717
YR 2025
FD 2025-01-03
LK https://hdl.handle.net/20.500.14352/129799
UL https://hdl.handle.net/20.500.14352/129799
LA eng
NO Marín Díaz, G., Gómez Medina, R., & Aijón Jiménez, J. A. (2025). A Methodological Framework for Business Decisions with Explainable AI and the Analytic Hierarchical Process. Processes, 13(1), 102. https://doi.org/10.3390/pr13010102
NO SIN FINANCIACIÓN
DS Docta Complutense
RD 21 mar 2026