<![CDATA[Most developing nations, including Malaysia, still lack a model for the decision-making process that is comprehensive enough to account for a wide variety of potential effects and failures. The implementation of this investigation is crucial for Enterprise Architecture (EA) parameters for Railway Industry (RI) supplier performance that emphasize strategic decision-making processes to help the organizations become more competitive. In response to this need, the research integrates Artificial Intelligence (AI) as an enabler within the EA model to support intelligent and data-driven decision-making. This research has implemented a strategic decision-making process in the RI context and conducted it from a developing country perspective. The study identifies several elements of the decision-making process faced and experienced by the RI and the potential gaps for further observations in adopting the EA model. As a result, a fresh conceptual model enhanced with AI-driven analytics and intelligent decision support was created and assessed. By fulfilling the aims of the study, this research makes important contributions to the RI in terms of the use of EA, aligned with the worldwide standard of the four fundamental EA criteria, and explores the transformative potential of AI integration to accelerate EA adoption. The study's findings will impact both theory and practice, providing a pathway for developing nations to harness AI for strategic advantage and digital maturity.]]>
