Explainability in Artificial Intelligence Models for Business Intelligence: Addressing NonTechnical Decision Makers’ Needs

Artificial Intelligence (AI) is transforming Business Intelligence (BI) by helping organizations make better decisions through predictive and prescriptive insights. However, many AI models are complex and difficult to understand, especially for non-technical decision-makers. This creates challenges in trusting and using AI-driven outputs effectively. To address this, we propose an Explainable AI (XAI) framework specifically designed to make AI insights clearer, more transparent, and easier to act on for business users. To address these issues, this article proposes a tailored Explainable AI (XAI) framework specifically designed for BI contexts. The framework incorporates five key components: interpretability guidelines, visualization techniques, natural language processing (NLP) interfaces, bias detection and mitigation tools, and role-specific customization. Conceptual validation through hypothetical scenarios in industries such as telecom, banking, and retail, we demonstrate how the framework reduces confusion, builds trust, and helps decision-makers confidently use AI to improve outcomes. This work relies on secondary data from reputable academic research and industry case studies to propose the framework. While this framework shows significant benefits, we acknowledge some limitations, such as the need for high-quality data, the resources required for initial setup, and frequent updates in fast-changing business environments. Future research should focus on creating advanced visual tools, real-time bias detection systems, and ways to measure the success of explainability frameworks. By bridging the gap between AI’s technical complexity and users’ understanding, this framework empowers decisionmakers to use AI insights effectively, making organizations smarter, more ethical, and more data-driven.