The integration of Artificial Intelligence in Business Intelligence systems has fundamentally transformed enterprise analytics capabilities, enabling sophisticated pattern recognition, predictive modeling, and automated decision-making processes. However, the opaque nature of many AI algorithms presents significant challenges in business contexts where transparency, accountability, and regulatory compliance remain paramount concerns. This comprehensive technical review examines the role of Explainable AI in addressing these critical challenges, providing detailed insights into current methodologies, implementation frameworks, and practical applications across enterprise analytics environments. The content explores theoretical foundations distinguishing interpretability from explainability, emphasizing their crucial roles for different stakeholder groups within organizations. Technical frameworks encompass model-agnostic and model-specific methods, including LIME, SHAP, and attention mechanisms, alongside implementation tools ranging from open-source libraries to enterprise platforms. Real-world applications demonstrate XAI effectiveness across financial services, healthcare, retail, manufacturing, and human resources sectors, highlighting regulatory compliance benefits and stakeholder trust improvements. Current challenges include computational complexity, explanation fidelity, multi-modal data integration, and scalability issues, while emerging trends focus on automated explanation generation, interactive interfaces, and causal reasoning methods. Regulatory and ethical considerations address compliance evolution, bias detection, and fairness metrics, while technical advancements explore foundation model interpretability and privacy-preserving techniques.

Explainable Artificial Intelligence, Business Intelligence, Enterprise Analytics, Model Interpretability

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