Explainable AI (XAI) for Cloud-Based Enterprise Applications: Building Trust and Transparency in AI-Driven Decisions

Abstract

The proliferation of Artificial Intelligence systems in enterprise cloud environments has generated unprecedented demand for transparency and interpretability in automated decision-making processes. Explainable AI (XAI) emerges as a transformative paradigm that addresses critical organizational needs by providing methodologies and techniques to make AI systems more interpretable, transparent, and accountable. This technical review examines the current state of XAI implementation in cloud-based enterprise applications, analyzing various methodologies including feature importance analysis, rule extraction techniques, surrogate models, and visualization approaches. The integration of XAI into cloud-based enterprise applications represents a fundamental evolution in organizational AI deployment strategies, requiring an optimal balance between performance and explainability to ensure stakeholders can understand, validate, and trust AI-driven decisions. Key application areas include building trust with users through personalized explanation frameworks, ensuring regulatory compliance and governance across multiple jurisdictional requirements, enhancing debugging and model improvement capabilities, and facilitating human-AI collaboration through hybrid decision-making systems. Implementation challenges encompass the trade-off between explainability and accuracy, lack of standardized metrics and evaluation frameworks, integration complexities with existing MLOps workflows, and scalability requirements for concurrent users across distributed geographical regions. Future directions focus on developing robust and scalable XAI techniques, user-centric interfaces, ethical frameworks, automated self-explaining systems, and cross-modal explanation capabilities that will enable more responsible and effective AI deployment across diverse organizational contexts.