Explainable AI Driven Frameworks for Enterprise Automation Cloud Native Platforms and Smart Data Systems Integration
DOI:
https://doi.org/10.15662/IJEETR.2026.0803001Keywords:
Explainable AI, Enterprise Automation, Cloud-Native Platforms, Smart Data Systems, Model Interpretability, Microservices Architecture, Data Integration, AI Governance, Digital Transformation, Intelligent SystemsAbstract
Explainable Artificial Intelligence (XAI) is rapidly emerging as a critical enabler for enterprise automation, particularly in cloud-native environments and smart data ecosystems. As organizations increasingly rely on AI-driven decision-making, the need for transparency, interpretability, and trust becomes essential. This paper explores the design and implementation of explainable AI-driven frameworks that integrate seamlessly with enterprise automation systems, cloud-native platforms, and intelligent data infrastructures. It highlights how XAI enhances operational efficiency, regulatory compliance, and decision accountability while supporting scalable and resilient architectures.
The study discusses the interplay between microservices, containerization, and AI pipelines, emphasizing how explainability can be embedded into each layer of enterprise systems. Additionally, it examines the role of smart data systems in enabling real-time analytics, adaptive learning, and automated workflows. A comprehensive methodology is proposed for developing XAI-enabled enterprise frameworks, focusing on model transparency, data governance, and system interoperability.
The findings suggest that explainable AI not only improves trust in automated systems but also facilitates better human-AI collaboration. However, challenges such as computational overhead, complexity, and trade-offs between accuracy and interpretability remain significant. This research contributes to the growing body of knowledge on building trustworthy AI systems in modern enterprise environments.
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