(untitled) (International Journal of Advanced Research in Computer Science & Technology(IJARCST), 2023)
The rapid adoption of cloud computing and Artificial Intelligence (AI) has transformed enterprise environments by enabling organizations to process large volumes of data, automate business operations, and improve strategic decision-making. However, the increasing complexity of AI models has raised concerns regarding transparency, interpretability, and trustworthiness, particularly in critical enterprise applications where decisions significantly affect business operations, compliance, and customer relationships. Explainable Artificial Intelligence (XAI) addresses these challenges by providing understandable explanations for AI-generated predictions while maintaining high predictive performance. This study proposes an intelligent decision framework that integrates Explainable AI with cloud computing to enhance predictive decision intelligence in enterprise environments. The proposed framework leverages scalable cloud infrastructure for real-time data processing, predictive analytics, model deployment, and continuous monitoring while incorporating explainability techniques to improve transparency and accountability. Machine learning algorithms analyze enterprise data to predict operational risks, resource demands, customer behavior, and business performance, whereas XAI methods generate meaningful explanations that support managerial decision-making. Cloud-native technologies ensure scalability, flexibility, and efficient resource utilization across distributed enterprise systems. The study demonstrates that combining Explainable AI with cloud computing enhances prediction accuracy, improves organizational trust in AI systems, supports regulatory compliance, and enables proactive business decision-making. The proposed framework contributes to the development of intelligent, transparent, and secure enterprise systems capable of supporting sustainable digital transformation and data-driven organizational growth