Adaptive Release Intelligence For Enterprise Mobile Applications An Ai-Driven Framework For Risk-Aware Deployment, Feature Control, and Automated Recovery

Abstract

Enterprise mobile applications undergo frequent releases involving new features, security patches, dependency updates, and backend-service changes. Conventional deployment strategies often rely on static testing and predefined rollout policies, which may not adequately capture changing application behavior, user conditions, device heterogeneity, and runtime failures. This paper proposes Adaptive Release Intelligence (ARI), an AI-driven framework for risk-aware mobile application deployment, dynamic feature control, and automated recovery. ARI integrates application telemetry, user behavior, device characteristics, network conditions, crash information, and release metadata to estimate deployment risk using temporal machine learning. A hybrid risk model combines failure probability, anomaly detection, change impact, user exposure, and recovery cost to determine release eligibility. The framework further employs adaptive feature flags and controlled canary deployment to progressively expose new functionality. A feedback-driven recovery mechanism automatically triggers feature disabling, traffic reduction, or rollback when post-release indicators exceed predefined risk thresholds. Explainable AI is incorporated to identify the factors contributing to release-risk decisions. The proposed framework establishes a closed-loop process from release assessment to deployment, monitoring, adaptive control, and recovery, enabling enterprise mobile applications to achieve safer and more responsive continuous delivery.