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

Abstract

Enterprise mobile applications are continuously updated to introduce new functionality, improve performance, address security issues, and respond to changing business requirements. Conventional CI/CD pipelines primarily depend on predefined quality gates, static test results, and manually configured deployment policies. Although these mechanisms provide essential safeguards, they may not adequately capture dynamic operational risks that emerge after deployment. This paper proposes Adaptive Release Intelligence (ARI), an AI-driven framework that transforms release management from a static gate-based process into a predictive and adaptive decision system. The proposed framework integrates application telemetry, crash information, performance indicators, user-impact signals, deployment history, feature-level behavior, device and network conditions, and CI/CD metadata to estimate release risk. Based on the predicted risk, ARI dynamically controls deployment progression, feature exposure, monitoring intensity, rollback decisions, and recovery actions. A Release Risk Score (RRS) is introduced to represent the estimated probability and severity of release-related failures. The framework further incorporates explainable AI to support governance and provide evidence for release decisions. The proposed approach aims to reduce uncontrolled release exposure, detect degradation at an early stage, improve recovery speed, and establish a continuous feedback loop between development, deployment, production monitoring, and subsequent release decisions.