Autonomous Component Lifecycle Management in Salesforce LWC using AI-driven Predictive Rendering - Shiramalla, Rupesh (International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2025)
Making use of an AI-based predictive rendering system, this paper investigates how Salesforce Lightning Web Components (LWC) can develop better (more autonomous and efficient) lifecycle management. On one hand, modern Salesforce applications require very fast user interfaces, and on the other, traditional LWC rendering cycles typically depend on manual optimization and thus bring unnecessary re-renders, increased computational load, and inconsistent performance of complicated component hierarchies. To surpass these issues, the research presents an innovative way of lifecycle management that utilizes subtle machine learning signals to forecast component state changes even before the changes take place, and thus, rendering is only carried out when significant updates are present. Tests run on a prototype component in a Salesforce environment have achieved noticeable improvements in render frequency reduction, component responsiveness, and platform resource usage, especially in data-intensive and event-driven UIs. A case study of a multi-layer dashboard application also demonstrates that predictive rendering can, without human intervention, optimize the balancing between accuracy and performance, thereby limiting over-rendering without losing UI fidelity. The results indicate that the inclusion of AI-driven heuristics into LWC lifecycles not only results in better performance but also makes it easier to develop because the development team can move away from manual tuning and toward adaptive, self-regulating behavior. This research provides a viable architectural model, offers implementation guidelines, and presents empirical data that supports the concept of predictive rendering operating in the Salesforce environment. The impact of this work is not limited to the Salesforce scope but could lead to the development of intelligent LWCs that adapt to user behavior, thus increasing scalability and paving the way for additional self-governing user interface paradigms in cloud-based enterprise applications.