Enhancing Low-Code Platforms For AI-Driven Business Automation
DOI:
https://doi.org/10.29284/pg86s882Keywords:
Automation, Industry 4.0, Internet of Things, Low-Code, Smart Manufacturing.Abstract
The need for application development has increased at speed has made low-code platforms crucial digital transformation enablers, though current research shows a shortfall in their inability to adequately deal with rule changing, dynamic process automation, and real-time decisions. Classic low-code solutions speed up deployment but not intelligent enough to self-optimize or deal with intricate enterprise scenarios. To bridge this gap, this study suggests an AI-enhanced low-code approach that combines Salesforce Apex with Einstein AI to boost developer productivity and enterprise automation. The aim is to illustrate how intelligent static code analysis, automated debugging, and predictive modeling enhance adaptability and minimize errors in CRM processes. The approach utilizes AI-driven static analysis of Apex code through Python-based NLP models (GPT-4, CodeT5, and LangChain) combined with Salesforce Einstein AI for real-time decision-making. The suggested model was validated using Kaggle CRM datasets and resulted in an accuracy boost by 17.6% compared to conventional low-code implementations and cutting down debugging effort by 23%. The AI-augmented framework had fast deployment times compared to baseline models and ensured greater code reliability. Salesforce acted as main implementation platform, showcasing end-to-end integration of AI into low-code development. The findings validate that the integration of Apex and AI makes low-code platforms more than accelerators and turns them into adaptive, intelligent ecosystems. It is established in this study that low-code AI-based platforms substantially increase the productivity of developers and automate business. Future studies will investigate how to scale this model to be applicable to other sectors with priority on enterprise-level governance, explainable artificial integration, and development through natural language.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
