AI Powered Low Code/No Code Software Development

Abstract: The Low-Code and No-Code (LCNC) platforms haverecently been recognized as a strategic move to speed up application development, minimize the need forspecialized programming knowledge, and quicklychange the digital environment of the enterprise.The current literature has consistently shown theproductivity benefits of LCNC platforms throughabstraction, visual modeling, and component-basedreuse; however, these benefits have been shown to be applicable only to standardized and simpleapplications. With the increasing use of LCNC platformsfor developing large- scale enterprise applications,the current issues of scalability, architecturalrobustness, quality, governance, and security havebecome more visible. Recently, the integration ofArtificial Intelligence (AI) into LCNC platforms hasreceived increasing attention, and the benefits ofintelligent automation, decision support, and adaptiveworkflow design have been widely explored.However, the new agency and reasoning paradigmsof AI have introduced new issues of explainability,compliance, and controlled autonomy in end-userdevelopment. This paper offers a thorough literature survey and comparative synthesis of existing research on LCNC platforms, specifically with regard to productivity results, drivers of adoption, architectural strategies, and AI integration. The results of this analysis indicate that existing research is still piecemeal and lacks empirical support for architectural patterns, quality, and AI-integrated workflows in an enterprise setting. Moreover, although AI-supported low-code development appears promising, empirical data on the productivity effect, governance aspects, and return on investment of AI-assisted development is still relatively scarce. On the basis of the synthesized results, this paper points out the key research gaps and open challenges in terms of scalability, architectural validation, governance, security, and agentic AI integration. The findings of this research offer a systematic basis for future research work on designing scalable, secure, and intelligent AI-assisted LCNC platforms for sustainable enterprise adoption.

Keywords: Low-Code/No-Code, AI-assisted software engineering, architectural governance, testability, enterprise software, developer productivity.