ABSTRACT: The rapid integration of AI-enabled software engineering practices has transformed the technology entrepreneurship landscape, yet the mechanisms through which these capabilities influence entrepreneurial innovation performance remain underexplored. Despite the increasing adoption of cloud-native development, DevSecOps practices, AI-based coding tools, low-code/no-code platforms, and microservices architecture, existing studies often examine these capabilities in isolation, overlooking their combined effect on innovation outcomes. This study aims to conceptualize the relationship between multidimensional AI-enabled software engineering practices and entrepreneurial innovation performance, providing a holistic framework for understanding their strategic role in technology ventures. The paper under consideration is developed with the help of the qualitative approach, relying on such sources of the secondary data as journals, books, historical sources, and reliable online publications in order to synthesize the existing knowledge, define the gaps in the research, and suggest an integrated conceptual model. As it has been analyzed, the synergistic use of AI-powered practices allows increasing scaling, the efficiency of operations, the speed of developing its products, reducing risks and being more responsive to the market, which all lead to the performance of innovations. The research suggests that technology entrepreneurs, managers, and policy makers should take a coordinated method of introducing such capabilities as they make investments in learning and knowledge management. The weaknesses comprise the conceptual form of the study and lack of primary empirical evidence as a way of stressing that future quantitative testing should exist in various technological situations. KEYWORDS: AI-Enabled Software Engineering, Cloud-Native Development, Develops, Low-Code Platforms, Microservices, Entrepreneurial Innovation, Technology Ventures
Maryam Folakemi Adepoju1, Abdullateef Ajibola Adepoju2, Aliyu Mohammed3 (Sun,) studied this question.