ABSTRACT: The rapid adoption of artificial intelligence (AI) in software engineering has transformed the ways entrepreneurial ventures innovate, yet the multidimensional impact of AI-enabled practices on entrepreneurial innovation performance remains underexplored. This study aims to develop a conceptual framework linking AI-enabled software engineering practices including automation-driven development, intelligent testing, AI-based code generation, DevOps integration, and AI-driven security to entrepreneurial innovation performance, measured across product, process, and business model dimensions. The research uses the conceptual approach, in which the findings are based on the data available in the secondary sources, such as academic journals, books, conference papers, historical documents, and reliable online sources. The literature review points out that the use of AI practices promotes efficiency, minimizes errors, prototyping, constant process improvements, and organizational learning, which in turn drive the results of innovation in the context of entrepreneurship. Theory Theoretical evidence reveals that the influence of AI adoption is moderated by workforce preparedness, knowledge management, and organizational capacities, whereas the integration of security strategies can guarantee sustainable and reliable innovation. On the basis of these understandings, the research advises entrepreneurs and software companies to plan fully embrace and integrate AI-enabled practices, allocate resources to upskilling groups, and align technological usage with organizational capacity. The study concludes that AI-enabled software engineering practices are critical drivers of entrepreneurial innovation, although empirical validation and context-specific research are needed to confirm the framework’s applicability across diverse markets. KEYWORDS: Artificial Intelligence, Software Engineering, Automation, Entrepreneurial Innovation, DevOps, Code Generation, Innovation Performance.