Configuring AI-guided sustainable competitive advantage for SMEs through business model innovation: A systematic literature review approach

<p dir="ltr">Researchers have been investigating effective frameworks for achieving competitive advantage for many years, particularly focusing on small and medium-sized enterprises (SMEs) to ensure their long-term viability. The recent rise of advanced technologies, especially artificial intelligence (AI), has intensified these discussions. Consequently, this study proposes a conceptual framework specifically designed to leverage AI for sustained competitive advantage in SMEs, examining their business models through this perspective.</p><p dir="ltr">To support this proposal, a systematic literature review (SLR) was conducted, encompassing a wide range of relevant literature, ultimately narrowing down to a final sample of 69 articles. The SLR method was selected to integrate the research systematically, transparently, and reproducibly. For qualitative analysis and framework development, the study employs thematic ontological analysis.</p><p dir="ltr">The research identifies multiple strands at the intersection of advanced technology and entrepreneurship, aimed at enhancing the competitiveness of SMEs. The primary outcome of this study is the creation of a comprehensive business model framework that includes both external antecedents—such as market and industry dynamics, technological infrastructure, government policies and support, strategic alliances, and socio-cultural factors—as well as internal antecedents like digital leadership, dynamic capabilities/adaptability, entrepreneurial mindset, data strategy, and growth/resilience. This framework ultimately contributes to sustainable performance.</p><p dir="ltr">From a practical perspective, the study offers a thorough pathway for SME owners and managers to adopt and utilize AI within their business strategies and operations. Based on the findings, SMEs can implement automation and machine learning to streamline business processes, reduce manual labor, and enhance overall operational efficiency. The study also discusses additional theoretical and practical implications, along with its limitations and suggestions for future research.</p>