<![CDATA[Research on artificial intelligence (AI) and digital transformation has examined business innovation, competitive advantage, and sustainable firm performance, but these outcomes are frequently addressed through separate analytical perspectives. This study synthesizes the relationships among AI capability, business innovation, competitive advantage, and sustainable firm performance, emphasizing organizational mechanisms and differences in the underlying evidence. A structured integrative literature review was conducted using a purposively selected corpus of 19 scholarly publications published between 2020 and 2026. Publications were classified according to technological capability, organizational mechanisms, innovation outcomes, competitive implications, and sustainability-related performance. Directed qualitative synthesis and comparative evidence matrices were employed to identify recurring relationships and distinguish reported findings from broader conceptual interpretations. The reviewed literature associates AI capability with data-driven decision-making, process reconfiguration, organizational adaptability, and business model innovation. Competitive implications emerge through operational efficiency, dynamic capabilities, strategic differentiation, and entrepreneurial opportunity recognition. Sustainability-related findings emphasize green innovation, knowledge integration, data-driven organizational culture, and sustainability-oriented business strategies. The synthesis identifies business innovation and complementary managerial capabilities as important explanatory mechanisms connecting AI resources with organizational outcomes. Its principal contribution is an integrated analytical framework distinguishing technological capabilities, organizational mechanisms, competitive outcomes, and sustainable performance without assuming a uniform causal sequence. Given the bounded literature corpus and heterogeneous research designs, the findings represent patterns of reported associations rather than pooled effects or generalizable causal estimates. ]]>
