AI adoption and innovation in MSMEs for enhancing competitiveness and firm performance

Purpose This study examines how AI adoption enablers influence AI-driven innovation, competitiveness and multidimensional firm performance among micro, small and medium-sized enterprises (MSMEs). Design/methodology/approach Data were collected from 268 Malaysian MSMEs and analysed using partial least squares structural equation modelling. A disjoint two-stage approach was used to model firm performance as a higher-order construct comprising technological, economic and sustainability performance. Necessary condition analysis (NCA) and importance-performance map analysis were also applied to identify performance bottlenecks and managerial priorities. Findings The results show that environmental conditions and technological readiness are positively and significantly associated with AI adoption, whereas organisational support does not operate as a sufficient adoption driver. However, AI adoption is strongly associated with AI-driven innovation, which in turn strengthens competitiveness and improves firm performance. The NCA further shows that high firm performance requires minimum threshold levels of technological readiness, environmental conditions, organisational support, AI adoption, AI-driven innovation and competitiveness. Practical implications Managers should not treat AI adoption as a symbolic digital upgrade. Performance gains depend on converting AI into process, product/service and business model innovation, and then into competitiveness. Policymakers should move beyond isolated AI grants towards coordinated support systems that combine infrastructure, skills development, advisory services, innovation financing and ecosystem confidence. Originality/value The study develops the Technology–Environment–Capability for AI Performance (TEC-AIP) Framework, integrating sufficiency and necessity logics to explain AI-enabled MSME performance in an emerging economy.