Ai-infused business model innovation for competitive advantage in the Era of big data and digital transformation

Background The use of artificial intelligence (AI) in business operations is increasing rapidly. However, existing research provides limited systematic insight into how AI integration reshapes business models. This is especially unclear when big data capabilities within digital transformation support AI. This lack of understanding hampers both theoretical development and practical decision-making for strategic AI deployment. Methods Drawing on dynamic capability theory and the resource-based view, this study employs structural equation modelling using a pooled cross-sectional dataset of 847 multinational corporations across 23 industries. The executive survey was collected in 2024 and complemented with retrospective survey information and archival firm-level indicators covering 2019–2024. The study adopts a sequential explanatory mixed-methods design, combining quantitative analysis with 312 executive interviews. Results The findings identify four pathways through which AI-driven business model innovation is positively associated with competitive advantage: algorithmic value proposition enhancement (β = 0.487, p < 0.001), intelligent ecosystem orchestration (β = 0.423, p < 0.001), predictive resource optimization (β = 0.392, p < 0.001), and autonomous competitive positioning (β = 0.356, p < 0.001). Together, these pathways explain 52.3% of the variance in competitive advantage (R 2 = 0.523) and account for 75.8% of the total association between AI integration and competitive advantage. Pathway effectiveness varies across business model contexts: algorithmic value proposition enhancement shows a stronger association in B2C contexts (β = 0.612) than in B2B contexts (β = 0.389), while intelligent ecosystem orchestration shows a stronger association in platform-based models (β = 0.534) than in traditional models (β = 0.318). Conclusions The study advances dynamic capability theory by offering a fine-grained empirical analysis of AI-enabled capability deployment and its association with competitive advantage. It also offers practitioners an evidence-based framework for prioritizing AI and big data investments according to their competitive context.