Optimizing business competitiveness through artificial intelligence: A framework for digital innovation and operational efficiency

The rapid advancement of artificial intelligence (AI) has transformed the business landscape by enhancing innovation, operational efficiency, and digital transformation. As firms strive for sustained competitiveness, AI-driven strategies have become increasingly critical. However, existing research lacks a comprehensive framework that systematically explains how AI adoption translates into business competitiveness. Unlike prior systematic or bibliometric reviews that primarily map AI applications or performance outcomes, this study integrates Latent Dirichlet Allocation (LDA)-based topic modeling with an open innovation perspective to synthesize fragmented insights into a coherent framework. Drawing on a systematic literature review of 831 Scopus-indexed publications from 2015 to 2024, this study applies LDA to identify key thematic patterns related to AI-driven business performance. The analysis reveals that AI contributes to business competitiveness through interconnected mechanisms of innovation dynamics, operational efficiency, and digital transformation, supported by data-driven decision-making, supply chain optimization, and firm digitalization. The review further suggests that industry performance and firm digitalization are suggested in the literature as potential mediating pathways that may reinforce AI’s influence on business competitiveness. This study advances the literature by proposing an integrative AI-driven business competitiveness framework that links advanced topic modeling with open innovation logic. The framework offers actionable insights for scholars, managers, and policymakers seeking to leverage AI for sustainable competitive advantage. Future research is encouraged to empirically test the proposed relationships across different industries and contexts. • Presents an AI-driven conceptual framework linking innovation dynamics, operational efficiency, firm digitalization, and business competitiveness. • Systematic Scopus review combined with LDA topic modeling to identify seven thematic areas. • Identifies four core aspects where AI impacts performance: Innovation Dynamics, Operational Efficiency, Digital Transformation, and Business Competitiveness. • Highlights practical implications for business: process optimization, supply chain innovation, and data-driven strategy formation. • Recommends directions for empirical validation and industry-specific research.