Adoption of Artificial Intelligence in Competitive Intelligence: A Systematic Literature Review

Abstract

The accelerating convergence of Artificial Intelligence (AI) and Competitive Intelligence (CI) represents a significant transformation in how organisations gather, analyse, and interpret strategic information. As global markets become increasingly dynamic and data-driven, understanding how AI enhances CI processes is critical for both scholars and practitioners. This study conducts a systematic literature review (SLR) using the PRISMA methodology to synthesise and critically evaluate existing research on the integration of AI into CI. Drawing from  peer-reviewed articles published between 2000 and 2025, the review identifies four dominant thematic clusters: AI-enabled data acquisition and mining, predictive analytics and machine learning in market forecasting, natural language processing in sentiment and competitor analysis, and ethical, organisational, and interpretative challenges in AI-driven intelligence. Findings reveal that while AI enhances the accuracy, speed, and depth of intelligence analysis, the literature remains fragmented across disciplines, with limited empirical validation and theoretical coherence. Notably, few studies address the human–AI interface, data governance, and contextual applicability in emerging economies. The paper presents an integrative conceptual framework that links AI capabilities with the CI cycle, highlighting avenues for future research, including ethical AI governance, explainable intelligence models, and applications in small and medium-sized enterprises (SMEs). The synthesis underscores that AI does not replace human intelligence but rather augments it—transforming CI into a more anticipatory, adaptive, and strategic function.