Natural Language Processing in Competitive Intelligence: A Systematic Review

This study examines how natural language processing (NLP) supports the competitive intelligence (CI) process and how its integration affects organizational decision-making. The review also identifies major research trends, practical applications, and key gaps in NLP-enabled CI research. A systematic literature review was conducted using the PRISMA approach. Relevant studies were retrieved from Scopus and Web of Science through structured Boolean search strings combining NLP-, CI-, and decision-making-related terms. After duplicate removal, title and abstract screening, full-text eligibility assessment, and quality appraisal, 94 peer-reviewed studies published between 2022 and 2026 were included in the final synthesis. Although NLP has been widely discussed in business analytics and artificial intelligence research, its specific role in CI remains fragmented across disciplines. This review addresses that gap by consolidating evidence from information systems, management, and computational research and by proposing an integrated framework linking NLP techniques, CI stages, decision effects, and risk pathways. The review shows that NLP supports CI through automated information gathering, content summarization, sentiment analysis, trend detection, weak signal identification, competitive landscape mapping, predictive analytics, and insight communication. However, the organizational value of NLP is not determined by technological adoption alone. It depends on source quality, model interpretability, human oversight, and alignment between analytical outputs and managerial needs. The review also identifies emerging practical applications in competitor monitoring, reputation analysis, market sensing, and risk early-warning. This study contributes a framework that explains both value creation and risk propagation in NLP-enabled CI systems. It also outlines concrete directions for future research, including longitudinal outcome studies, CI-specific benchmarks, human–AI collaboration design, hallucination mitigation, and ethical governance mechanisms.