Abstract
While competitive intelligence (CI) is vital for strategic decision-making, traditional CI methods are often manual, time-consuming and narrow in scope. Technological advances in artificial intelligence and machine learning, along with intensive training and transformer models, offer opportunities to automate CI and analyse large volumes of unstructured data. This study sought to explore the use of deep learning and transformer models within a CI framework to support strategic decision-making, while addressing interpretability, data quality, and ethical concerns. A systematic qualitative literature review was conducted in accordance with PRISMA guidelines. The review included articles from Scopus, used purposive sampling, and applied an inclusion criterion for peer-reviewed articles published between 2015 and 2025. Using an iterative process, nine studies were identified and thematically analysed inductively using thematic coding. The results indicated that deep learning and transformer models could improve CI processes by enhancing sentiment analysis, competitor monitoring, and forecasting capabilities. However, there are some limitations related to computational power requirements, explainability, and scalability. Organisations need to ensure their AI adoption plans align with their ethical considerations, governance structures, and workforce upskilling measures to maximise the value of these models. Changing workflows and infrastructure components is also fundamental for both real-time and CI applications. The significant advances enabled by deep learning and transformer models transform CI into a process that enables organisations to obtain adaptive, accurate, and ethical insights. However, ensuring transparency in deliverables, establishing robust data governance, and validating both potential and perceived outcomes will remain crucial. The research also sought to develop a conceptual framework to guide the ethical and strategic implementation of transforming and deep learning models into CI, drawing on concepts from the academic literature and the professional field. Deep learning and transformer models enable innovative ways to CI, providing businesses with more agile, accurate, and ethical insights. Effective implementation involves transparent deliverables, personnel training and upskilling, strong data governance frameworks, and careful consideration of intended and unforeseen implications.