Strategic Intelligence in the Age of Artificial Intelligence

This issue brings together the authors’ research, which reveals contemporary topics in the context of studying CI, which resonate with contemporary scientific development trends and dynamics of change, and relate to recent discoveries in the field. Artificial intelligence (AI) and advanced data analytics are increasingly shaping how organizations generate intelligence and support strategic decision-making. In contemporary management research, strategic intelligence is no longer viewed solely as information gathering but as an integrative capability that combines analytical technologies, knowledge management, and strategic foresight. The integration of AI into strategic intelligence systems enhances the ability of organizations to process complex data and extract meaningful insights. Consequently, AI-supported intelligence systems facilitate knowledge-driven decision-making and contribute to improved organizational performance (e.g., Maitra et al., 2025; Seremeti & Anastasiadou, 2025). Several theoretical perspectives help explain how the integration of strategic intelligence and AI contributes to organizational effectiveness. Decision Theory provides an important foundation by explaining how organizations evaluate alternatives and make choices under uncertainty. Empirical evidence indicates that AI-enabled decision systems can significantly increase decision accuracy while reducing decision-making time, thereby improving the efficiency of strategic processes (e.g., Sikhakolli et al., 2025; Seremeti & Anastasiadou, 2025). The successful adoption of AI technologies can also be understood through the Technology Acceptance Model (TAM), which emphasizes that perceived usefulness and ease of use influence the adoption of new technologies within organizations. Consequently, organizational readiness, digital capabilities, and user acceptance become key determinants of effective AI integration (Abuzaid, 2024; Maitra et al., 2025). Another important theoretical lens is Information Processing Theory, which highlights the role of organizational capacity to process and interpret information in complex environments. Modern organizations face increasing information overload due to the exponential growth of digital data. AI technologies expand organizational information-processing capacity by automating data analysis, integrating diverse data sources, and delivering real-time insights that support more informed and timely decisions. These capabilities significantly enhance the analytical foundation of strategic intelligence systems. From a dynamic perspective, Organizational Learning Theory emphasizes the role of knowledge creation, knowledge sharing, and continuous adaptation in shaping organizational performance. AI contributes to organizational learning by extracting insights from large datasets and supporting evidence-based strategy development. By enabling organizations to identify patterns, evaluate outcomes, and refine strategies, AI strengthens adaptive learning processes and promotes innovation-driven growth (Alami & Al-Masaeid, 2025; Sposato, 2025). AI-driven analytics enables organizations to process large volumes of data, identify patterns, and generate predictive insights that improve decision quality and strategic responsiveness (Vincent, 2021; Weiser & von Krogh, 2023). In highly dynamic contexts, AI-based modeling and scenario simulations help organizations anticipate environmental changes and reduce uncertainty in strategic planning. At the same time, research emphasizes that the most effective outcomes emerge when AI-generated insights are combined with human judgment and intuition in decision processes (Vincent, 2021). Consequently, AI-supported intelligence systems enhance strategic agility and strengthen organizations’ ability to respond to emerging challenges and opportunities in omplex environments (Weiser & von Krogh, 2023; Asmar & Al-Rob, 2024). Beyond theoretical explanations, AI-enabled intelligence systems improve knowledge-driven decision making by transforming data into action-able insights that support evidence-based stra-tegic choices (Sikhakolli et al., 2025; Abdeljaber et al., 2025). In addition, AI contributes to operational efficiency by automating analytical processes and optimizing resource utilization, allowing managers to focus on strategic and innovative activities (Okafor & Murphy, 2025). Research increasingly emphasizes the impor-tance of human–AI collaboration, where AI systems augment rather than replace managerial expertise. Human oversight remains essential for interpreting analytical outputs, ensuring ethical accountability, and aligning technological insights with organizational values and strategic objectives (Štrukelj & Dankova, 2025; Alami & Al-Masaeid, 2025). Future research should continue to explore hybrid intelligence models that combine technological capabilities with human expertise to support sustainable and responsible decision making in increasingly complex organizational environments.