The Evolution of Competitive Intelligence in a Complex Business Environment
DOI:
https://doi.org/10.37380/jisib.v14.i2.2546Abstract
Competitive Intelligence (CI) refers to the systematic collection, analysis, and dissemination of information about a business, its external environment, and the overall business context to support strategic decision-making. As the environment becomes increasingly complex and dynamic, the need for CI becomes more pronounced. In recent years, the field of CI has undergone significant transformation, driven by technological innovations, the demand for real-time information, and a rise in interdisciplinary approaches. These developments are reflected in recent academic publications, which increasingly focus on topics such as the integration of artificial intelligence (AI), business intelligence tools, innovation support, the role of education, global collaboration and competition, and the evolution of interdisciplinary work.
Emerging trends include the integration of AI and big data analytics, which are fundamentally changing how organizations collect and process information. AI-powered systems facilitate real-time analysis of large datasets, uncovering patterns and trends that would be difficult to identify manually. This transformation enhances decision-making by delivering timely and actionable insights. For instance, predictive analytics—enabled by machine learning algorithms—allow businesses to anticipate market shifts, identify emerging competitors, and optimize strategic actions (Sun et al., 2021; Chen et al., 2021). The use of AI in CI is expected to continue growing. Advanced systems not only automate routine data collection tasks but also support more sophisticated analyses, offering deeper insights and more accurate forecasts.
AI is also increasingly recognized as a catalyst for innovation within organizations, fostering the development of new products and services. This connection between AI and innovation underscores the importance of cultivating an organizational culture that values knowledge acquisition and environmental awareness (de las Heras-Rosas & Herrera, 2021).
As CI tools and methodologies evolve, the demand for professionals with both technical and analytical skills is rising. In response, educational institutions are updating curricula to include data science, business analytics, and information management. Beyond technical competencies, CI professionals must also develop soft skills such as critical thinking, adaptability, and ethical decision-making (Freyn & Hoffman, 2023; Calof & Cekuls, 2023). Lifelong learning and continuous professional development are essential to keep pace with new tools and practices. Many academic programs now emphasize simulations and case-based learning to better prepare future CI analysts for real-world challenges. Given the growing complexity of CI tasks, interdisciplinary expertise—combining knowledge from business, technology, psychology, and communication—is increasingly vital.
Collaboration across disciplines and industries will further accelerate the advancement of CI methodologies, ensuring that intelligence practices remain relevant in a rapidly changing business landscape. This synthesis of knowledge and practice will help CI become a more integrated and strategic function that underpins organizational success. As organizations continue to navigate uncertainty and complexity, CI will play a critical role in enabling proactive and informed decision-making. Looking ahead, improved data integration and interdisciplinary collaboration will be key drivers of CI’s evolution, ensuring that organizations remain agile, innovative, and competitive.
Accordingly, the Journal of Intelligence Studies in Business (JISIB) is receiving a growing number of submissions on contemporary CI applications. These publications aim to connect scholars and professionals in the CI field, fostering ongoing dialogue and development. I would like to express my gratitude to all contributors to this issue.
On behalf of the Editorial Board,
Sincerely Yours,
Prof. Dr. Andrejs Cekuls
University of Latvia, Latvia
References
Calof, J., Cekuls, A. (2023). SCIP Prague 2023 – Academic Track: What is the future direction of competitive intelligence. Journal of Intelligence Studies in Business, 13 (Special Issue 1), pp. 4-9. DOI: https://doi.org/10.37380/jisib.v13si1.1132
Chen, Y., Li, C., Wang, H. (2022). Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021), Forecasting, 4 (4), pp. 767-786. DOI: https://doi.org/10.3390/forecast4040042
de las Heras-Rosas, C., Herrera, J. (2021). Innovation and competitive intelligence in business. A bibliometric analysis, International Journal of Financial Studies, 9(2), June 2021, Article number 31. DOI: https://doi.org/10.3390/ijfs9020031
Freyn, S., Hoffman, F. (2023). Competitive intelligence in an AI world: Practitioners’ thoughts on technological advances and the educational needs of their successors, Journal of Intelligence Studies in Business, 12(3), pp.6 – 17. DOI: https://doi.org/10.37380/jisib.v12i3.893
Sun, W., Nan, Y., Yang, T.Z., Hu X.Y., Jiang, Y. (2021). Integration Innovation of Competitive Intelligence, AI and Big Data Analysis, Communications in Computer and Information Science, 1424, pp. 347 – 357. DOI: https://doi.org/10.1007/978-3-030-78621-2_28
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