The Complexity of Competitive Intelligence in the Age of data ambiguity and Artificial Intelligence

The aim of the study is to explore the development aspects of competitive intelligence (CI) in terms of the challenges posed by the increase in data volume, incl. unverified data, data reliability, and the integration of artificial intelligence (AI) into data analysis processes. The primary research question explores how the role of the human factor has become increasingly important in ensuring the accuracy and reliability of data used by AI, especially in an era dominated by AI and rapid information dissemination. The study highlights the imperative role of human judgment in the era of AI-driven data analysis, highlighting skills, competencies, and authority as critical factors in evaluating data processing outcomes. This points to the risks associated with the uncritical adoption of AI-generated solutions, which can lead to innovative but impractical outcomes that consume significant organizational resources. Furthermore, the study calls for a balanced approach to integrating AI into CI processes, supporting strategies that enhance the synergy between human analytical prowess and AI computational efficiency. This approach is critical to overcoming the challenges posed by data reliability and ensuring the effective implementation of CI strategies that are both innovative and grounded in reality.