From Big Data to organizational knowledge: Limits and possibilities of artificial intelligence in corporate governance
DOI:
https://doi.org/10.33448/rsd-v14i9.49560Keywords:
Knowledge Management, Artificial Intelligence, Corporate Governance, SECI, Big Data, Information Overload, Knowledge Engineering.Abstract
This article investigates how Big Data is transformed into organizational knowledge, exploring the limits and possibilities of Artificial Intelligence (AI) in corporate governance. It contextualizes the phenomenon of information overload and examines knowledge management models such as the SECI framework (Nonaka–Takeuchi model: Socialization, Externalization, Combination, and Internalization) to differentiate data, information, and knowledge. A historical review highlights the exponential growth of data volumes from 2000 to 2025, emphasizing the escalation from exabytes to zettabytes per year. The article discusses how AI supports analytical automation, decision-making, and knowledge personalization, while also warning of risks such as bias, opacity, and insufficient governance. A case study of the Arqueum DMDocs platform, used for process and electronic document management in large organizations, demonstrates practical applications. A comparative framework contrasts traditional practices with AI-enabled approaches. In the discussion, corporate governance, AI, and knowledge management are integrated, stressing the need for data and algorithm governance policies and the importance of aligning technological strategies with organizational culture. The article concludes by highlighting AI’s potential to generate actionable knowledge from Big Data when supported by responsible governance, and points out research gaps to be addressed in future studies.
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