Parallels in Evolution: From Siloed ISs to Integrated AI

Ioannis Patias
Faculty of Mathematics and Informatics, University of Sofia St.Kliment Ohridski“, 5 James Bourchier blvd., 1164, Sofia, Bulgaria
patias@fmi.uni-sofia.bg
DOI: 10.46793/BISEC25.160P

 

ABSTRACT: The evolution of information systems (ISs) has mirrored a trajectory from siloed, task-oriented systems to integrated, process-oriented enterprise resource planning (ERP) systems. This transformation has been driven by the increasing complexity of business operations and the need for efficient, data-driven decision-making. As organizations sought to streamline processes and improve overall performance, the integration of disparate systems became a crucial priority. Concurrently, artificial intelligence (AI) has experienced a similar evolution. Early AI applications were often limited to specific tasks, such as image recognition or natural language processing. However, recent advancements have enabled the development of more sophisticated AI systems capable of understanding complex contexts and making autonomous decisions. This paper explores the parallels between the evolution of IS and AI, arguing that the future of AI lies in greater integration and process orientation. By examining historical trends and current developments, we identify key factors driving this convergence. These include the increasing availability of data, advances in machine learning algorithms, and the growing demand for automation and efficiency. We propose that the next generation of AI will be characterized by its ability to seamlessly integrate with existing systems, understand and respond to organizational needs, and optimize processes across the entire enterprise. By leveraging AI’s potential to automate routine tasks, enhance decision-making, and uncover valuable insights, organizations can achieve significant competitive advantages.

KEYWORDS: Information System (IS), enterprise recourse planning (ERP), Artificial Intelligence (AI), AI Integration, Process Orientation

ACKNOWLEDGMENTS: The research of this author1 was supported by the European Union, through the project PANORAIMA (101189994) in the organisation <999887641 – SOFIA UNIVERSITY ST KLIMENT OHRIDSKI>.

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IZVOR: Proceedings of the 16th International Conference on Business Information Security BISEC’2025