Business Process Engineering through Artificial Intelligence Algorithms and Chaos Theory: A Conceptual Framework

Today's business environments are characterized by increasing complexity and volatility, which highlights the limitations of traditional process engineering models. Faced with nonlinear and unpredictable organizational phenomena, classical methods of analysis and optimization become insufficient, making it necessary to integrate more advanced theoretical and technological frameworks. Chaos theory provides a conceptual tool for describing these dynamics, and artificial intelligence brings the ability to identify hidden patterns and generate robust predictions in seemingly unstable systems. This paper examines the synergy between chaos theory and artificial intelligence in business process engineering, focusing on how machine learning algorithms and neural networks can support managerial decisions, optimize processes, and strengthen organizational resilience. The main contribution is the formulation of a conceptual and methodological framework through which chaotic models, augmented with artificial intelligence, can support the digital transformation of organizations. Although the integration of these paradigms raises challenges related to complexity, transparency, and data access, the research findings highlight the real potential for building adaptive, intelligent, and future-oriented business systems.