AI evolution and its role in transforming the automation of commercial activities

Mykhailo Lukash, Yevhenii Chuprun, Oksana Lysak, Anatolii Husakovskyi and Kyrylo Hanhanov

LatIA, 2025, vol. 3, 344

Abstract: This article examined the impact of artificial intelligence (AI) on the automation of business processes, focusing on how intelligent systems enhanced management efficiency and operational optimization. Special attention was given to cognitive neuro-fuzzy models and their role in transforming business processes in the digital era. The study was timely, considering the exponential growth of data and the complexity of modern organizational structures, which demanded fast, accurate, and adaptive management solutions. AI technologies provided such capabilities, while companies that failed to adopt them risked losing competitive advantage amid ongoing digital transformation. The study aimed to develop and justify a conceptual approach to automating business processes through AI. To achieve this, two primary methods were applied: cognitive modeling using semantic M-networks to reflect human imaginative thinking in process structures, and reinforcement learning to optimize processes based on feedback mechanisms. The methodology combined theoretical literature analysis, mathematical modeling, and empirical examination of real business processes. The findings demonstrated that integrating AI significantly improved overall business process efficiency by reducing complexity, costs, and feedback loops, while enhancing control, regulation, and financial outcomes. The M-network model illustrated how AI adapted processes to dynamic environments and supported decision-making through visualized cognitive maps. Future research directions included advancing cognitive learning algorithms to handle larger datasets, designing adaptive AI interfaces tailored to individual user behavior, and exploring AI’s influence on cross-functional collaboration to foster comprehensive digital management ecosystems.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:rlatia:v:3:y:2025:i::p:344:id:1062486latia2025344

DOI: 10.62486/latia2025344

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