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DOI: 10.15862/19FAOR425 (https://doi.org/10.15862/19FAOR425)

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Pushkarsky M.S. Automation of corporate business processes based on artificial intelligence technologies. Russian journal of resources, conservation and recycling. 2025; 12(s4). Available at: https://resources.today/PDF/19FAOR425.pdf (in Russian). DOI: 10.15862/19FAOR425

Automation of corporate business processes based on artificial intelligence technologies

Pushkarsky Mikhail Sergeevich
Financial University under the Government of the Russian Federation, Moscow, Russia
E-mail: 249078@edu.fa.ru

Academic adviser: Kapustina Nadezhda Valeryevna
Financial University under the Government of the Russian Federation, Moscow, Russia
E-mail: NVKapustina@fa.ru

Abstract. This study analyzes business process automation mechanisms through the lens of data management within an organization. The author demonstrates that accelerating individual links in a production or logistics chain without ensuring end-to-end data consistency not only fails to improve the overall result but also creates new points of inconsistency: the accelerated section «overloads» adjacent stages with outdated or incomplete information. Thus, the effect of automation is inverted — speed increases, while the quality of management decisions declines. The central practical result of the work is a process model in BPMN notation, incorporating a cyclical feedback loop. The model assumes the sequential passage of data through the stages of collection, verification, normalization, intelligent analysis, and decision-making, followed by the return of the result to the system for further enrichment. This closed loop gradually increases data reliability without the need for a complete overhaul of the existing infrastructure. The author also examines the role of the human factor in a highly automated environment, pointing to the continuing importance of expert judgment in decision-making based on non-formalizable parameters. Additionally, the author introduces characteristics of key implementation barriers: fragmented corporate IT landscapes, a lack of standards for data exchange between systems, and a lack of personnel competencies to support intelligent decisions. The obtained results have clear practical applicability for organizations with heterogeneous information systems seeking to reduce operational costs and improve process resilience. The proposed integrated approach forms a methodological framework suitable for adaptation to industry specifics, particularly in logistics, manufacturing, and digital supply chain management.

Keywords: business process automation; artificial intelligence; logistics; data management; BPMN; data consistency; digital transformation

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