The rapid advancement of artificial intelligence (AI) fundamentally transformed the business environment by optimising management processes, enhancing operational efficiency, and generating new challenges for enterprises. This necessitated a comprehensive analysis of its impact on the transformation of business processes. The objective of the study was to examine the influence of AI on the transformation of business processes in enterprises of various sizes, identify primary directions of its application, and assess development prospects. The study was grounded in the use of systemic analysis, comparative analysis, case study method, and statistical data analysis. Scientific publications, corporate reports, analytical materials, and practical implementation cases were processed. The findings revealed that the adoption of AI significantly increased the efficiency of business processes in large enterprises through operational automation, customer interaction personalisation, and enhanced market forecasting. In the financial sector, machine learning algorithms reduced fraud rates and expedited document processing, consistent with global practices in banks such as JPMorgan Chase, Mizuho, and SMFG. In retail, improvements in recommendation system accuracy were observed, as reported by global companies like Amazon and Netflix, where personalisation contributed to increased conversion rates. Manufacturing enterprises reported optimised logistics and reduced operational costs due to AI implementation. Furthermore, enterprises actively explored the potential of generative AI in marketing, product development, and employee productivity, yielding results aligned with international experience, including the deployment of GitHub Copilot, which improved the speed and quality of software development. The practical value of the study lay in the applicability of its results for executives, analysts, and consultants to enhance digital transformation strategies and improve enterprise competitiveness
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Andriushchenko et al. (Wed,) studied this question.
www.synapsesocial.com/papers/68c1d5e554b1d3bfb60f899c — DOI: https://doi.org/10.59214/mb/2.2024.18
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