Leveraging artificial intelligence for business performance improvement
Abstract
The object of research is Artificial Intelligence (AI) as a strategic tool for the transformation of modern business processes. The key characteristics of AI in a business context include its ability to process large datasets, automate operations, optimize resources, and create a personalized customer experience, which collectively impacts a company's performance.
Problem statement. In the context of accelerated digitalization, businesses face the fundamental problem of transitioning from a general understanding of AI's potential to its practical integration to achieve measurable financial results. It is necessary to clearly identify and systematize the specific mechanisms through which AI technologies directly affect profitability.
Unresolved aspects of the problem. Despite a significant number of studies on specific aspects of AI application, there is a lack of a comprehensive analysis that would systematize its impact. Additionally, insufficient attention has been paid to developing a practical roadmap for AI integration.
The purpose of the article. The purpose of the article is to systematize the key mechanisms of Artificial Intelligence's influence on business performance and to develop a structured approach for its strategic implementation to maximize profitability.
Presentation of the main material. The study employs a systematic analysis method to structure AI's impact on business through the "dual-engine" model. Tools such as intelligent automation, predictive maintenance, dynamic pricing, and hyper-personalization are examined. Case studies of leading Ukrainian companies have been analyzed.
Conclusions. It is established that AI acts as a profitability catalyst, synergistically affecting both the reduction of operational costs and the acceleration of revenue growth. It is substantiated that successful integration requires a clear strategy, a phased approach and cultural adaptation. The absence of an AI strategy in the modern business environment is a conscious choice in favor of losing a competitive advantage.
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References
Asad, M., Tuffaha, M., & Al-Hussein, M. (2023). A review of artificial intelligence applications in the logistics and transportation industry. Cleaner Logistics and Supply Chain, 7, 100101. DOI: https://doi.org/10.1016/j.clscn.2023.100101
Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, 57(7), 2179-2202. Retrieved from: http://dx.doi.org/10.1080/00207543.2018.1530476
Carvalho, T. P., Soares, F. A., Vita, R., Francisco, R. D. P., Basto, J. P., & Alcalá, S. G. (2021). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 158, 107680. Retrieved from: http://dx.doi.org/10.1016/j.cie.2019.106024
Cohen, M. C., Perakis, G., & Pindyck, R. S. (2017). Dynamic pricing in a non-stationary environment. Management Science, 63(3), 647-660. DOI: https://doi.org/10.1287/mnsc.2015.2341
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
den Boer, A. V. (2023). A review of dynamic pricing: From theory to data-driven applications. European Journal of Operational Research, 307(2), 487-505. Retirieved from: https://doi.org/10.1016/j.ejor.2022.09.022
Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., ... & Wang, Y. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59, 102168. DOI: https://doi.org/10.1016/j.ijinfomgt.2020.102168
Gupta, S., Meissonier, R., Drave, V. A., & Roubaud, D. (2021). The future of artificial intelligence in marketing: A taxonomy and research agenda. Journal of Business Research, 128, 77-90. DOI: https://doi.org/10.1016/j.jbusres.2021.01.034
Horiach, H., & Horiach, V. (2023). Transformation of supply chains and logistics with the help of artificial intelligence to increase efficiency and profitability. Herald of Khmelnytskyi National University. Economic sciences, 6, 137-143. [in Ukrainian].
Illiashenko, S. M., & Shypulina, Yu. S. (2023). Marketing dynamic pricing as a progressive trend of the digital society. Problems of systemic approach in economy, 1(90), 49-55. DOI: https://doi.org/10.32752/2520-2200-2023-1-90-49-55 [in Ukrainian].
Krakovetska, L. M. (2021). Transformation of business processes of enterprises in the conditions of digitalization. Economics and management of the national economy, 41, 68-73. [in Ukrainian].
Kumar, V., Rajan, B., Singh, N. D., & Singh, D. L. (2019). Getting to “smarter” marketing: How AI is changing the game. Journal of the Academy of Marketing Science, 47(6), 1125-1131.
Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement, and impact on firm performance. Information & Management, 58(3), 103434. DOI: https://doi.org/10.1016/j.im.2021.103434
Pavlovskyi, M. V., Hatska, L. P., & Zavadska, O. M. (2023). Digital transformation of business in the conditions of modern changes. Economy and society, 50. DOI: https://doi.org/10.32782/2524-0072/2023-50-31 [in Ukrainian].
Prychepa, Y. Y., & Kobets, V. M. (2022). Artificial intelligence as a tool for increasing the efficiency of the enterprise's marketing activities. Herald of Khmelnytskyi National University. Economic sciences, 2, 194-199. [in Ukrainian].
Trushkina, N. V. (2021). Digital transformations in logistics and supply chain management based on artificial intelligence. Economic Herald of the Donbas, 1(63), 114-125. Retrieved from: https://www.google.com/search?q=https://doi.org/10.12958/1817-3772-2021-1(63)-114-125 [in Ukrainian].
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