Abstract
The study is focused on the formation of an integrated financial model for the implementation of AI agents in customer service and the evaluation of their impact on operational efficiency, business margin, and customer LTV (Lifetime Value). The task appears simple. In fact, it is not. The relevance of the topic is explained by the fact that scientific approaches to assessing the economic effect of AI remain fragmented. Because of this, it is difficult to see a holistic picture of the relationship between costs, productivity, and customer behavior. In this work, attention is focused on the development of a model that combines operational indicators and behavioral characteristics within a single system for evaluating the financial results of an enterprise. The methodology is of a conceptual-analytical type with system and comparative analysis methods. Most similar studies are like that. But at the same time, the results of empirical studies from 2022–2026 are taken into account, in which the impact of AI agents on productivity, costs and customer behavior is quantitatively assessed. As part of this method, a synthesis of scientific positions was carried out and, on this basis, an analytical model of relationships between key financial indicators was built. Data matters. The obtained results show that the implementation of AI agents increases operational efficiency. This occurs through the automation of repetitive processes, the reduction of request processing time, and a decrease in variable costs. The effect of scale works. Increasing volumes without proportional growth in resources becomes a key factor in increasing business margins. At the same time, the improvement of customer experience, in particular through the personalization of interaction, is associated with the growth of retention rate and LTV. As a result, the model shows that ROI is formed under the influence of both operational and behavioral changes. It has also been established that the effectiveness of implementing AI agents depends on the context. Everything depends on the conditions. Decisive importance is given to the level of digital maturity of the enterprise, the type of customer scenarios, and the balance between automation and human involvement. The practical value lies in the possibility of using this model to justify investments in AI solutions, evaluate their financial efficiency, and forecast long-term business results.
References
1. Aguiar-Costa, L. M., Cunha, C. A. X. C., Silva, W. K. M., & Abreu, N. R. (2022). Customer satisfaction in service delivery with artificial intelligence: A meta-analytic study. RAM. Revista de Administração Mackenzie, 23(6), eRAMD220003. https://doi.org/10.1590/1678-6971/eRAMD220003.en
2. Agrawal, G., De Maria, R., Davuluri, K., Spera, D., Read, C., Spera, C., Garrett, J., & Miller, D. (2025). Redefining CX with agentic AI: Minerva CQ case study. arXiv. https://doi.org/10.48550/arXiv.2509.12589
3. Ali, N., & Shabn, O. S. (2024). Customer lifetime value (CLV) insights for strategic marketing success and its impact on organizational financial performance. Cogent Business & Management, 11(1), 2361321. https://doi.org/10.1080/23311975.2024.2361321
4. Brunswicker, S., Zhang, Y., Rashidian, C., & Linna, D. W., Jr. (2025). Trust through words: The systemize-empathize effect of language in task-oriented conversational agents. Computers in Human Behavior, (165), 108516. https://doi.org/10.1016/j.chb.2024.108516
5. Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at work (NBER Working Paper No. 31161). National Bureau of Economic Research. https://doi.org/10.3386/w31161
6. Chen, H., Ng, E., Smyl, S., & Steininger, G. (2024). Predicting customer lifetime value using recurrent neural net (arXiv:2412.20295v2). arXiv. https://doi.org/10.48550/arXiv.2412.20295
7. Chen, Y., & Prentice, C. (2025). Integrating artificial intelligence and customer experience. Australasian Marketing Journal, 33(2), 141–153. https://doi.org/10.1177/14413582241252904
8. Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709–1734. https://doi.org/10.1007/s10796-021-10186-w
9. Fang, L., Yuan, Z., Zhang, K., Donati, D., & Sarvary, M. (2025). Generative AI and firm productivity: Field experiments in online retail (arXiv:2510.12049v3). arXiv. https://doi.org/10.48550/arXiv.2510.12049
10. Hardcastle, K., Vorster, L., & Brown, D. M. (2025). Understanding customer responses to AI-driven personalized journeys: Impacts on the customer experience. Journal of Advertising, 54(2), 176–195. https://doi.org/10.1080/00913367.2025.2460985
11. Kassa, B. Y., & Worku, E. K. (2025). The impact of artificial intelligence on organizational performance: The mediating role of employee productivity. Journal of Open Innovation: Technology, Market, and Complexity, 11(1), 100474. https://doi.org/10.1016/j.joitmc.2025.100474
12. Ledro, C., Nosella, A., Vinelli, A., Dalla Pozza, I., & Souverain, T. (2025). Artificial intelligence in customer relationship management: A systematic framework for a successful integration. Journal of Business Research, (199), 115531. https://doi.org/10.1016/j.jbusres.2025.115531
13. Marcineková, K., Sujová, A. J., & Ďurica, R. (2025). Implementing AI chatbots in customer service optimization—A case study in micro-enterprise. Information, 16(12), 1078. https://doi.org/10.3390/info16121078
14. Ngo, T. T. A., Chau, H. K. L., Tran, N. P. N., & Bui, C. T. (2026). The impact of AI-powered service on customer continuance usage intention in e-retailing: An extended expectation confirmation model. Acta Psychologica, (262), 106095. https://doi.org/10.1016/j.actpsy.2025.106095
15. Pitardi, V., Bartikowski, B., Osburg, V.-S., & Yoganathan, V. (2023). Effects of gender congruity in human–robot service interactions: The moderating role of masculinity. International Journal of Information Management, (70), 102489. https://doi.org/10.1016/j.ijinfomgt.2022.102489
16. Prodanchuk, M., Bezdushna, Y., Tripak, M., Bernaziuk, O., Shevchuk, N., & Vlasov, V. (2025). Accounting, analytical, and organizational-legal support for managing marketing activities and their impact on the financial results of the enterprise. Financial and Credit Activity Problems of Theory and Practice, 5(64), 486–496. https://doi.org/10.55643/fcaptp.5.64.2025.4849
17. Rana, S., Singh, S. K., & Chandel, A. (2024). AI in customer service automation: Balancing efficiency with human touch. In AI, corporate social responsibility, and marketing in modern organizations (pp. 173–194). IGI Global. https://doi.org/10.4018/979-8-3373-0219-5.ch009
18. Yao, A., Jia, F., Jiang, L., Shi, Z., & Wei, Q. (2025). Artificial intelligence customer service adoption strategy in platform supply chain: Strategic interactions and consumer automation aversion. European Journal of Operational Research. Advance online publication. https://doi.org/10.1016/j.ejor.2025.12.032
19. Zhao, L., & Wu, W. (2025). Is customer service innovation always preferable? The impact of AI technology on an online retailer’s customer service decision. Journal of Theoretical and Applied Electronic Commerce Research, 20(2), 101. https://doi.org/10.3390/jtaer20020101
20. Zoppelletto, A., Bullini Orlandi, L., Zardini, A., Rossignoli, C., & Kraus, S. (2023). Organizational roles in the context of digital transformation: A micro-level perspective. Journal of Business Research, (157), 113563. https://doi.org/10.1016/j.jbusres.2022.113563
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