MODELING FINANCIAL INDICATORS OF AI AGENT IMPLEMENTATION: OPTIMIZING BUSINESS MARGIN AND CUSTOMER SERVICE LTV

Анотація

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.

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