Economic efficiency of implementing adaptive AI interfaces in e-commerce systems

Abstract

The study’s relevance stems from the growing role of e-commerce in the digital economy and the need to improve the efficiency of user interaction with online platforms amid high competition. The article aims to assess the economic efficiency of implementing adaptive AI interfaces in e-commerce systems by substantiating their impact on the effectiveness of digital business processes, user behavioral parameters, and key economic indicators of the functioning of online platforms.

Methods of theoretical generalization and system analysis are used to study the evolution of interface solutions; comparative analysis to determine the impact of personalization on user behavior; economic and analytical approaches to assess the effectiveness of digital transformations; and methods for interpreting the results of digital analytics and behavioral data.

The impact of adaptive AI interfaces on the transformation of user behavioral characteristics and on key indicators of e-commerce economic efficiency is determined. It has been proven that interface personalization increases conversion rates, improves the efficiency of marketing costs, and optimizes operational processes on digital platforms. The feasibility of an integrated methodological approach to assessing the economic efficiency of implementing adaptive solutions, combining investment, operational, behavioral, and strategic levels of analysis, is substantiated, and key scientific and practical problems associated with the technological complexity of integration, algorithmic opacity, and risks of data use are identified.

It is established that adaptive AI interfaces serve as a tool for strategically increasing the competitiveness of digital platforms, enabling a transition from a reactive e-commerce model to proactive management of user interactions. The rationality of the phased implementation of adaptive solutions, supported by experimental testing and the integration of behavioral analytics with economic indicators of enterprise activity, is proven.

Prospects for further research include developing interpretable artificial intelligence models, improving methods for quantitatively measuring the economic effects of personalization, and studying the impact of adaptive interfaces on the formation of competitive dynamics in digital markets and the long-term sustainability of e-commerce.