Intelligent automation of bank contact centers: AI architecture and data-driven performance management (The case of Alfa-Bank)

Vladimir Vyacheslavovich  Verkhoshinskiy

doi:10.17323/2587-814X.2026.3.7.28

Vladimir Vyacheslavovich Verkhoshinskiy Alfa-Bank, Moscow, Russia https://orcid.org/0009-0005-3600-2619

DOI: https://doi.org/10.17323/2587-814X.2026.3.7.28

Keywords: artificial intelligence, intelligent automation, bank contact centers, large language models, data-driven management, key performance indicators, human–AI interaction

Abstract

The scaling of artificial intelligence (AI) in bank contact centers is transforming customer service technologies and the mechanisms for managing operational performance. However, it remains insufficiently understood how AI architecture, data management, and the performance measurement system jointly convert technological capabilities into sustained outcomes while meeting the requirements of information and technological sovereignty. This study aims to identify the conditions for effective intelligent automation of a bank contact center and to assess the dynamics of the bank’s key operational indicators. An exploratory study of Alfa-Bank’s practice was conducted, supplemented by secondary quantitative analysis. The empirical base comprises time series of key performance indicators (KPIs) for 2023–2026, AI deployment documentation, competitive benchmarking materials, and corporate and open sources. The study employed data triangulation, architecture mapping, trend analysis, and process tracing. The theoretical foundation combines the resource-based view, dynamic capabilities theory, and the concept of the data-driven organization. The findings reveal a five-layer technology platform integrating customer-facing conversational systems, AI-powered agent assistance, enterprise knowledge management, automated quality assurance, and personalized learning. Generative AI extends each layer, enabling a shift from fragmented automation to an integrated, context-aware, data-driven AI environment. The effectiveness of AI is mediated by a management system that simultaneously monitors automation and customer satisfaction. In 2024, insufficient technological maturity led the bank to lower its target automation rate from 52% to 43% in order to preserve service quality. As contact volumes grew, this increased staffing requirements and the cost of serving an active customer. In 2025, systematic data management and the deployment of generative AI made it possible to raise the automation rate to 56% while maintaining quality. The study conceptualizes AI as infrastructure for organizational learning and management. It demonstrates that a dual-constraint KPI architecture and institutionalized competitive benchmarking constitute organizational capabilities that convert technological innovation into measurable service and economic outcomes.

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