Agentic Voice AI in Enterprise Call Centers: Data-Driven Cost-Benefit and Strategic Analysis of RAG-Powered Automation in Financial Services and E-commerce

Bhogawar, Nachiket Anantrao (2025) Agentic Voice AI in Enterprise Call Centers: Data-Driven Cost-Benefit and Strategic Analysis of RAG-Powered Automation in Financial Services and E-commerce. World Journal of Advanced Research and Reviews, 27 (2). pp. 1984-1990. ISSN 2581-9615

The convergence of Retrieval-Augmented Generation (RAG) and agentic voice AI is revolutionizing enterprise call centers—particularly in financial services and e-commerce—by automating complex workflows, increasing compliance, and delivering measurable cost savings at scale. Through multi-source quantitative analysis and case studies such as Bank of America’s Erica (serving 42 million users), HSBC’s Voice ID (£249 million fraud prevented), and NIB Health (saving $22 million annually), this paper demonstrates that RAG-enabled voice agents reduce average handle time by 40–60%, boost first-contact resolution by up to 30%, and enable enterprise-wide operational cost reductions exceeding $7.9 billion annually. Break-even is typically reached within 24 months, and 5-year ROI regularly exceeds 125% as adoption barriers decline and no-code platforms mature. Beyond the numbers, the research highlights essential success factors: hybrid human-AI collaboration, comprehensive compliance frameworks, and agile orchestration tools. These findings provide both a blueprint and a business case for product managers and enterprise leaders seeking scalable, compliant, and human-centric automation in high-volume, regulated environments.