Supervisor

Neil Doyle

Programme

MA in International Business

Keywords

Efficiency-Empathy Paradox, Artificial Intelligence, Customer Service Operations

Abstract

This research investigates the "Efficiency-Empathy Paradox" within the customer service operations of a global tier-one lithium-ion system supplier, focusing on the tension between AI-driven operational speed and human emotional intelligence. While Artificial Intelligence (AI) has optimized transactional queries, "blind routing" architectures—where human capital is misallocated to mechanical tasks—generate significant financial leakage.

Adopting an Explanatory Sequential Mixed Methods design, this study first analyzed a secondary dataset of 10,000 interactions. The quantitative findings revealed a baseline operational cost of $50,281.86. To address this, the study proposes and simulates the Adaptive Empathy Hybrid Framework (AEHF), a sentiment-based routing protocol designed to maximize AI deflection for mechanical tasks while reserving human experts for complex disputes. Results from the optimized simulation demonstrate a transformative impact: a 35.0% reduction in total operational costs, achieving $17,575.51 in net savings. Furthermore, the model improved operational efficiency by reducing average response times from 4.34 to 1.81 minutes. These findings provide a data-driven road-map for the leadership at BMZ Germany GmbH to transform customer service from a cost center into a lean, strategic retention engine.

Date of Award

2026

Full Publication Date

2026

Access Rights

open access

Document Type

Capstone Project

Resource Type

thesis

Digital Object Identifier (DOI)

https://doi.org/10.63227/652.299.170

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