Respond speed, information accuracy, and query complexity: Understanding AI chatbot in customer service

Abstract

This paper examines the role of AI chatbots in customer service, focusing on their challenges in handling complex queries and the effects of information accuracy and response time on customer satisfaction. Using Cognitive Load Theory, we argue that chatbot effectiveness depends on the cognitive effort required for different query types. The investment industry was selected for its high demands on privacy, security,and quick decision-making. Analysis of 648 customer chatusing Process Macro shows that AI chatbots improvesatisfactionfasterresponses and more accurate information, especially for low-complexity queries, while humans perform better on complex issues. A second study with 352 students found similarpatterns: participants preferred chatbots for simple tasks and human representatives for complex ones due to the need for personalization and deeper understanding. Overall, thefindings highlight the importance of balancing speed and accuracy ininteractions and offer practical guidance on how businesses can allocate tasks between AI systems and human agents based on query complexity. © 2026 by IGI Global Scientific Publishing. All rights reserved.