Customer Service Representative's Perception of the AI Assistant in an Organization's Call Center

• URL: http://arxiv.org/abs/2507.00513v1
• Date: Tue, 01 Jul 2025 07:27:34 GMT
• Title: Customer Service Representative's Perception of the AI Assistant in an Organization's Call Center
• Authors: Kai Qin, Kexin Du, Yimeng Chen, Yueyan Liu, Jie Cai, Zhiqiang Nie, Nan Gao, Guohui Wei, Shengzhu Wang, Chun Yu,
• Abstract summary: This study investigates how customer service representatives perceive AI assistance in their interactions with customers.<n>We found that AI can alleviate some traditional burdens during the call but also introduces new burdens.<n>This research contributes to a more nuanced understanding of AI integration in organizational settings.
• Score: 20.676082015030012
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The integration of various AI tools creates a complex socio-technical environment where employee-customer interactions form the core of work practices. This study investigates how customer service representatives (CSRs) at the power grid service customer service call center perceive AI assistance in their interactions with customers. Through a field visit and semi-structured interviews with 13 CSRs, we found that AI can alleviate some traditional burdens during the call (e.g., typing and memorizing) but also introduces new burdens (e.g., earning, compliance, psychological burdens). This research contributes to a more nuanced understanding of AI integration in organizational settings and highlights the efforts and burdens undertaken by CSRs to adapt to the updated system.

Related papers

• Agentic Web: Weaving the Next Web with AI Agents [109.13815627467514]
  The emergence of AI agents powered by large language models (LLMs) marks a pivotal shift toward the Agentic Web.<n>In this paradigm, agents interact directly with one another to plan, coordinate, and execute complex tasks on behalf of users.<n>We present a structured framework for understanding and building the Agentic Web.
  arXiv  Detail & Related papers  (2025-07-28T17:58:12Z)
• Intelligent Mobile AI-Generated Content Services via Interactive Prompt Engineering and Dynamic Service Provisioning [55.641299901038316]
  AI-generated content can organize collaborative Mobile AIGC Service Providers (MASPs) at network edges to provide ubiquitous and customized content for resource-constrained users.<n>Such a paradigm faces two significant challenges: 1) raw prompts often lead to poor generation quality due to users' lack of experience with specific AIGC models, and 2) static service provisioning fails to efficiently utilize computational and communication resources.<n>We develop an interactive prompt engineering mechanism that leverages a Large Language Model (LLM) to generate customized prompt corpora and employs Inverse Reinforcement Learning (IRL) for policy imitation.
  arXiv  Detail & Related papers  (2025-02-17T03:05:20Z)
• CAMI: A Counselor Agent Supporting Motivational Interviewing through State Inference and Topic Exploration [41.03565985067749]
  This paper introduces CAMI, a novel automated counselor agent grounded in Motivational Interviewing (MI)<n>We evaluate CAMI's performance through both automated and manual evaluations.<n>Results show that CAMI not only outperforms several state-of-the-art methods but also shows more realistic counselor-like behavior.
  arXiv  Detail & Related papers  (2025-02-05T01:09:09Z)
• CRMArena: Understanding the Capacity of LLM Agents to Perform Professional CRM Tasks in Realistic Environments [90.29937153770835]
  We introduce CRMArena, a benchmark designed to evaluate AI agents on realistic tasks grounded in professional work environments.<n>We show that state-of-the-art LLM agents succeed in less than 40% of the tasks with ReAct prompting, and less than 55% even with function-calling abilities.<n>Our findings highlight the need for enhanced agent capabilities in function-calling and rule-following to be deployed in real-world work environments.
  arXiv  Detail & Related papers  (2024-11-04T17:30:51Z)
• A Learnable Agent Collaboration Network Framework for Personalized Multimodal AI Search Engine [14.123823081267336]
  This paper proposes a novel AI Search Engine framework called the Agent Collaboration Network (ACN) The ACN framework consists of multiple specialized agents working collaboratively, each with distinct roles such as Account Manager, Solution Strategist, Information Manager, and Content Creator. A highlight of the ACN is the introduction of a Reflective Forward Optimization method (RFO), which supports the online synergistic adjustment among agents.
  arXiv  Detail & Related papers  (2024-09-01T07:01:22Z)
• Constraining Participation: Affordances of Feedback Features in Interfaces to Large Language Models [49.74265453289855]
  Large language models (LLMs) are now accessible to anyone with a computer, a web browser, and an internet connection via browser-based interfaces. This paper examines the affordances of interactive feedback features in ChatGPT's interface, analysing how they shape user input and participation in iteration.
  arXiv  Detail & Related papers  (2024-08-27T13:50:37Z)
• The Ethics of Advanced AI Assistants [53.89899371095332]
  This paper focuses on the opportunities and the ethical and societal risks posed by advanced AI assistants. We define advanced AI assistants as artificial agents with natural language interfaces, whose function is to plan and execute sequences of actions on behalf of a user. We consider the deployment of advanced assistants at a societal scale, focusing on cooperation, equity and access, misinformation, economic impact, the environment and how best to evaluate advanced AI assistants.
  arXiv  Detail & Related papers  (2024-04-24T23:18:46Z)
• Synergizing Human-AI Agency: A Guide of 23 Heuristics for Service Co-Creation with LLM-Based Agents [16.560339524456268]
  This study serves as a primer for interested service providers to determine if and how Large Language Models (LLMs) technology will be integrated for their practitioners and the broader community. We investigate the mutual learning journey of non-AI experts and AI through CoAGent, a service co-creation tool with LLM-based agents.
  arXiv  Detail & Related papers  (2023-10-23T16:11:48Z)
• A system for Human-AI collaboration for Online Customer Support [16.22226476879187]
  We present a system where a human support agent collaborates in real-time with an AI agent to satisfactorily answer customer queries. We describe the user interaction elements of the solution, along with the machine learning techniques involved in the AI agent.
  arXiv  Detail & Related papers  (2023-01-28T11:07:23Z)
• Intent Recognition in Conversational Recommender Systems [0.0]
  We introduce a pipeline to contextualize the input utterances in conversations. We then take the next step towards leveraging reverse feature engineering to link the contextualized input and learning model to support intent recognition.
  arXiv  Detail & Related papers  (2022-12-06T11:02:42Z)
• Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration [116.28433607265573]
  We introduce Watch-And-Help (WAH), a challenge for testing social intelligence in AI agents. In WAH, an AI agent needs to help a human-like agent perform a complex household task efficiently. We build VirtualHome-Social, a multi-agent household environment, and provide a benchmark including both planning and learning based baselines.
  arXiv  Detail & Related papers  (2020-10-19T21:48:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.