AI-Powered Crisis Management: Revolutionizing Customer Service Emergencies
DOI:
https://doi.org/10.60087/jklst.v4.n2.010Abstract
This study examines the potential of AI as a disruptive technology to address brand crisis customer service by specifically analyzing the ability of AI to deliver personalized, real-time responses that can enhance customer perceptions of a firm’s handling of a crisis situation. The research design employed online surveys, semi-structured interviews with customer service workers, a controlled experiment, and a secondary analysis of cases in a mixed-methods study. Among the most relevant conclusions, Artificial Intelligence has been proven to be able to reduce response times to initial crisis responses while adding a personalization capability driven by data. For basic requests, AI can offer immediacy such that information queries are not provided. They also highlight the ongoing need for human intervention in emotional and complex situations where empathy and nuances are key.
This study indicates that an optimum level of customer satisfaction is achieved using a hybrid human interaction model. This study is limited by its narrow focus on specific AI technologies, relatively small sample size, lack of demographic diversity, and dependence on a simulated crisis environment. Practice implications indicate that the role of AI in mobilization should involve providing this type of information, an initial point of contact that can be reached quickly, and clear and easy pathways for moving problems to a human who can handle more complexity. Its value lies in informing companies of useful practical implications in organizing responses to disasters and building trust in AI.
Keywords: Artificial intelligence, crisis management, customer service, brand crisis, emergency response, customer satisfaction, natural language processing, chatbots, sentiment analysis, personalized communication.
Downloads
References
Pendyala, M. K., & Lakkamraju, V. V. (2024). Impact of Artificial Intelligence in Customer Journey. International Journal of Innovative Science and Research Technology (IJISRT), 1528–1534. https://doi.org/10.38124/ijisrt/ijisrt24aug807
Taherdoost, H. (2021). A Review on Risk Management in Information Systems: Risk Policy, Control and Fraud Detection. Electronics, 10(24), 3065. https://doi.org/10.3390/electronics10243065
Leocádio, D., Guedes, L., Oliveira, J., Reis, J., & Melão, N. (2024). Customer Service with AI-Powered Human-Robot Collaboration (HRC): A Literature Review. Procedia Computer Science, 232, 1222–1232. https://doi.org/10.1016/j.procs.2024.01.120
R, M., B, S. K., & Soju, A. V. (2024). Artificial Intelligence and Service Marketing Innovation (pp. 150–172). igi global. https://doi.org/10.4018/979-8-3693-2153-9.ch007
Rane, N. L. (2024). ChatGPT and similar generative artificial intelligence (AI) for smart industry: role, challenges, and opportunities for Industry 4.0, Industry 5.0, and Society 5.0. Innovations in Business and Strategic Management. https://doi.org/10.61577/ibsm.2024.100002
Coombs, W. T. (2017). Revising Situational Crisis Communication Theory (pp. 21–37). routledge. https://doi.org/10.4324/9781315749068-3
Sun, X., & Liu, W. (2023). Expanding Service Capabilities Through an On-Demand Workforce. Operations Research, 73(1), 363–384. https://doi.org/10.1287/opre.2021.0651
Jasmand, C., De Ruyter, K., & Blazevic, V. (2012). Generating Sales While Providing Service: A Study of Customer Service Representatives’ Ambidextrous Behavior. Journal of Marketing, 76(1), 20–37. https://doi.org/10.1509/jm.10.0448
Chang, T.-S., & Hsiao, W.-H. (2024). Understand resist use online customer service chatbot: an integrated innovation resist theory and negative emotion perspective. Aslib Journal of Information Management. https://doi.org/10.1108/ajim-12-2023-0551
Aslam, F. (2023). The Impact of Artificial Intelligence on Chatbot Technology: A Study on the Current Advancements and Leading Innovations. European Journal of Technology, 7(3), 62–72. https://doi.org/10.47672/ejt.1561
Bharadiya, J. P. (2023). The role of machine learning in transforming business intelligence. International Journal of Computing and Artificial Intelligence, 4(1), 16–24. https://doi.org/10.33545/27076571.2023.v4.i1a.60
Murugeah, M. K. (2024). Enhancing efficiency and Personalization in Food and Beverage Service through AI: Future Trends and Challenges. International Journal for Multidimensional Research Perspectives, 2(7), 01–17. https://doi.org/10.61877/ijmrp.v2i7.162
Kedi, W., Ijomah, T., Idemudia, C., & Ejimuda, C. (2024). AI Chatbot integration in SME marketing platforms: Improving customer interaction and service efficiency. International Journal of Management & Entrepreneurship Research, 6(7), 2332–2341. https://doi.org/10.51594/ijmer.v6i7.1327
Al-Shafei, M. (2024). Navigating Human-Chatbot Interactions: An Investigation into Factors Influencing User Satisfaction and Engagement. International Journal of Human–Computer Interaction, 41(1), 411–428. https://doi.org/10.1080/10447318.2023.2301252
Anane-Simon, R., & Atiku, S. O. (2023). Artificial Intelligence and Automation for the Future of Startups (pp. 133–153). igi global. https://doi.org/10.4018/979-8-3693-0527-0.ch007
Adam, H., Ghassemi, M., Balagopalan, A., Alsentzer, E., & Christia, F. (2022). Mitigating the impact of biased artificial intelligence in emergency decision-making. Communications Medicine, 2(1). https://doi.org/10.1038/s43856-022-00214-4
Darzi, P. (2023). Could Artificial Intelligence be a Therapeutic for Mental Issues? Science Insights, 43(5), 1111–1113. https://doi.org/10.15354/si.23.co132
Gelbrich, K. (2009). Beyond Just Being Dissatisfied: How Angry and Helpless Customers React to Failures When Using Self-Service Technologies. Schmalenbach Business Review, 61(1), 40–59. https://doi.org/10.1007/bf03396779
Kalogiannidis, S., Papaevangelou, O., Kalfas, D., Chatzitheodoridis, F., & Giannarakis, G. (2024). The Role of Artificial Intelligence Technology in Predictive Risk Assessment for Business Continuity: A Case Study of Greece. Risks, 12(2), 19. https://doi.org/10.3390/risks12020019
Osasona, F., Atadoga, A., Amoo, O., Abrahams, T., Farayola, O., & Ayinla, B. (2024). REVIEWING THE ETHICAL IMPLICATIONS OF AI IN DECISION MAKING PROCESSES. International Journal of Management & Entrepreneurship Research, 6(2), 322–335. https://doi.org/10.51594/ijmer.v6i2.773
Bing, Z. J., & Leong, W. Y. (2025). Ethical Design of AI for Education and Learning Systems. ASM Science Journal, 20(1), 1–9. https://doi.org/10.32802/asmscj.2025.1917
Cui, P., & Alias, B. S. (2024). Opportunities and challenges in higher education arising from AI: A systematic literature review (2020–2024). Journal of Infrastructure, Policy and Development, 8(11), 8390. https://doi.org/10.24294/jipd.v8i11.8390
Um, T., Chung, N., & Kim, T. (2020). How does an Intelligence Chatbot Affect Customers Compared with Self-Service Technology for Sustainable Services? Sustainability, 12(12), 5119. https://doi.org/10.3390/su12125119
Bhuiyan, M. S. (2024). The Role of AI-Enhanced Personalization in Customer Experiences. Journal of Computer Science and Technology Studies, 6(1), 162–169. https://doi.org/10.32996/jcsts.2024.6.1.17
Geske, A. M., Herold, D. M., & Kummer, S. (2024a). Integrating AI support into a framework for collaborative decision-making (CDM) for airline disruption management. Journal of the Air Transport Research Society, 3, 100026. https://doi.org/10.1016/j.jatrs.2024.100026
Karinshak, E., & Jin, Y. (2023). AI-driven disinformation: a framework for organizational preparation and response. Journal of Communication Management, 27(4), 539–562. https://doi.org/10.1108/jcom-09-2022-0113
Okeleke, P., Ezeigweneme, C., Ajiga, D., & Folorunsho, S. (2024). Predictive analytics for market trends using AI: A study in consumer behavior. International Journal of Engineering Research Updates, 7(1), 036–049. https://doi.org/10.53430/ijeru.2024.7.1.0032
Edilia, S., & Larasati, N. D. (2023). Innovative Approaches in Business Development Strategies Through Artificial Intelligence Technology. IAIC Transactions on Sustainable Digital Innovation (ITSDI), 5(1), 84–90. https://doi.org/10.34306/itsdi.v5i1.612
Vatankhah, S., Bamshad, V., Arici, H. E., & Duan, Y. (2024). Ethical implementation of artificial intelligence in the service industries. The Service Industries Journal, 44(9–10), 661–685. https://doi.org/10.1080/02642069.2024.2359077
Ulnicane, I. (2024). Intersectionality in Artificial Intelligence: Framing Concerns and Recommendations for Action. Social Inclusion, 12. https://doi.org/10.17645/si.7543
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)

This work is licensed under a Creative Commons Attribution 4.0 International License.
©2024 All rights reserved by the respective authors and JKLST.