Year: 2025
Type: article
Abstract: 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.
Authors: Victor Ogbo Akande, Debo Joseph Oyana
Institution: South Ural State University
Language: English
Topic: AI in Service Interactions
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412056290