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This paper explores the application of artificial intelligence (AI) in enhancing technical support operations through predictive maintenance and problem resolution. The objective is to examine how AI-driven solutions can optimize support efficiency, reduce downtime, and improve overall customer satisfaction. The research methodology involves a comprehensive review of existing literature, case studies, and the implementation of AI models in a controlled technical support environment. Key findings indicate that AI can significantly improve predictive maintenance by analyzing historical data, identifying patterns, and forecasting potential system failures before they occur. This proactive approach not only minimizes operational disruptions but also extends the lifespan of technical equipment. Additionally, AI-powered problem resolution tools, such as chatbots and virtual assistants, have demonstrated their ability to provide real-time support, reduce response times, and handle a large volume of inquiries with high accuracy. The study also highlights the integration of machine learning algorithms in technical support workflows, enabling continuous learning and adaptation to new issues. By automating routine tasks and providing data-driven insights, AI facilitates more efficient allocation of human resources to complex problems that require expert intervention. The utilization of AI in predictive maintenance and problem resolution presents a transformative opportunity for technical support operations. The findings underscore the potential for AI to not only enhance operational efficiency and reliability but also to deliver superior customer experiences. Future research should focus on scaling AI applications across diverse technical environments and addressing challenges related to data privacy and algorithmic bias.
Folorunsho et al. (Fri,) studied this question.