Artificial Intelligence Model for Citizen Service in Mixed-Economy Companies - Paz, William Higuera; Blanco, Fabiola Sáenz (International Journal of Intelligent Systems and Applications in Engineering, 2023)
In a competitive and constantly evolving world, excellence in customer service is critical to business success; however, the management of PQRS (Petitions, Complaints, Claims, and Suggestions) can be challenging. In this context, data-driven artificial intelligence, artificial intelligence emerges as a powerful transformational tool in this area, using natural language processing algorithms and data analytics, and can provide personalized and rapid responses, thereby improving the customer experience. Similarly, machine learning is critical as it enables automation and improves the quality of responses; furthermore, machine learning systems can analyze large volumes of information, identify patterns and trends, and learn from feedback. These systems can understand the content of requests and generate accurate and relevant responses, adapting to the needs of each user, optimizing customer service, and improving problem resolution. This thesis presents a practical approach for mixed-economy companies interested in optimizing their customer service processes as follows: data-driven artificial intelligence can drive operational efficiency and overall business success; the combination of industrial engineering and artificial intelligence offers opportunities to optimize processes; and simulation and data analytics facilitate decision making, empowering industrial engineers to efficiently optimize processes.