ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ РОЗПОДІЛУ ЗАДАЧ ЗА ДОПОМОГОЮ ШІ

Abstract

Listen

Translate article

This research is dedicated to the development and implementation of an approach aimed at enhancing the efficiency of task allocation among support service agents using Artificial Intelligence (AI). With the growing volume of data and the increasing complexity of customer issues, the traditional approach of manually assigning tasks has become less effective, leading to delays in issue resolution, overloading of agents, and inefficient use of resources. Modern companies actively seek innovative solutions to optimize this process, with AI becoming a key tool for achieving the desired outcomes. The foundation of this approach is the principle of automating task distribution based on the analysis of employees' qualifications, experience, and current workload. AI is capable of automatically processing input data about tasks and identifying the most suitable executor. This reduces the time needed to resolve a task and improves its quality. The approach consists of seven sequential stages: from gathering information about employees and tasks to a retrospective evaluation of task performance. At each stage, AI uses specifically designed instructions to help it execute tasks, make decisions regarding distribution, and predict potential risks. The approach provides for collecting information about employees, analyzing the task, identifying the necessary skills to complete it, matching these skills to employee profiles, predicting potential risks, and offering recommendations for their avoidance. The final stage is a retrospective evaluation of task performance effectiveness and feedback from users. For each stage, specific AI instructions have been developed, which include examples of input data, queries, and expected results. This ensures the flexibility and accuracy of the model's operation at all stages of the process. As a result, the proposed approach allows companies to more effectively distribute tasks among employees, considering their experience, qualifications, and current workload. It enhances the quality of customer service, increases job satisfaction, and optimizes the use of company resources.