Modern approaches to the automation of task management for small teams using machine learning methods

DOI: 10.31673/2412-9070.2025.023411

Abstract

Text of annotation translation – Modern project and task management technologies offer numerous tools that provide basic functions for organizing team work. However, most of these solutions are focused on large companies, which often makes them too complex for small teams. The lack of tools for automated forecasting of task completion times and determining their priorities limits the effectiveness of such systems. Small teams need flexible solutions that reduce the burden on work organization, automate routine tasks, and promote rational resource allocation. Therefore, the problem is the lack of automated tools for accurately predicting task completion times and determining their priority, which leads to inefficient resource allocation and work delays.
The main approaches to task management automation are analyzed, in particular, methods that can be used to forecast task completion times and determine priorities using machine learning. Special attention is paid to the TF-IDF, Random Forest Regressor, and K-means algorithms. TF-IDF allows for efficient processing of text descriptions of tasks, converting them into numerical features, which provides an analytical basis for the operation of machine learning models. Random Forest Regressor is used to accurately predict task completion times, which helps teams plan the workflow. The K-means algorithm is used to cluster tasks by their importance and complexity, providing auto matic prioritization.
Popular tools such as Trello, Asana, Jira, Wrike, provide basic functionality for task management, but do not use machine learning methods for automation. The considered approaches can be integrated into existing systems or implemented as a separate solution for small teams seeking to increase productivity without significant costs for complex platforms.
The results of the study emphasize the importance of using machine learning to automate task management. This approach allows you to reduce dependence on the human factor, reduce the time for organizing tasks and optimize the planning process. In addition, it helps to increase the efficiency of teamwork, which is especially important for small teams working in conditions of limited resources.

Keywords: task management automation; machine learning; time forecasting; clustering; TF-IDF; Random Forest Regressor; K-means.