Research on AI-Enabled Project Planning and Schedule Management

Abstract

Project planning and schedule management directly affect whether a project can be completed within the specified time. Traditional management approaches mainly rely on project managers’ experience to decompose tasks, estimate durations, and allocate resources, while meetings, reports, and manual inspections are used to monitor project implementation. Although this approach offers considerable flexibility, it may lead to insufficient planning evidence, delayed information feedback, and untimely schedule adjustments when projects become larger, task relationships become more complex, and resources change frequently. Artificial intelligence technologies provide new methods for improving project planning and schedule management through their capabilities in data processing, pattern recognition, trend forecasting, and solution optimization. Using literature review and inductive analysis, this study examines the role of artificial intelligence in project planning, task-duration forecasting, resource allocation, schedule monitoring, deviation warning, and dynamic adjustment. The findings indicate that artificial intelligence can improve the accuracy of project planning, help managers identify schedule risks in advance, and provide adjustment recommendations when the project environment changes. However, its application is also constrained by data quality, model applicability, system integration, staff capabilities, and unclear responsibility boundaries. To realize the practical value of artificial intelligence, enterprises should not merely purchase software or deploy algorithms. They should improve project data standards, integrate artificial intelligence into management processes, establish mechanisms for human review and model evaluation, and strengthen project teams’ ability to understand and use data. Artificial intelligence is more suitable as an assistive tool for project managers than as a complete substitute for human judgment. By combining data analysis with management experience, enterprises can improve the feasibility of project plans and enhance the proactive control of project schedules.