AI Driven Strategies for Efficient Project Tracking and Delivery in Software Engineering Management

Abstract

The effective monitoring of progress and the timely execution of deliverables continue to present problems in the field of software engineering management owing to shifting requirements, resource limitations, and inadequate support for real-time decisions. This research intervention develops, and tests strategies powered by AI with programmatic and delivery success enhancements. We construct a framework that integrates machine learning models, Natural Language Processing (NLP), and predictive analysis to improve the accuracy of planning, as well as task and resource prioritization. The methodology has been validated with project data obtained from real life agile environments, measuring performance against other management techniques. Findings indicate a decrease of 27% in project delivery times, an improvement of 19% in accuracy of forecasts, and a decrease of 22% in latency of bug resolution lags. The AI approaches made in the study gave better transparency, responsiveness, and decision-making efficiency in different types of software projects AI approaches proposed in the study enhanced responsiveness, transparency, and efficiency of decision-making for various types of software projects. Further, the paper outlines the readiness for implementation, organizational considerations, and the consequences of using AI in project workflows for software engineering. The results form a basis to advance research and the use of AI in software engineering management in the industry.