Enhancing Risk Identification in Software Project Management through Artificial Intelligence and Machine Learning

The increasing complexity of software development projects has amplified the challenges associated with risk identification and management.Traditional risk identification methods, such as expert judgment and historical data analysis, often struggle to account for dynamic and unforeseen risks in rapidly evolving project environments.This paper explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques in project risk management, with a focus on software development projects.It examines the potential of AI/ML models to automate risk identification, analyse large datasets, and provide predictive insights to mitigate project uncertainties.Furthermore, challenges related to AI adoption, such as data quality and interpretability, are addressed.The findings contribute hope to contribute on AI-driven project management and emphasize the necessity of integrating intelligent systems for more proactive and efficient risk mitigation strategies.