USING ARTIFICIAL INTELLIGENCE FOR RISK MANAGEMENT IN PROJECTS WITH THE SCRUM METHODOLOGY

Abstract

In the rapidly evolving area of Information Technology projects, the integration of Artificial Intelligence into project risk management assessment processes represents an innovative approach that could pitch a risk evaluation process. The study investigates the standard risk management strategies and their limitations in the context of agile project environments by Scrum methodology. Standard methodologies for risk management often fall short in dynamically adapting to the rapidly changing schedule and evolving requirements characteristic of Agile projects. This gap in effectiveness becomes especially important when dealing with projects managed under the Scrum methodology, which prioritizes flexibility, rapid iteration, and stakeholder involvement. The study proposes an innovative solution to this problem through the application of AI technologies, which can significantly enhance both the precision and depth of risk assessments. The study explores several AI models, highlighting their potential to revolutionize risk management in IT projects. One of the key insights from the study is the ability of AI to process and analyze big amounts of data at a speed and efficiency that is impossible to achieve with a regular expert team. This capability allows for real-time risk assessment, enabling project teams to make informed decisions quickly and adapt their strategies to mitigate potential issues before they grow into significant problems. Moreover, AI's predictive analytics can forecast potential risks based on historical data, allowing for proactive risk management rather than reactive. The research further discusses the implementation challenges and considerations for incorporating AI into Scrum-based projects, including ethical considerations, data privacy concerns, and the need for interdisciplinary expertise to design, deploy, and manage AI-enhanced risk management systems.