AI-РІШЕННЯ ДЛЯ ОПТИМІЗАЦІЇ SCRUM: ПРОГНОЗУВАННЯ ПРОДУКТИВНОСТІ КОМАНДИ

This study presents the development, training, and AWS cloud deployment of an AI-based assistant leveraging an LSTM network to enhance Scrum team velocity prediction. The research focuses on analyzing the assistant’s interaction with key Scrum processes, highlighting its potential to optimize sprint planning and improve team performance forecasting. Through this analysis, specific sprint planning challenges suitable for AI-driven solutions were identified, paving the way for enhanced prediction accuracy and reduced uncertainty in project management. The proposed architecture outlines a logical sequence of integrated services that collectively contribute to improving Scrum process efficiency. Initial testing of a locally deployed LSTM network using a smaller dataset validated the suitability of the chosen model and confirmed its capability for accurate performance prediction. These findings establish a foundation for developing a scalable AI assistant capable of supporting Scrum teams in dynamic environments with evolving requirements. This research underscores the feasibility of applying AI technologies, particularly LSTM networks, to Scrum optimization. The results demonstrate significant potential for improving sprint planning, reducing uncertainty, and supporting adaptive project management strategies. The planned advancements in cloud-based deployment and performance evaluation will provide actionable insights into the economic and operational viability of integrating AI-driven prediction tools into real-world Scrum environments. Future work will focus on deploying the trained LSTM model in a production AWS environment to evaluate its practical performance, scalability, and operational costs. This stage will include detailed monitoring of computational resource usage and cost analysis to identify opportunities for optimization. By refining algorithmic components and improving model efficiency, we aim to enhance cost-effectiveness while maintaining high predictive accuracy.