The objective of CausAICA is to develop a Root Cause Analysis framework with a complementary interactive diagnostic assistant that leverages causal AI, multimodal data integration, and engineering models to uncover why failures occur in complex machinery. By linking sensor data, maintenance logs, and expert insights, the targeted approach supports more efficient failure investigations and provides explainable insights to improve product reliability and enable the right corrective actions. Additionally, it points to blind spots where critical data is missing, helping to refine monitoring and data collection strategies to improve RCA accuracy. This approach not only has the potential to reduce operational costs and minimize downtime but also empowers technicians and engineers to drive continuous improvements in machine quality and reliability.