In the dynamic field of information systems, the integration of Causal AI with traditional machine learning (ML) techniques presents a transformative approach to data analysis and decision-making. This chapter explores the potential of leveraging Causal AI to enhance ML applications across various domains, including business analytics, e-commerce, healthcare, security, and financial services. Unlike conventional ML models that primarily focus on identifying patterns and correlations, Causal AI delves deeper to uncover the cause-and-effect relationships within data. This capability enables more accurate predictions and informed decisions, addressing the limitations of ML in explaining why certain outcomes occur. This chapter provides a comprehensive overview of the principles of Causal AI, compares its applications with those of traditional ML in the aforementioned domains and process for applying Causal AI. By leveraging Causal AI, different information systems have a potential to achieve greater interpretability, reliability, and actionable insights, ultimately leading to more effective solutions.