Artificial intelligence (AI)-enabled automation is increasingly reshaping operational management by combining automated process execution, machine learning, predictive analytics, and data-driven decision support. This research examines how AI-enabled automation can optimize operational activities across heterogeneous sectors while addressing challenges related to scalability, interpretability, security, process integration, and human-machine interaction. The study adopts a structured research-and-review methodology based exclusively on the provided literature, synthesizing evidence concerning business process analytics, robotic process automation (RPA), intelligent process automation, AI-enabled processes, predictive analytics, supply-chain forecasting, risk prediction, and digital manufacturing. The analysis indicates that the principal value of AI-enabled automation extends beyond task replacement: it emerges from the integration of predictive intelligence with automated execution and continuous process monitoring. However, cross-sector implementation is constrained by differences in process maturity, data quality, governance requirements, security exposure, and the degree of human judgment required. The findings support a layered operational optimization framework in which data infrastructure, predictive intelligence, automated execution, process analytics, and governance operate as interconnected components. The study contributes a conceptual basis for organizations seeking scalable automation strategies while emphasizing that optimization should be evaluated through both operational performance and decision quality rather than automation volume alone.