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.

Artificial Intelligence, Process Automation, Robotic Process Automation

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