AI-Powered Automation: Architectures, Patterns, and Governance for Autonomous Systems explores the new frontiers that are created when artificial intelligence converges with quantum operations, sustainability, personalizing, and regulation. Artificial intelligence-powered automation brings intelligence, prediction, and decision-making into daily and difficult operations. AI-Powered Automation: Architectures, Patterns, and Governance for Autonomous Systems begins by explaining how decision-making systems can be enhanced through the use of neuro-symbolic AI, before discussing quantum workflow orchestration and autonomous governance. It goes on to consider AI-enabled sustainability automation, carbon-aware workload placement, and predictive modelling for resource-efficient systems, then examines when and how to integrate human decision-making into automated systems and when zero-touch AI automation is suitable. The book investigates the use of large language models for automation generation, covering LLM-based DevOps, RPA, and smart assistants, then explores how AI-automated digital twins evolve through feedback and optimization. It then addresses automation of legal, editorial, and research processes through AI agents and copilots - including the implications for IP, bias, and reliability - hyperpersonalized automation in user experience design and real-time automation adaptation based on individual behavioural models, before finishing with a look at autonomous enterprises: a future vision of self-operating organizations. Offering case studies from across a variety of industries, AI-Powered Automation: Architectures, Patterns, and Governance for Autonomous Systems resolves practical problems in compliance, sustainability, and orchestration to provide graduate students, researchers and professionals with a holistic understanding of how automation can adopt AI.