The role of AI in DevOps is transforming the way software development happens, combining AI technologies like machine learning, natural language processing, and robotic process automation to optimize software delivery processes and achieve higher quality and reliability. AI helps organizations automate repetitive tasks, provide insights that facilitate decision-making by analyzing large datasets, and drive efficiency in DevOps practices. The benefits of using AI in DevOps include optimized application performance, faster time-to-market, better decision making, improved quality assurance, intelligent monitoring, cost and risk reduction, machine learning, natural language processing, deep learning, and robotic process automation. However, challenges such as communication barriers, resistance to change, skill gaps, data quality concerns, complex integrations, and regulatory compliance must be addressed. To fully harness the potential of AI in DevOps, it is essential to adopt best practices, including pilot testing with small and manageable problems, aligning with business goals, observing algorithm behavior, training teams, maintaining transparency, ensuring high-quality data, using predictive analytics, and monitoring with AI. The future of AI in DevOps is toward more sophisticated AI-powered platforms and advanced analytics, promising further workflow improvements and enhanced system resilience.