The AI Lifecycle encompasses the end-to-end process of discovering, assessing, developing, deploying, and managing AI systems. It includes ideation, feasibility and cost-benefit analysis, defining project goals, data collection and processing, model development and training, validation and testing for accuracy and fairness, deployment into production, and ongoing monitoring and maintenance. This lifecycle ensures AI solutions remain effective, ethical, and efficient throughout their operational life, requiring continuous evaluation and adaptation to new data and changing environments.