AI forecasting and planning are the processes of using artificial intelligence (AI) and data analytics to predict demand, inventory, and delivery for the supply chain. AI forecasting and planning can use various techniques, such as machine learning, deep learning, natural language processing, knowledge representation and reasoning, and expert systems, to analyze data from various sources, such as historical sales, customer behavior, market trends, weather patterns, or social media signals, and generate forecasts and plans that optimize the supply chain performance. AI forecasting and planning can offer a range of benefits for supply chain management, such as increased service levels, reduced costs, increased efficiency, greater agility, and data-driven decision making. However, AI forecasting and planning also have some challenges and risks, such as requiring large amounts of data, posing ethical or legal concerns, lacking human touch and empathy, encountering technical issues or limitations, and reflecting or amplifying human biases. Therefore, it is essential to ensure that AI forecasting and planning are designed and deployed with respect, fairness, accountability, transparency, and security in mind.