AI predicts demand fluctuations by analyzing historical data, identifying patterns, and using machine learning algorithms to forecast future demand2. It considers various factors such as seasonality, market trends, customer behavior, and external events to make accurate predictions1. This allows businesses to adjust their strategies and optimize inventory management, pricing, and resource allocation.
Companies implementing AI dynamic pricing face challenges such as customer perception of unfairness, regulatory compliance, and data privacy1. They must ensure that their pricing models are transparent, fair, and adhere to data protection regulations while also managing the risk of price wars and maintaining long-term brand perception.
AI models predict demand fluctuations by analyzing historical data, identifying patterns and trends, and considering external factors such as market conditions, competitor pricing, and consumer behavior. Machine learning algorithms enable these models to adapt and improve their predictions based on real-time data, ensuring accurate and reliable forecasts.