You put that in your cart. How about this cool thing to go with it?` is a common phrase used by online retailers to suggest complementary products to customers who have added items to their cart. This phrase is often accompanied by a list of frequently bought together items, which are generated by an AI-aided recommendations engine. These engines use machine learning algorithms to analyze user data and provide personalized suggestions for content based on amassed user data such as purchase history, clicks, conversions, and demographics. Modern recommendation systems have become sophisticated with the rise of artificial intelligence, allowing them to track the sequence of user behavior and provide considerably richer insight into a specific user's goals and interests. AI-based solutions are attuned to the ways companies target customers interact with products and services during a given session, providing relevant recommendations tailored to what they're seeking. The use of machine-learning algorithms that use statistical models enables AI-aided recommendation systems to make predictions based on data collected about users' activities on a website or in an app. These systems can be categorized into different types such as collaborative filtering and content-based filtering, with hybrid systems utilizing both approaches providing the best suggestions. The integration of AI recommendations has been shown to improve shopping-cart AOV, drive conversions by suggesting cross-sells, and enhance the end-to-end customer experience on a site. With non-AI recommendations, people were expected to know what they wanted and be able to accurately describe it, whereas with AI-based recommendations, people may never have sought out items or made item discoveries on their own. Overall, AI-powered recommendation systems are one of the most popular applications of data science, driving conversions by suggesting relevant content based on amassed user data.