This contributed story by Gurdip Singh, Chief Product Officer at Blue Yonder, originally appeared in Unite.AI on Aug. 25, 2023. Excerpts from the story below. To read the full article visit Unite.AI.

Just as supply chain disruptions became the frequent subject of boardroom discussions in 2020, Generative AI quickly became the hot topic of 2023. After all, OpenAI’s ChatGPT reached 100 million users in the first two months, making it the fastest-growing consumer application adoption in history.

Supply chains are, to a certain extent, well suited for the applications of generative AI, given they function on and generate massive amounts of data. The variety and volume of data and the different types of data add additional complexity to an extremely complex real-world problem: how to optimize supply chain performance. And while use cases for generative AI in supply chains are expansive – including increased automation, demand forecasting, order processing and tracking, predictive maintenance of machinery, risk management, supplier management, and more – many also apply to predictive AI and have already been adopted and deployed at scale.

This piece outlines a few use cases that are especially well suited for generative AI in supply chains and offers some cautions that supply chain leaders should consider before making an investment.