The Retail AI Adoption Problem

There are many similarities between adoption challenges in price optimization and those facing AI.Bigstockphoto In order to understand the coming AI adoption problem in retail, you first need a little history. In the early 2000's a new technology hit the retail market, called price optimization. The first use-case to be adopted focused on markdown optimization, or pricing inventory near the end of its life to clear out as fast as possible at the greatest margin possible. Coming off of the Internet bubble bursting, retailers had big problems with too much inventory, and markdown optimization was their savior – helping them clear out overstocked items without taking an entire bath on margin in the process. Markdown optimization produced some counter-intuitive results, and was initially resisted, but as its value became proven, more and more retailers with short lifecycle products found themselves in a position of a market expectation that they would have markdown optimization to protect themselves from bad product purchase decisions. The counter-intuitive part came in two ways.