AI has advanced since the 60s with gains in processing speed, memory, and quantitative models. In retail, the quality of data has improved not only with NLP, but also with new data sources such as demographics and purchase history.
AI already drives in-store digital media. It also serves operations, merchandising, and marketing. Distributed intelligence powers inventory management. Analytic processing enables site selection, marketing mix modeling, and demand forecasting.
But there are limits. AI can process and calculate but not interpret or decide at a strategic level. It can follow pre-programmed rules when tracking storms or directing air traffic but needs human control when rules change.
And the rules often do change. What happens when goals change from minimizing working capital to maximizing product availability? Or increasing sales per SKU instead of growing overall brand sales? Or targeting mainstream instead of high end buyers?
Disregarding the Jeopardy rules, let’s phrase the answer as an answer. Because rules change, humans can push tasks to machines, but they can’t relinquish decisions to Watson.