This paper presents a Jordanian third-party logistics (3PL) warehouse and distribution service provider, the Jordanian Company (TJC), located in Jordan, a developing country. This study aims to assess the order-picking activity in the existing company’s warehouses, and when there is a weakness, it will adopt AI (artificial intelligence) to enhance its performance. The methodology adopted an analytical, quantitative approach used to time stock-keeping unit (SKU) picking and loading activities to find if there is any time waste that occurs when picking and moving while collecting the SKUs of a specific customer order. Data was collected from a single firm (i.e., TJC) as a case study from a developing country. The findings of this study found that the traveling time between locations, aisles, and warehouses is 34% of the total time needed to pick and load the wave. The actual time for picking SKUs is only 41.7% of the total time, load inspection and truck loading 16.4%, while traveling, inspecting, and loading time is 58.3% of the total time. Furthermore, the order-pickers were assigned specific zones. The picking activity was more efficient because the traveling time was only between pallet locations within aisles. We concluded that the TJC picking procedure needs to be re-evaluated, and AI applications need to be used for enhancement. The traveling time between pallet positions and aisles must be reviewed to reduce travel time; thus, using AI to assign routes is crucial. The layout of SKU locations needs to be optimized to reduce traveling time, especially when picking SKUs of the same family.