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The development of the logistics industry depends on efficient information processing and data management. The transfer of AI models means a change in the way data is processed. In the cloud, large-scale data can be processed centrally, but there are problems with latency and network stability. Processing at the edge can respond to local needs more quickly, improving real-time performance and efficiency.

Take air express delivery as an example. Time is life. Fast and accurate information transmission and processing are essential to ensure that goods are delivered on time. The application of AI models on the edge can achieve more accurate real-time decision-making in cargo loading and unloading, transportation route planning and other links. For example, through sensors and edge computing devices, the status and environmental conditions of goods can be monitored in real time, and transportation strategies can be adjusted in time to reduce delays and losses.

In addition, financial aspects are also affected. Processing data in the cloud is relatively expensive, and decentralized processing at the edge may reduce some infrastructure costs. But at the same time, careful financial planning is also required for new technology investments.

Logistics companies should actively embrace this change, strengthen technology research and development and talent training, and optimize business processes to adapt to new technological trends. Only in this way can they remain invincible in the fierce market competition.

In short, this view of Intel executives has brought new thinking and opportunities to the logistics industry. Logistics companies should seize the opportunity to achieve transformation and upgrading.