23 Daily — According to 36kr.com, the logistics industry is witnessing a shift in artificial intelligence (AI) application focus—from emphasizing sophisticated large models to integrating AI into practical, on-site operations. G7 EasyFlow implements wearable AI devices and vehicle-mounted hardware to visualize logistics site data, enhancing operational transparency and efficiency while reducing accidents and disputes, redefining the value of drivers and logistics work.
Logistics AI Shift: From Model Complexity to On-Site Effectiveness
At the 2026 Digital Logistics Conference attended by over 3,000 industry professionals, discussions centered on AI's practical application in logistics. Cai Jin, President of China Federation of Logistics & Purchasing, emphasized logistics as not only an industry but a socio-economic production organization. Companies should use advanced organizational methods to help upstream and downstream partners cut costs and enhance efficiency. Despite previous advancements in digitalization and process online integration, key on-site operations like vehicle loading/unloading and warehouse handovers remain opaque 'black boxes' without systematic oversight, causing difficulties in identifying liability in damage or disputes and hampering swift feedback for process improvements.
G7 EasyFlow’s Solution: On-Site Sensing and Data Structuring
G7 EasyFlow’s founder and CEO, Zhai Xuehun, revealed the platform’s monthly token consumption surged from 6.7 billion in 2025 to 770 billion, reflecting rapid AI adoption, yet only about 4% of active clients have fully integrated AI into operations. Zhai highlighted that practical AI involves two key aspects: acquiring real-time site facts and offering on-site support. He noted a significant gap between 'usable AI' and 'AI operating directly in logistics environments.' Leveraging Purple Box vehicle hardware and Paipai Bean wearable AI devices, G7 EasyFlow captures previously invisible actions like off-vehicle handovers, temperature measurements, and cargo inspections, converting unstructured site behaviors into structured data enabling verification and assessment of standard operating procedures.
Real-World Impact and Logistics Scene Innovation
In instant retail and chain restaurant supply chains, brief handover periods between warehouses and drivers have been management blind spots traditionally monitored via limited human inspection. Using G7 EasyFlow’s AI devices, drivers automatically record handover events; AI recognizes cargo appearance and handover actions, providing complete data traces to resolve responsibility disputes. Additionally, AI automates train arrivals and forklift inventory management in large manufacturing yards, reducing manual workload and easing congestion. For fleets of 600 new energy heavy trucks, AI provides real-time safety scores paired with incentive mechanisms, lowering near-miss incidents from five or six weekly to two. Urban last-mile delivery uses wearable AI to document cold-chain cargo handling, ensuring service quality with traceable evidence. These applications demonstrate significant breakthroughs in site operation monitoring and management enhancement through AI.
Editor's take: Focusing on On-Site Visibility and Data-Driven Logistics AI Deployment
The logistics AI evolution away from model demonstrations toward on-site operational integration centers on the adoption of wearable and vehicle-mounted sensing devices to achieve comprehensive data visibility and structuring of site behaviors. This approach not only improves management transparency and lowers accidents and disputes but also transforms standard operating procedures from abstract requirements into verifiable, data-backed practices. For entrepreneurs, this signals a shift where logistics technology markets demand integrated hardware-software solutions with deep on-site contextualization rather than isolated algorithmic improvements. Viable validation approaches include piloting deployments in real operational contexts to assess improvements in operational efficiency, incident rates, and management costs, thus confirming the tangible value of AI technologies and services.