Imagine future logistics centers where towering stacks of goods and bustling workers are replaced by AI-powered systems that precisely coordinate every truck, cargo ship, and even predict market fluctuations. This isn't science fiction—it's the emerging reality as artificial intelligence reshapes global supply chains.
In a recent exclusive interview, we spoke with Mr. Cai Xiaoming, Vice President of Yita Technology and a 15-year veteran in international logistics digitalization, who provided expert insights into AI's transformative potential, implementation challenges, and future development paths for the industry.
Has the AI Era Truly Arrived?
"The AI era has undoubtedly arrived," stated Mr. Cai, drawing parallels between AI's impact and historical industrial revolutions. He noted how each technological leap—from steam engines to computers—fundamentally transformed production methods and lifestyles.
Highlighting current developments, Mr. Cai observed that industries like finance, manufacturing, and international logistics are actively restructuring organizations, optimizing processes, and redefining competition through AI adoption. He designated 2025 as "Year One of AI," predicting profound impacts on corporate development trajectories over the next decade.
Key AI Applications in International Logistics
Mr. Cai outlined several transformative applications:
- Intelligent Route Optimization: AI analyzes real-time traffic, weather, and other variables to enhance transportation efficiency and reduce costs, particularly in multimodal transport coordination.
- Automated Customs Clearance: AI systems automatically identify HS codes and input declaration elements, significantly improving clearance speed and accuracy for time-sensitive shipments like e-commerce goods.
- Freight Market Forecasting: Machine learning models process historical data to predict shipping price fluctuations, enabling better risk management and strategic decision-making.
- Autonomous Customer Service: AI handles inquiries, generates quotes, processes orders, and provides shipment tracking updates, improving service efficiency while reducing labor costs.
- Self-Driving Transport: Applications like Ningbo-Zhoushan Port's unmanned container trucks demonstrate how autonomous vehicles enhance port operations while reducing safety risks.
Implementing AI: Practical Considerations
Mr. Cai emphasized gradual, foundation-first implementation strategies:
- Real-Time Shipment Monitoring: AI-powered tracking systems liberate staff from manual data collection while providing customers with enhanced visibility.
- Corporate Knowledge Bases: Centralized repositories of trade terms, customs regulations, and operational procedures accelerate new employee training through AI-assisted learning.
Challenges in AI Adoption
The executive identified several critical barriers:
- Talent Shortage: Scarcity of professionals with both AI expertise and logistics knowledge.
- Investment Requirements: Significant capital needed for hardware, software, and workforce development.
- Limited Case Studies: Few proven success stories to demonstrate ROI and guide implementation.
- Leadership Commitment: Executive understanding and willingness to drive organizational change.
Balancing AI Predictions With Market Realities
Mr. Cai cautioned that while AI-generated forecasts provide valuable insights based on historical data and trend analysis, market dynamics—like sudden carrier rate changes—require human judgment to interpret predictions appropriately.
The Human Factor in AI-Driven Logistics
Addressing workforce concerns, Mr. Cai clarified: "The real risk isn't to specific roles, but to individuals resistant to learning and critical thinking." He compared logistics professionals using AI to factory technicians maintaining automated lines—the technology enhances rather than replaces human value when properly leveraged.
The Personal Impact of AI
Reflecting on his experience, Mr. Cai noted AI has intensified professional demands: "To become efficiently 'lazy' with AI, you first must become smart—continuously expanding your technical understanding to maximize these tools."
Ethical and Operational Frameworks
When characterizing human-AI collaboration, Mr. Cai prioritized: Rules (establishing operational parameters), Coordination (structured teamwork), and Value (derived benefits). This framework ensures AI applications remain aligned with business objectives while generating measurable returns.
Regarding hypothetical scenarios about AI making unconventional decisions (like substituting air freight with animal transport), Mr. Cai dismissed such concerns, explaining that mature AI systems incorporate comprehensive business logic and self-correcting mechanisms to prevent irrational outputs.