AI “Agents” Are Transforming Global Logistics Operations

The global logistics industry is entering a new phase of transformation as artificial intelligence is no longer just a supporting tool but is becoming part of the operational workforce. Major corporations such as FedEx are leading the development of an “AI agent workforce” model—where AI systems function as “digital employees” directly participating in logistics processes.

This trend marks a significant technological advancement, as AI is no longer limited to data processing but is increasingly capable of making decisions across various operational activities.

1. AI Agents – From Support Tools to “Digital Workforce”

Unlike traditional AI applications, AI agent models enable systems to autonomously perform specific tasks that previously required human intervention.

At FedEx, AI is being deployed in various operations such as:

• Transportation network planning

• Operational data analysis

• Customs declaration support

• Business process optimization

The company aims to integrate AI into more than 50% of its core operational processes by 2028, highlighting the growing depth of technology adoption in logistics.

According to FedEx representatives, AI will evolve into a “digital coworker” that works alongside humans rather than fully replacing them.

2. Agentic AI – The Next Step for Logistics

One of the most prominent trends today is Agentic AI—AI systems capable of making decisions based on data and contextual understanding.

Beyond executing individual tasks, AI agents can be structured hierarchically, including:

• Manager agents

• Audit agents

• Worker agents

This approach ensures transparency, controllability, and traceability throughout the entire operational process.

In logistics, Agentic AI can integrate macroeconomic and micro-level data to optimize transport networks, forecast demand, and enhance supply chain performance.

3. Challenges in Implementing AI in Logistics

Despite its significant potential, deploying AI agents in logistics also comes with challenges.

One of the biggest issues is fragmented data. Logistics companies often operate across multiple systems, making data standardization and AI integration more complex.

Additionally, factors such as:

• High technology investment costs

• Unclear return on investment

• Risks related to security and governance

are key barriers that may prevent AI projects from achieving expected outcomes.

According to industry forecasts, a considerable proportion of AI agent projects may be discontinued if they fail to demonstrate real-world effectiveness.

4. Data – The Core Foundation of AI in Logistics

To implement AI effectively, businesses must build a strong data foundation. This is the key factor determining the performance and accuracy of AI systems.

FedEx is currently developing an enterprise data platform to support more than 200 AI use cases across its supply chain. Consolidating data from multiple sources is considered a critical step to ensure accurate decision-making.

In practice, data quality directly impacts the quality of AI decisions. Inaccurate or inconsistent input data can lead to incorrect outcomes.

5. Logistics Enters the Era of Intelligent Operations

The adoption of AI agents is shaping a new logistics model—where operations are automated and optimized in real time.

Companies implementing AI are experiencing notable improvements such as:

• Faster order processing

• Higher operational accuracy

• Reduced labor costs

• Improved forecasting and decision-making capabilities

However, this transformation also requires workforce reskilling, as employees shift toward supervising and optimizing systems rather than performing manual tasks.

Conclusion

According to THT Cargo Logistics, the adoption of AI in logistics—especially AI agent models—will become a key factor in enhancing business competitiveness in the near future.

As supply chains grow increasingly complex, leveraging technology to improve transparency, optimize operations, and enhance decision-making is inevitable.

However, to ensure successful implementation, businesses must start with data standardization, build appropriate governance systems, and gradually integrate technology into core operational processes.

Combining practical operational expertise with advanced technology will enable logistics companies to better adapt to the rapid changes of the global market.