Square Insights The Next Stage of Logistics AI Automation:
The Question Is No Longer Whether to Adopt AI, but How Much to Delegate

Key Takeaways

In the global logistics industry, the question is no longer whether companies should adopt AI. The strategic issue has shifted to how far automation should be extended across logistics operations. Industry experts estimate that up to 90–95% of repetitive rule-based logistics tasks could potentially be automated. Recent supply chain technology trends are increasingly focused on autonomous execution AI and AI agent-based operational models. As a result, the role of logistics professionals is evolving from routine task execution to AI operations management and exception-based decision making.

1. Logistics AI Adoption: The Focus Is Now on the Scope of Execution

AI adoption is rapidly expanding across the global logistics industry. Many companies have already implemented AI technologies or are actively evaluating their deployment in operational environments.

This shift was widely discussed at Manifest 2026, one of the largest global logistics and supply chain conferences held in Las Vegas. The event brought together logistics companies, technology providers, and investors to discuss the future of supply chain technology. One key question repeatedly emphasized by industry experts was the following. The question is no longer whether to adopt AI, but which operational tasks should be delegated to AI.

Discussions across the industry are increasingly focused on autonomous execution AI capable of performing real operational tasks rather than simply supporting data analysis. This signals a shift in logistics automation from analytical support tools to operational execution systems.

2. Logistics AI Is Evolving from Support Tools to Execution Agents

In the early stages, logistics AI was primarily used for analytics and recommendation functions, such as demand forecasting, freight rate analysis, inventory optimization, and supply chain risk assessment. These capabilities mainly served as decision-support tools. However, recent trends in logistics technology are shifting toward execution-oriented AI. A key enabler of this transition is AI agent-based automation. AI agents are designed to directly perform specific operational tasks, including transport booking, shipment document verification, freight data entry, system information updates, and exception _alert_s with follow-up confirmation.

3. The Impact of Automation Appears in Workflows Rather Than Individual Tasks

Many companies have introduced RPA to automate operational tasks. However, logistics operations are rarely single-step tasks; they typically consist of multi-step workflows. A typical operational process may include the following steps.

• 1)

  Receiving an email

• 2)

  Downloading files

• 3)

  Checking data

• 4)

  Entering information into Excel

• 5)

  Uploading data into a system

• 6)

  Notifying the customer

Traditional automation approaches often focused on automating only specific steps within this workflow. In contrast, the approach increasingly emphasized by supply chain technology companies is workflow-based automation. Instead of automating individual tasks, AI can understand the entire workflow and execute connected steps across the process. This approach is particularly effective in areas such as:

• Transport booking management

• Logistics documentation processing

• Supply chain exception handling

• Customer request management

4. Core Technologies Enabling Autonomous Execution AI

For AI to perform real operational tasks, simple data processing capabilities are not sufficient. What matters most is the ability to understand the context behind operational decisions.

Most enterprise systems only record what happened. However, in real logistics operations, understanding why a decision was made is far more important. This context is often distributed across various sources such as customer emails, contract documents, internal collaboration messages, and meeting records. To address this challenge, supply chain technology companies are introducing concepts such as the following.

• Context Graph
  A data structure that connects and interprets the context of operational decisions

• Decision Memory
  A data structure that stores the reasoning and decision history behind past decisions

When these technologies are combined, AI can move beyond simple data processing and understand operational situations and make context-aware decisions.

Conclusion: The Core of Logistics AI Strategy Is the Scope of Automation

The message highlighted at Manifest 2026 is clear. AI adoption has already begun. The real strategic question is no longer whether to adopt AI but how companies redesign logistics operations around AI-driven workflows. Three technologies are emerging as key drivers of future supply chain innovation.

• Autonomous execution AI

• Workflow-based automation

• AI agent-based operational models

In the coming years, competitive advantage in logistics will likely come from companies that redesign their operations around AI-powered processes rather than simply adopting AI tools.

Frequently Asked Questions (FAQ)

How can AI be used in logistics operations?

AI in logistics can be applied across various areas, including demand forecasting, freight rate analysis, inventory management, and supply chain risk analysis. More recently, AI-powered automation is also being used to handle repetitive tasks such as transport booking, document verification, and data entry.

What is the difference between logistics automation and AI automation?

Traditional automation, such as RPA, typically focuses on automating specific task steps. In contrast, AI-driven automation can understand and connect multiple stages of a workflow, enabling it to execute more complex processes across the entire operation.

What are the key factors for logistics companies preparing to adopt AI?

To implement AI-driven logistics operations, it is important to establish integrated data systems, enhance supply chain visibility, and build digital platforms that support data-driven decision-making.

Samsung SDS Global Logistics Services

Building an AI-powered logistics operating environment requires a combination of global logistics networks, operational expertise, and digital platform capabilities. Samsung SDS enhances global supply chain visibility through its digital logistics platform Cello Square, which provides real-time monitoring across the entire logistics process, AI and machine learning-based risk prediction, and accurate estimated time of arrival information. Samsung SDS also supports companies’ digital supply chain transformation through integrated IT-based logistics services covering the entire process from factory, transportation, customs clearance, warehousing, to distribution.

If you would like to learn more about AI-powered global logistics operations and supply chain visibility, please explore Samsung SDS logistics services.

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