As the logistics sector begins to explore AI, companies are uncovering powerful capabilities and identifying impactful use cases.
Artificial intelligence (AI) and generative AI have dominated industry conversations this year. As the logistics sector begins to explore the full potential of these technologies, companies are uncovering powerful capabilities and identifying the most impactful use cases.
Discover:
- How to select an appropriate generative AI platform for your business
- The risk of public models
- Top use cases for logistics companies
How to select a generative AI platform for your logistics company
One of the biggest challenges in exploring Generative AI is simply knowing where to begin. When I work with clients, I always start by asking: Are your employees already using tools like Microsoft Copilot, Google Gemini, or ChatGPT? If so, take time to understand how they’re using these tools and what value they’re seeing.
Regardless of your starting point, we strongly recommend choosing a private AI model over a public one like ChatGPT (more on that below). A private model helps secure your data remains secure. Check whether your ERP or core systems offer built-in generative AI capabilities — or consider starting with platforms like Copilot or Gemini to explore what’s possible.
Once you’ve selected a platform, begin with a pilot group of 10–15 employees. Let them explore the tool’s out-of-the-box features and gather feedback. It’s also a great idea to bring in an AI consultant to help identify opportunities for custom development tailored to your business needs — and to guide your team toward the kinds of outcomes we’ll highlight below.
The risk of public AI models
Public generative AI models are designed to learn from vast amounts of data, including user interactions. When individuals input sensitive or proprietary information into these models, there's a risk the data could be retained or used to further train the system. This creates potential exposure for confidential business strategies, client data, or internal communications — especially when the model is hosted on infrastructure outside the organization’s control.
Another concern is the lack of transparency and governance over how public models process and store information. Unlike enterprise-grade or private models, public platforms often don’t offer detailed audit trails, customizable data retention policies, or integration with internal compliance frameworks. This makes it difficult for organizations to verify their use of AI aligns with regulatory requirements, industry standards, or internal risk management protocols.
Finally, public models may inadvertently generate outputs that are biased, inaccurate, or misaligned with an organization’s values. Because these models are trained on open internet data, they can reflect harmful stereotypes or misinformation. Without the ability to fine-tune or constrain the model’s behavior, businesses risk reputational damage or operational errors when relying on public AI tools for decision-making or customer-facing applications.
Top generative AI use cases for logistics companies
Logistics companies are increasingly leveraging generative AI across a range of operational areas, from driver communication and dispatch automation to inventory forecasting and document processing.
These use cases often require customization, and while many platforms offer out-of-the-box capabilities, private models must be trained using enterprise-specific data to deliver meaningful results. This typically involves collaboration with a data scientist or data architect to align the model with business goals and workflows.
Once the data is integrated and the model is configured, ongoing maintenance becomes critical. Regular validation and tuning helps the model deliver reliable insights, especially in high-impact areas like route optimization, customs documentation, and warehouse planning.
1. Driver interaction and dispatch automation
Generative AI is being used to build conversational agents interacting directly with drivers. These tools allow drivers to ask questions about pickups, drop-offs, or routing in their preferred language and receive instant, accurate responses. This reduces reliance on dispatch teams and improves communication speed.
2. Inventory management and forecasting
AI agents are helping production teams manage inventory by answering queries like “How much of product X is in stock?” or “When will we run out based on usage trends?” This eliminates manual report-building and improves decision-making speed.
3. Document automation
Generative AI is streamlining the processing of invoices, bills of lading, and scale tickets. By extracting data from uploaded documents and integrating with ERP systems, businesses are reducing manual entry, curtailing errors, and accelerating workflows.
4. Route optimization
AI tools are being developed to dynamically optimize delivery routes based on traffic, weather, fuel costs, and order flows. This enables faster, more reliable deliveries and reduces operational costs.
5. Customs documentation and compliance
Generative AI can automatically generate customs paperwork and provide compliance with international regulations. This reduces delays and improves accuracy in cross-border logistics.
6. Customer support automation
AI-powered chatbots are being deployed to handle product-related queries, reducing the load on engineering and support teams. These bots provide instant answers and improve customer satisfaction.
7. Warehouse allocation and planning
Generative AI helps design better warehouse layouts and allocation plans by analyzing order patterns, space utilization, and labor availability. This improves throughput and reduces bottlenecks.
How CLA can help logistics companies with AI
At CLA, many clients approach us with questions like, “How do we begin our AI journey?” or “We’ve selected a platform—now what?”
Whether you're just starting out or looking to expand your capabilities, our digital team is here to help. We’d be happy to schedule a consultation with your organization to better understand your goals and support you in advancing your digital transformation.