AI in Logistics: Real-World Examples from Around the Globe

Artificial Intelligence (AI) is transforming the logistics and transportation industry, making supply chains more efficient, cost-effective, and resilient. From route optimization and warehouse automation to predictive analytics and autonomous vehicles, AI is driving innovation and solving longstanding logistical challenges. In this blog post, we’ll explore some of the most exciting real-world examples of AI in logistics from around the world, highlighting how AI enhances each solution.

North America

Vorto

Founded in 2014 and located in Denver, Colorado, Vorto’s autonomous supply chain platform aims to reduce carbon emissions from supply chain transportation and enhance the quality of life for around 3.5 million truck drivers by optimizing their time. Used by Fortune 500 companies throughout North America, Vorto’s technology automates the processes of data preparation, analysis, and decision-making.

Vorto’s AI algorithms analyze vast amounts of data to optimize logistics operations. By automating data preparation and analysis, AI reduces the time and resources required for manual data processing. This allows logistics companies to make faster and more informed decisions, improving overall efficiency and reducing operational costs.

Coupa

Founded in 2006 and based in San Mateo, California, Coupa enables supply chain companies to make data-driven decisions with its suite of AI and digital tools. With the Supply Chain Modeler, businesses can compile logistics data and predict operational results by running various scenarios. AI features also factor in variables such as tariffs and environmental events, so companies can assess all possible risks and adjust their supply chains accordingly.

Coupa’s AI-driven tools analyze supply chain data to identify trends, predict outcomes, and optimize processes. By running various scenarios, companies can evaluate the potential impact of different decisions and choose the most effective course of action. This helps reduce risks, improve efficiency, and enhance overall supply chain resilience.

UPS

UPS’ AI-driven ORION system has helped save 10 million gallons of fuel annually by optimizing delivery routes, reducing operational costs significantly. With AI, logistics companies can predict delays before they happen, offering customers accurate delivery estimates and improving satisfaction.

The ORION system uses AI to analyze traffic patterns, weather conditions, and historical delivery data to optimize delivery routes in real-time. This reduces fuel consumption, lowers emissions, and improves delivery efficiency. By predicting potential delays, AI also enables companies to provide more accurate delivery estimates, enhancing customer satisfaction.

Europe

DHL

DHL has been actively exploring AI applications in logistics. Their AI trend report highlights various use cases and benefits of AI in the logistics industry. DHL uses AI for predictive analytics to optimize inventory management, route optimization to improve delivery efficiency, and warehouse automation to enhance productivity.

DHL’s AI-driven predictive analytics analyze historical data and market trends to forecast demand accurately. This helps optimize inventory levels, reducing overstocking and stockouts. AI also optimizes delivery routes in real-time, improving delivery efficiency and reducing operational costs. In warehouses, AI-powered automation streamlines processes, increasing productivity and reducing errors.

Asia

GreyOrange

GreyOrange, a Singapore-based company, has developed AI-powered logistics robots and warehouse automation solutions. Their robots are designed to handle repetitive tasks in warehouses, improving productivity and reducing errors. AI algorithms enable the robots to learn and adapt to different tasks and environments.

GreyOrange’s robots use AI to navigate warehouses, pick and pack orders, and sort inventory. AI algorithms enable the robots to learn from their experiences, improving their efficiency and accuracy over time. This reduces the need for manual labor, lowers operational costs, and enhances overall warehouse productivity.

Infor

Founded in 2002 and headquartered in New York, New York, Infor’s intelligent supply chain applications employ advanced algorithms, optimization engines, and machine learning to unify the digital and physical worlds so companies can access rich insights and make more informed business decisions. Solutions include supply chain planning, procure-to-pay automation, supply chain finance, supply management, supply chain visibility, transportation management, and warehouse management.

Infor’s AI-driven applications analyze supply chain data to provide actionable insights and optimize processes. Machine learning algorithms identify patterns and trends, enabling companies to make data-driven decisions. This improves supply chain planning, reduces costs, and enhances overall operational efficiency.

Oceania

Epicor

Epicor, headquartered in Austin, Texas, employs Microsoft Azure, a cloud-based AI solutions platform, to make its business solutions for manufacturers and distributors — including supply chain and logistics — even smarter. The company has also explored incorporating Microsoft’s speech-to-text and advanced search capabilities to improve the way customers interact with its applications.

Epicor’s AI-driven solutions leverage Microsoft Azure’s advanced algorithms and machine learning capabilities to optimize supply chain operations. This includes improving inventory management, optimizing transportation routes, and enhancing customer interactions through speech-to-text and advanced search features. AI helps companies make more informed decisions, reduce costs, and improve overall efficiency.

Global

Amazon

Amazon’s fulfillment centers use 520,000+ AI-powered robots, drastically reducing order processing times. AI-driven automation helps in picking and packing orders efficiently, sorting and storing inventory in real-time, and enhancing inventory tracking and management.

Amazon’s AI-powered robots use machine learning algorithms to optimize their movements and tasks. This enables them to pick and pack orders more efficiently, reducing processing times and improving overall warehouse productivity. AI also enhances inventory tracking and management, ensuring accurate stock levels and reducing waste.

Maersk

Maersk has integrated AI-driven cybersecurity to safeguard its global supply chain from cyberattacks and fraud. AI-powered tools identify suspicious delivery patterns, prevent cargo theft and lost shipments, and use blockchain and AI for secure transactions.

Maersk’s AI-driven cybersecurity tools analyze data to identify suspicious patterns and potential threats. This helps prevent cargo theft, lost shipments, and cyberattacks, ensuring the security and integrity of the supply chain. AI also enhances the accuracy and security of transactions through blockchain technology.

Conclusion

These examples showcase the diverse applications of AI in logistics across different regions, highlighting its potential to transform the industry by improving efficiency, reducing costs, and enhancing customer satisfaction. As AI continues to evolve, we can expect even more innovative solutions that will further optimize logistics operations worldwide.

References

• AI in Logistics: Top 23 Use Cases and Applications in 2025
• AI in Logistics: Use Cases, Benefits, Challenges
• 16 Examples of AI in Supply Chain and Logistics (2025)