AI and Logistics: It’s a Match

AI is no longer science fiction. Beyond well-known consumer use cases like digital assistants, AI powers facial recognition to unlock phones and computers, or language-learning apps that leverage Natural Language Processing (NLP).

In the business world, AI drives productivity and efficiency. Chatbots serve as virtual agents, automating customer support. Personalized product recommendations in retail or predictive maintenance in manufacturing are other proven applications.

AI and Logistics: Meeting Today’s Challenges

AI-powered cognitive systems can learn, recognize patterns, and derive recommendations—or even trigger processes autonomously. In logistics, that means AI can support warehouse employees in decision-making or alert them to likely events before they happen.

This is where logistics can already benefit today: never have the requirements been higher. Few industries face as much competitive and time pressure. Customer expectations keep rising. Delays or errors are not tolerated.

At the same time, data quality has become critical. Poor master data, for example, can lead to incorrect shipments and dissatisfied customers. Managing the supply chain and continuously optimizing processes requires deep expertise—yet that knowledge is often concentrated in just a few individuals. When those people are unavailable, the know-how gap becomes a real bottleneck.

Digitalizing the Supply Chain: The Foundation for AI

The key enabler of AI in logistics is digitalization. Many companies are already connecting systems and stakeholders across the supply chain to create transparency and meet rising demands. Once all relevant supply chain data is accessible, AI enables companies to use it intelligently—driving efficiency and lifting logistics to a new level.

Inside the warehouse, AI can act as a digital assistant, supporting employees across multiple processes.

Visual Recognition

AI-powered image recognition reduces the “human factor” in processes—and with it, the risk of error. This massively improves process reliability. Visual recognition delivers particular value in inbound logistics, quality control, and outbound processes.

Here’s how it works: An item is photographed with a mobile data terminal or smart device app. The AI learns the article, including all relevant data, and can recognize it automatically going forward.

At goods receipt, visual recognition simplifies master data capture and enables unique identification later on. It validates the article and quantity picked for an order and checks quality and condition. If something doesn’t meet requirements, action can be taken immediately. At outbound, visual recognition supports final checks and loading.

Smart Assistant

A smart assistant is essentially a digital colleague that makes expert knowledge available anytime. By learning from user feedback, it gets better over time.

One example: a chatbot. Warehouse staff can consult it at any point in a process when they need assistance or additional information. If an item is missing from a storage location or excess goods have been picked, the employee can ask the AI for guidance—and receive a data-driven recommendation within seconds.

Predictive Analytics

Knowing today what will happen tomorrow: Only a few logistics companies currently plan resources efficiently and on demand. Resource planning often relies on gut feeling and past experience—hardly precise.

This is where AI delivers significant value. Using historical data, process models, and external factors, predictive analytics creates forecasts that improve workforce and resource planning. Thanks to machine learning, these forecasts become increasingly accurate over time.

This makes it possible to allocate personnel more effectively and to plan capacity with higher precision. Predictive analytics also enables predictive maintenance of equipment and machinery, helping avoid downtime and reduce costs.

Smart Documents

Smart Documents take document handling to the next level by intelligently digitalizing paperwork. One example: delivery note management in inbound logistics.

Delivery notes often vary significantly depending on the supplier—the layout and placement of key information is rarely standardized. With AI, an app can automatically identify, extract, and transfer relevant data into a standardized template.

That eliminates manual searching or the need for specialized software. The result: leaner, faster, and more accurate document processing.