Artificial Intelligence in DCs: The Real-Life Example of Order Preparation
Guest Blog from MHI Member NūMove Robotics & Vision
Artificial intelligence (AI) is rapidly transforming the way distribution centers operate. Once considered experimental, AI has entered modern warehouse environments, driven by measurable operational gains and increasing confidence across the industry. According to the “2025 MHI Annual Industry Report,” 28% of respondents already use AI in their warehouse operations, and adoption is expected to grow to 82% by 2030. More than half of respondents (52%) believe AI has the potential to disrupt the industry or generate a competitive advantage.
As AI becomes more accessible and powerful, its impact is most visible in the practical improvements it brings to everyday tasks in distribution centers.
AI in Today’s Distribution Centers
AI now enhances several key warehouse functions. It supports pick‑and‑pack processes by analyzing workflows and improving the sequencing of tasks. It strengthens replenishment activities by anticipating stock movement and reducing the likelihood of interruptions. Software-assisted decision-making, combined with AI-driven optimization, also makes vehicle routing more dynamic, adjusting to real-time conditions on the floor.
Machine vision systems, a major subset of AI, play an increasingly crucial role. Their ability to identify, classify, and locate products means they can adapt to ever-changing packaging, product formats, and SKU variations. This flexibility is essential in industries where product life cycles and packaging updates occur frequently.
The trend is clear: AI is less about futuristic automation and more about making existing operations more efficient, adaptable, and resilient.
A Real-Life Example: AI in Order Preparation
Order pallet preparation is one of the most compelling cases demonstrating AI’s value. Many distribution centers and fulfillment centers prepare mixed‑SKU orders for retail partners, stores, or online consumers. The task requires high levels of accuracy, especially when operators or robots must pick items from donor pallets, totes, or cases.
Traditionally, every new SKU, or even a simple change in packaging graphics, required manual updates to image processing programs. This approach created operational bottlenecks and increased reliance on specialized programmers.
AI-driven machine vision dramatically changes this landscape. Instead of relying on fully predefined rules, AI learns to recognize products regardless of packaging changes. Whether a label is redesigned, a promotional graphic is added, or a seasonal color variant appears, the system continues operating without additional programming.
There are many use cases based on AI in distributing, including order preparation for fast‑moving beverages like beer and soda. For example, consider a dual‑robot system that first depalletizes products before performing mixed palletizing. In this workflow, the depalletizing robot picks individual units from a donor pallet and places them into a temporary buffer. From there, the palletizing robot retrieves the products and builds a stable mixed pallet. Using AI-powered image processing, the system identifies and locates the next product to pick on the depalletizing side, even when new packaging is introduced. No additional input from programmers is required to adapt to new SKUs.
This flexibility is equally valuable in e-commerce environments. When preparing a multi-SKU shipping case for end consumers, AI enables automated systems to recognize items instantly, even when new colors or graphics appear, without halting operations for software adjustments.
Why AI and Software Form a Strong Combination?
AI’s capabilities become even more powerful when integrated into broader warehouse software ecosystems. Software systems provide structure and orchestration, managing replenishment, sequencing orders, or optimizing pick paths, while AI adds adaptability to real-world variability.
This combination ensures operational continuity even when product lines evolve quickly. Workflows remain stable and efficient, allowing distribution centers to react to SKU variety without the need for constant intervention.
Key Advantages of AI-Driven Order Preparation
Versatility
AI-enabled systems can accommodate new packaging graphics without requiring programming updates. This adaptability reduces downtime and ensures fluid operations even in environments with frequent product refreshes.
Ease of Use
Warehouse teams no longer need to contact vendors or programmers whenever a product design changes. AI handles visual variability through training, allowing the system to continue operating smoothly.
Future Readiness
Because new SKUs and packaging variations appear constantly, distribution centers benefit from a system that can evolve with market changes. AI provides this capability, making order preparation more resilient over time.
Conclusion
AI has moved beyond the realm of conceptual innovation and is now a reliable, practical tool enhancing operations within distribution centers. Its ability to handle packaging variability, support efficient workflows, and reduce programming dependencies demonstrates its real-world value.
As SKU diversity continues to rise and customer expectations tighten, AI offers an adaptable, future-ready foundation for order preparation and other warehouse functions. It strengthens existing automation, allowing organizations to maintain high throughput, respond quickly to product changes, and operate with greater confidence across their distribution networks.
