Author: Jennifer Hall | 4 min read
March 31, 2025
In today’s supply chain landscape, AI is no longer just a trend—it’s a necessity. As industries strive to boost productivity, streamline operations, and improve efficiency, AI is emerging as the key to unlocking new levels of success. Traditional systems often struggle to keep up with the fast-paced, ever-evolving nature of modern supply chains, but AI offers the flexibility and adaptability needed to thrive in this dynamic environment.
For warehouse operations, AI is reshaping Warehouse Management Systems (WMS) by providing smarter, data-driven solutions that go beyond outdated methods. If you want your warehouse to keep up with the demands of the future, AI is the game-changer your operations need.
One of the most exciting areas of AI development is in natural language processing (NLP), which allows systems to understand and respond to conversational commands. In a warehouse setting, this could mean employees interacting with voice systems using natural language rather than having to learn specific commands or syntax. Voice systems have historically required users to train them on their voices and only execute specific commands, but with NLP, employees can speak as they normally would, maintaining their regular vernacular.
For example, in the case of a short pick, the picker can conversationally respond to a prompt to “pick five” as “but there’s only four” rather than a series of robotic response-pause-response interactions.
This kind of intuitive interaction makes it easier for workers to engage with voice systems without needing to undergo extensive training or becoming frustrated by the rigid structure for voice commands. And thus, they extend the areas where their voice can be used.
AI is also making a significant impact in how businesses gather and act on employee and customer feedback. In a warehouse setting, traditional feedback methods like suggestion boxes or team meetings can be slow and inefficient, often relying on the “squeaky wheel” getting attention. By the time issues are raised in a meeting or breakroom, they may be forgotten or less relevant. AI can change that by capturing real-time feedback, analyzing it, and providing actionable insights to managers.
For example, AI-powered systems could automatically collect feedback from employees while they perform their tasks, identifying patterns and highlighting areas for improvement. This could include feedback on issues like a low beam causing an unergonomic pick or a tricky product that needs to be re-slotted. Instead of just recording feedback at a later time, AI captures it in real-time and synthesizes it for managers to act on immediately. This streamlines the feedback process, ensuring that managers have up-to-date information to make quick, informed decisions.
Now what if we reimagined the entire architecture of a WMS? Traditional WMS solutions are typically built around “if-then” statements, a standard coding structure. These systems, while effective, are limited by predefined rules that dictate operations. For example, in a typical WMS, if stock levels drop below a certain threshold, the system can trigger a replenishment order. However, this approach can be inflexible and unable to adapt to shifting conditions. Fixed triggers can lead to disruptions in processes, even in the most optimized WMS setups.
Enter AI, which is revolutionizing software development by moving beyond these simplistic rules. Instead of relying on rigid “if-then” logic, what if AI could allow systems to make real-time, intelligent decisions based on a range of factors? In the case of a WMS, rather than just triggering replenishment when stock levels fall, an AI-powered system could dynamically adjust the replenishment process. Taking into account that shift’s demand, traffic for inbound deliveries and the time to execute a replenishment or pick by those specific associates to fine-tune operations. This all informs the ideal timing of replenishments relative to the arrival of the next pick, prioritizing tasks according to the specific needs of that day or night, all while improving workflow efficiency and optimizing interleaving.
Warehouse management systems have been developed over decades, and it’s unlikely that major players will completely overhaul their proven architectures overnight. While we may eventually see new WMS solutions built from the ground up with AI at their core, these systems will take time to gain traction and establish reliability.
What about a “super manager”? This AI-powered user would act as the master controller for the WMS, overseeing all operations and making real-time decisions about inventory management, picking, and replenishment. The beauty of this approach is that it would work within the existing architecture of the WMS, allowing companies to leverage their current systems while integrating AI to improve efficiency and decision-making.
These AI-driven “super managers” would have the authority to override traditional rules and workflows, allowing businesses to gradually transition to AI-powered systems without disrupting ongoing operations.
As businesses continue to embrace automation, AI is poised to play a central role in transforming supply chains. From sales and administration to warehousing and logistics, AI offers a more flexible, adaptive, and intelligent approach to automation. However, successfully integrating AI into existing systems requires a holistic approach that bridges the gap between legacy infrastructure and next-generation technologies.
The key takeaway is clear: automation and AI are not just about replacing human labor—they are about creating smarter, more efficient systems that can adapt to the ever-changing demands of the business world. Companies that take a proactive, forward-thinking approach to AI integration will be better positioned to thrive in the future of automation.
As AI continues to evolve, it’s crucial for businesses to stay ahead of the curve by continually updating their strategies and systems to leverage the latest advancements in technology. The future of AI is dynamic, and those who embrace this change today will be prepared to succeed tomorrow.
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Jennifer Hall shares insights into the transformative effects of AI on warehouse efficiency and supply chain operations, taking a practical standpoint.
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