Blog October 10, 2025

A Practical Guide to AI in Warehouse Management

By: Jeremy Rotenberg

Updated: October 10, 2025 | 4 min read

Artificial Intelligence. It’s the talk of every industry, and the supply chain is no exception. We see so many promises of revolutionary change, it can be easy to get lost in the noise. When does the hype end and the real work begin? How do you actually move from buzzwords to real organizational change?

Lots of leaders are struggling to separate the hype from the reality. 74% of businesses are reporting no meaningful value from AI use, so what gives? The key is to understand that AI isn’t a magic wand to wave at every problem. It’s a powerful tool that, when applied properly, can produce unprecedented levels of efficiency. But success depends on knowing when, where, and how to use it. This article offers a clear, strategic framework for leveraging AI in your operation, and how to actually get use out of it.

AI vs. Traditional Algorithms

First, let’s draw a clear line in the sand. Not all “smart” technology in your warehouse is AI. For decades, operations have run on powerful, reliable algorithms.

Algorithms are the bedrock of warehouse management systems. They are sets of hand-coded, deterministic rules written by programmers. Think FIFO picking rules or wave planning logic. They are fast, reliable, and their logic is easy to trace. They follow explicit instructions perfectly.
AI and Machine Learning, on the other hand, don’t just follow instructions; they infer patterns from data to learn and improve over time. AI thrives in areas where the rules are hard to define or constantly changing, making it ideal for prediction, adaptation, and optimization.

Every operational control system you use today is fundamentally algorithmic. The goal isn’t to rip and replace these systems, but to make them better by layering AI where it delivers the most value.

Where AI Delivers Real-World Value

AI is not a solution for everything, much like a microwave oven can’t replace your entire kitchen. A microwave doesn’t do everything, but what it does—heating food quickly—it does better than anything else. AI is the same. It provides targeted benefits in specific areas.

Here are two powerful examples of AI in warehouse management today:

Proactive Replenishment & Error Prediction: Traditional replenishment systems often rely on a simple Min/Max algorithm: when the quantity of an item in a pick slot drops below a certain level, a task is created to refill it. This is reactive. An AI-powered approach is proactive. Agents living within the WMS can analyze order patterns and fulfillment speed to predict when a slot is going to run low or become empty far in advance. It can then dynamically assign replenishment tasks with the ultimate goal of achieving zero stock-outs in the pick line.
Radically Improved User Experience: Think about how your warehouse team interacts with your WMS. It’s often rigid and predetermined. With AI-driven voice technology, that interaction becomes a two-way street that can capture feedback and transfer it into actionable tasks. For instance, a worker could say, “Hey, my pick path is having me pick the broccoli before this heavy rice bag. I think a re-slot is required because the produce is getting squished”. The AI not only understands this but can instantly trigger a slotting inspection task for a supervisor to review. This transforms frontline workers into active participants in a continuous improvement loop.

Your 4-Step Checklist Before Jumping into AI

Ready to add AI to your operation? Not so fast. The difference between a successful AI project and a frustrating failure is asking the right questions before you start. Here is a simple decision-making process to guide you.

Is your current algorithm already working well? If a process is already highly efficient and reliable, leave it alone. Don’t replace a perfectly good rules-based system with a more complex AI model just for the sake of it.
Is this problem a genuine fit for AI? AI excels at prediction, optimization, and dealing with uncertainty. If the challenge can be solved with straightforward rules and optimization, an algorithm is likely the better choice.
Do you have the right data? This is the most critical question. Machine learning models are not magic; they are trained on data. You need a large volume of clean, relevant, historical data from your WMS or other systems to train a model effectively. Without good data, you have nothing. As we say all the time, “garbage in, garbage out”.
Can your systems take AI input? An AI model is useless if it can’t act on its conclusions. Once your AI has optimized a pick path or predicted a replenishment need, can it push those instructions back into your WMS?. If your current systems can’t accept these external instructions, you may need to consider upgrading to platforms natively built for AI.

Conclusion: From Hype to Hope

The adoption of any new technology follows a predictable cycle: a peak of inflated expectations, a trough of disillusionment, and finally, a plateau of productivity. Many companies are currently sliding into that trough with AI because they approached it as a cure-all.

The key to successful AI deployment is to skip the disillusionment by being strategic about how, when, and where you are implementing it.

Ready to find the right balance for your operations? Contact us today to help you navigate the path forward.

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