When I was starting out in the military in the 1980s, we had something called “green machines” – very large computers in suitcase-sized cases that were tough enough to be parachuted out of a C-130. This was how we did logistics in the dirt. It was the cutting edge back then, and it got me thinking about technology and what it might be capable of in the future. I’ve now been in warehousing and distribution for decades, running after pallets, chasing down orders and learning how to turn overstock into success. I’ve seen how logistics technology has evolved. Innovations like advanced AI and robotics make those old green machines look like dinosaurs. This article is part 2 in a series about how distributors can get the most out of today’s tech. 

In part one of this series, I explained how wholesale distributors can generate a lot of value from their current ERP and WMS systems if they document everything, train personnel and ensure that all features are used as intended. Now that you’ve aligned your existing technology stack, it’s time to make your operation ready for the tech that is defining the future of distribution: artificial intelligence. 

AI is already changing this industry. But here’s the truth: You can’t just bolt on the latest AI tools and expect magic. Distributors that have successfully implemented AI didn’t do it by chasing trends – they built the right foundation first. 

If you’re running a distribution business, you won’t get by on buzzwords alone. You need a step-by-step plan to make AI a strategic advantage and not a pricey failed experiment.  

Step 1: Develop a Digital Strategy That Matters 

Start by asking this question: What business problems are you trying to solve?  Managers who skip this step often build expensive technologies that bring no change. Your digital strategy should be based on specific objectives, such as reducing the inventory costs by 10% or increasing OTIF rate to 99%.  

Next, assess your current tech stack. Are you being held back by outdated systems, clunky processes or poor data quality? 

Spend where it will result in real growth, efficiency or an enhanced customer experience, not where it sounds good. Without this focus, you’re essentially spending your money on shiny trinkets. 

Step 2: Move Core Systems to the Cloud 

Your ERP and WMS are the core. If they’re on premises, you’re limiting AI’s potential. Meanwhile, you’re missing out on the cloud’s many advantages, including: 

• Scalability: As your business expands, you can easily expand your system in kind, and you don’t have to spend a lot of money on hardware to do it. 
• Cost-effectiveness: You only pay for what you use – no more wasting money to maintain half-empty servers.
• Availability: Real-time data is always available to your team, whether they’re on the warehouse floor or working from home.
• Integration: AI tools and analytics solutions are more readily connected with cloud platforms.

When migrating, determine if a single-tenant environment (a dedicated instance that provides more control) or a multi-tenant solution (shared infrastructure that is less expensive) will better meet your requirements. In theory, AI can be run on-prem, but the cloud is more flexible and reasonable for most distributors. 

Step 3: Create a Data Lake for Intelligence 

AI needs access to data, which is hard to provide when your data is all over the place. A data lake is a cloud-based central repository that includes structured data, like inventory records and order history, as well as unstructured data, like customer interactions and IoT sensor signals. With data pertaining to things like sales patterns, supplier performance and operational metrics centralized in one location, AI can discover patterns that would be otherwise hard for humans to identify and generate recommendations that can help improve your decision making. 

Step 4: Improve Data Quality 

AI is only as good as the information it receives; a bad input will result in a bad output. There are several constraints here, including: 

• Unsynchronized systems. Break your data silos so your AI tools don’t get tripped up by conflicting or outdated information.
• Data formatting and rules. Data such as SKUs, units and dates can be entered and stored in many different formats. For example, some countries format dates with the month first, while others put the day first. Normalizing data across your company will make it easier for AI tools to recognize and use it.
• Data errors. Use validation tools to detect errors at an early stage.

Cleaning data may not be the most glamorous part of the process, but it’s the difference between effective and ineffective AI. 

Step 5: Quick AI Benefits 

With the base set, aim for quick wins without overthinking it. Here are some areas where AI can deliver tangible value in relatively short order: 

• Order Anomaly Detection: Small distributors have built basic machine learning models that analyze incoming orders and flag unusual patterns, such as a customer placing an order for 10 times their usual quantity or for products they have never bought before. These systems can operate on current infrastructure with almost no configuration. 

• Email Classification and Routing: AI tools can automatically organize, prioritize and respond to customers’ emails and inquiries to the right department. This requires minimal integration with existing systems and can often be done as a standalone solution. 

• Inventory Reorder Point Optimization: Simple machine learning models can study the historical sales data and suggest more accurate reorder points for different SKUs without altering the complete warehouse management.  

• Basic Chatbots for Customer Service: Entry-level AI chatbots can handle the basic functions of customer service such as processing order status queries, referencing return policies and checking product availability.  

• Document Processing Automation: AI-powered document processing tools can extract data from invoices, purchase orders and shipping documents with very limited configuration, thus eliminating the need for manual data entry and potential errors.    

These kinds of tools can prove the value of AI right away. Once you have them, move on to more sophisticated applications: demand planning, dynamic pricing, maintenance, order processing and route planning.  

Step 6: Prepare Your People 

AI will not replace your team; rather, it will change the way they work. Success depends on their buy-in, so the change management is essential. 

When Tom, an AR clerk who has been entering invoices against payments for decades, learns that AI will be used for this task, his first thought might be about job security. But if this change is presented as an opportunity that lets Tom concentrate on solving complex cases and offer faster service to customers, he’ll understand AI to be a valuable ally that enhances his work.  

The best AI implementations are the ones that balance technology and human expertise. When you move repetitive tasks to AI, your people will have more time to work on new strategic functions, where their skillsets will be more valuable. 

The Bottom Line 

Customers expect low prices, fast delivery and flawless execution. If you prepare well, AI can help you deliver all three. A clear strategy, cloud systems, good data and a ready team are not just AI requirements – they’re the only way to compete in this market. 

Where is your operation? Are you checking data quality? Considering a cloud shift? AI readiness is not a one-time exercise; it is a capacity that differentiates leaders from followers in distribution. 

Also in this series: 

• Part 1: How to Unlock the Full Potential of Your Existing Technology in Wholesale Distribution, which encourages distributors to properly leverage the tech they already have before looking to upgrade. 

• Part 3: AI is Actively Transforming Warehouse Operations and Inventory Management, which looks at analytics, automation and robotics, with real-world examples.  

• Part 4: Overcoming the Challenges of AI Implementation in Distribution, which digs into cost, training and integration, and how to overcome those challenges for a strong ROI. 

• Part 5: The Future of AI in Wholesale Distribution: What’s Next and How to Stay Ahead, which explores emerging trends such as supply chain visibility and last-mile delivery.