If you haven’t recently found yourself surprised by what artificial intelligence (AI) can do, perhaps thinking, “I didn’t know AI could handle that,” you may already be falling behind. Fortunately, there’s no shortage of opportunities to explore how AI can support your supply chain.
Today’s supply chains are entering a new era of AI and advanced technology. While many industries have raced ahead, supply chains often lag behind, not because leaders lack interest, but because building the right foundation is complex and often undervalued.
Where AI stands in supply chains today
Some early adopters are already seeing results with tools such as:
- Optical character recognition that reads spreadsheets and PDFs, then feeds the data directly into order management systems.
- Automation platforms that create, route, and dispatch purchase orders once a planner signs off.
- Inventory planning systems for vendor-managed or off-site inventory, minimizing manual oversight and expensive telemetry.
- Demand planning engines that factor in outside variables like weather or major events to deliver smarter forecasts.
- Warehouse automation that improves space utilization and reduces your reliance on manual labor.
At its core, using AI means training a system to make decisions based on defined rules and learned patterns. AI doesn’t think for itself—it applies structured logic in line with its training.
How you can prepare
Even if AI feels like it’s years away for your business, investing in the right groundwork now will build a tech-focused culture and often deliver immediate gains, especially for teams already using enterprise resource planning (ERP) or materials requirement planning (MRP) systems.
Here are four ways to start.
1. Begin with a clear vision
Start by defining what you want your future to look like and determine how to leverage tools to support it. In today’s digital environment, it’s more relevant than ever to ask, “Can we use technology to solve this?”
Whether the goal is increasing inventory density, reducing headcount without sacrificing throughput, or sharpening forecast accuracy, a well-defined problem statement is your first step toward meaningful transformation.
2. Prioritize people and processes
Before you automate, make sure processes are standardized.
Take purchase orders, for example. The system needs to know how to verify pricing, find the right approver based on order value, determine expected receipt dates, and more. There’s a great deal of information that’s required throughout the entire process; and with that comes the need to make decisions.
If your team doesn’t have clear workflows or decision-making processes in place, automation—and AI—will be tough to implement effectively.
3. Standardize and cleanse your data
Tech systems rely on clean, consistent data. A system can’t automatically tell that “something,” “some.thing,” “some_thing,” and “Smthg” are all the same. Training a system often starts with building mapping tables, but it’s far more efficient to cleanse and normalize your source data from the start. This may require a shift in how data is collected, and where it is housed.
Automated order entry—with or without using AI to support the automation—is a popular goal, but for it to work, your system needs to know where to find information and how to interpret it. Some organizations solve this by requiring customers to use portals or structured templates.
While advanced systems can parse unstructured documents like PDFs, they still need training to identify fields such as company name, order quantity, or purchase order number—plus all the ways your customers might label them.
4. Define decision-making rules
AI readiness also means documenting the “if/then” logic that drives your decisions. Even though AI can handle countless variables, it still needs clear rules. Take time to map out how your team makes decisions, even routine operational choices that seem quite basic. This step is critical for successfully integrating AI into your business.
Don’t overlook your foundation
All of these steps assume you have a core system—like an ERP—already in place. If your supply chain doesn’t have a cohesive platform that connects order entry, procurement, production tracking, costing, and accounting, putting that foundation in place should be top priority.
And, if you’re using an ERP that’s underperforming, these strategies will not only drive immediate improvements, but also set you up for future success with AI and other advanced technologies.
Looking ahead
Don’t wait for AI to be fully mature to start preparing your business. By building a clear vision, strengthening processes, cleaning up data, and defining decision rules now, you’ll be ready to take advantage of the opportunities AI can bring to your supply chain. And in the meantime, you’ll likely uncover efficiencies and cost savings that benefit you long before your first AI project goes live.
