A practical look at how distributors are using AI to move faster, gain visibility across operations, and keep pace with rising customer demands, and where it pays off first.
Most distributors have stopped asking whether to use AI and started asking where it actually pays off. Spending is up across forecasting, fulfillment, and customer service, but the returns are uneven, and the difference usually comes down to where a distributor chose to start.
For years, AI in distribution lived in pilot projects and conference keynotes. That has changed quickly.
In one multi-year industry benchmark, the share of distributors doing nothing with AI fell from roughly 62 percent in 2023 to about 29 percent in 2025, while the group using it across several parts of the business climbed to about a third.
The shift matters because it reframes the conversation: AI in distribution is less about automation for its own sake and more about keeping operations in step with the business around them.
Many distributors treat the presence of AI in their operations as the advantage, as though adopting it is what puts them ahead. The real advantage is not the technology itself, but what it does once it’s in use, such as giving the business the ability to respond faster and more accurately as order volume, SKU counts, and customer expectations rise.
For example, a distributor that forecasts demand a week sooner—or answers a customer in minutes instead of hours—wins on service without adding headcount, and this is the lens worth keeping as the use cases pile up.
The most useful way to understand AI in distribution is to look at where it already earns its place. Four areas stand out across supply chain and logistics operations:
This is the clearest win for most distributors. AI reads order history, seasonality, and live demand signals to sharpen forecasts and right-size stock, which cuts both stockouts and excess.
Research from McKinsey on distribution operations points to inventory reductions in the range of 20–30%, along with lower logistics and procurement costs, when AI strengthens demand planning. One building products distributor lifted fill rates by 5–8% after putting an AI-driven supply chain control tower in place.
Generative AI gives sales and service teams a real lift. It drafts quotes, surfaces cross-sell and reorder suggestions, and handles routine post-sale questions, freeing people for the conversations that need a human.
Deloitte points to internal sales and customer service as one of the largest near-term opportunities for distributors to capture value from generative AI.
AI speeds up order entry, reads incoming documents, and flags exceptions before they turn into late shipments. When demand swings, it helps fulfillment teams hold service levels without scrambling, which keeps promises to customers intact during the periods that test them most.
Pricing is where small improvements add up fast. AI watches pricing signals and margin leakage across thousands of line items, then flags where a number has drifted out of line. With margins tight across most categories, the payback here is often the easiest to measure.
For all the momentum, results still lag enthusiasm. Research from MDM and NAW covering more than 400 distributors found nearly nine in ten companies active with AI in some form, yet far fewer are happy with what they are getting back. Roughly three-quarters expect meaningful value from AI, while fewer than one in five say they have actually seen it.
The reason for that gap is rarely the technology, and knowing what actually causes it is how you avoid spending on AI that never pays off.
The reason AI pilots stall is rarely the model itself. Most projects break down for three reasons:
All three of these reasons are fixable, and none of them is an AI problem.
Given that gap, sequencing matters more than ambition. The distributors seeing returns tend to start where the data is cleanest and the business case is clearest—usually demand forecasting, inventory, and customer-facing support. Once value is proven in one area, they expand from a win instead in phases of applying AI across the business on a single broad rollout.
The most common starting question is what data AI needs. For most distributors, the answer is already in the building: order history, inventory records, and customer data, which is the data sitting in existing systems are exactly what AI models draw on.
The first move is making that data usable and trustworthy, not buying another tool. A focused effort to clean and connect the data you already own does more for an AI project than any single piece of software.
AI rewards distributors who know where their data and their best use cases line up. Before committing resources to implementing AI, it helps to take an honest look at what you already have and where AI would pay off soonest.
AI in distribution is used to improve forecasting, inventory management, order processing, pricing, and customer service. Many distributors are building on the data already inside their ERP systems, using modern cloud platforms like Infor CloudSuite Distribution to connect that data and make AI-driven insights more reliable and actionable.
The most common AI use cases in distribution include demand forecasting, inventory optimization, automated order management, dynamic pricing, and AI-assisted sales and customer support. Distributors often see the fastest value when these use cases are built on a unified data model within a modern ERP environment rather than layered onto disconnected systems.
Distributors can use AI to improve operations by automating repetitive processes, increasing forecast accuracy, and identifying patterns in customer and order data. For organizations running legacy systems like FACTS or A+, the most effective approach is to extend the value of that data through a connected cloud platform that enables AI without disrupting core operations.
AI delivers the most value in areas with high data volume and repeatable processes, such as forecasting, inventory planning, and customer interactions. These areas benefit most when data is centralized and continuously updated—something distributors achieve more consistently when moving from on-premise environments to cloud-based ERP platforms.
AI in the distribution industry relies on clean, connected data such as order history, inventory levels, customer records, and pricing data. For many distributors, this data already exists within their ERP system. The key step is modernizing how that data is structured and accessed—often through a cloud ERP like CloudSuite Distribution—to create a reliable foundation for AI.
AI projects in distribution often fail due to disconnected data, unclear ownership, or use cases that are not tied to measurable business outcomes. Distributors that attempt to layer AI tools on top of fragmented or on-premise data environments tend to see pilots stall. A more effective approach is to first establish a clean, connected data foundation that AI can build on. Products like Aktion’s DataBridge and other data products can help you bring your data in line with the structured needed to see real AI value.
To get started with AI in distribution, organizations should focus on a specific, high-impact use case (such as demand forecasting or inventory optimization) where data is already strong. Many distributors begin by modernizing their data foundation within a cloud ERP platform, then expanding AI capabilities in stages, reducing risk while building on proven results.
Patrick Brennan, Vice President of the Supply Chain Division, leads the team providing support and services tailored to the distribution industry.
