Trade spend has always been a complicated part of the business. Today, it is also one of the least scalable.

Whether you are a large manufacturer managing national programs or a focused supplier working with a few key distributors, the process is similar. Agreements are negotiated by sales. The complexity shows up later, when bill backs arrive, deductions are logged, and finance teams are left to untangle the data.

The burden lands squarely on back-of-house teams. That is where the cost of poor trade data becomes visible. It is also where the most immediate opportunity exists.

Where the Work Really Happens

Most trade management systems struggle under the weight of inconsistent inputs. Pricing agreements, volume incentives, and national contract terms are often formatted differently by each partner. A distributor may use one product code, while your ERP expects another. The bill back arrives in one format. The deduction is recorded in a different one. The result is friction that slows payment and complicates reconciliation.

This is a data and workflow breakdown that affects financial performance.When these mismatches pile up, margin erodes quietly. Payments are delayed. Validation takes more effort. Cash remains unsettled. Relying on manual cleanup to solve these problems creates hidden cost over time.

The Role of AI in Trade Spend

AI is not a substitute for a clear trade strategy. But it can make that strategy work harder.

In many cases, the underlying workflow is already solid. What AI can do is enhance what is already working and reduce the need for human intervention by focusing attention only where something looks off. It brings consistency to unstructured inputs and helps teams understand what is happening earlier in the process to respond with confidence.

‍From Noise to Action

The challenge is the volume of files and the variation in how they arrive. Distributors and group purchasing organizations often send claims in formats that do not align with your internal systems. That disconnect makes reconciliation harder than it needs to be.

AI learns from your historical data. It detects patterns in bill backs, normalizes formats across sources, and provides clear, validated information for teams to act on. The goal is faster processing, greater visibility and control.

Trade spend is where automation can deliver fast value. This topic was covered in more depth during our recent executive session, From Cost to Capability: Rethinking the Manufacturing Ecosystem in the Age of AI. If you missed it, you can watch the full conversation on demand here. It explores how AI can drive meaningful improvement in supply chain processes by starting with the areas that impact margin most.

iTradeNetwork applies AI to help finance and sales teams resolve data and process gaps, such as mismatched items and accounts, duplicate claims, and late issue detection. Cerena for Manufacturers supports this by structuring trade data upstream, creating a more reliable foundation for automation and AI.