A practical path for finance and procurement teams to prove out AI-powered invoice processing before rolling it out company-wide.
Most accounts payable teams do not need convincing that manual invoice processing is slow. What they need is a low-risk way to prove that an AI-powered alternative works in their own environment before committing to a full rollout. That is what a pilot is for.
A good AP automation pilot is not a proof of concept in a demo environment; it is a real, bounded slice of the actual workload, run long enough to produce honest results.
Step One: Define a Bounded Pilot Scope
Rather than automating all invoice processing at once, start with a defined slice: one vendor category, one business unit, or one invoice type. Utility invoices are a common starting point because their volume is steady and their format is predictable, which makes it easier to tell whether a result is a genuine pattern or a one-off exception.
The scope should be small enough to monitor closely, ideally with someone reviewing every flagged exception personally during the pilot, but large enough to generate a meaningful number of invoices to learn from.
The point of a pilot isn’t to prove that AI can process invoices in general. It’s to prove that it can process your invoices, integrated with your ERP, without creating new work somewhere else.
Step Two: Test Against Real Exceptions
During the pilot, the metric that matters most is not how many invoices got processed; it is what happened to the ones that did not go smoothly. In Smart Payables, that means watching how reliably an invoice gets matched to its purchase order and receipt, how often something gets flagged for review, and whether those flags were the right call. A short pilot period, tracked consistently, will usually surface a handful of concrete numbers worth watching:
- Match rate: The share of invoices that process cleanly against a purchase order and receipt without needing manual intervention.
- Exception accuracy: Of the invoices flagged for review, how many actually needed it versus how many were flagged unnecessarily.
- Cycle time: How long it takes an invoice to move from receipt to approval, compared to the manual process it is replacing.
Step Three: Scale Based on ROI, Not Momentum
Once the pilot has run long enough to produce a stable picture, typically a full billing cycle or two, the decision to expand should rest on those numbers, not on enthusiasm. A pilot that shows a high match rate, accurate exceptions, and a shorter cycle time is a reasonable case for extending Smart Payables to additional vendor categories or business units.
Scaling gradually, using the same measurement discipline at each stage, is what turns a successful pilot into a sustainable, organization-wide shift in how invoices get processed, rather than a rollout that outruns the evidence for it.
I look forward to seeing how these developments will improve service levels and customer satisfaction in the freight industry!