Validate Dynamics 365 Copilot's AP Matching and Collections Suggestions During UAT

What This Does

Before end users go live with Dynamics 365's AP matching or collections features, UAT has to prove the AI's suggestions hold up against the client's actual configuration, not just Microsoft's demo data. This is the check that turns an untested assumption into real acceptance criteria: Dynamics 365 Finance's Copilot features suggest which purchase order an incoming invoice matches and draft collections outreach from payment prediction scoring.

Before You Start

• You are testing inside the client's own Dynamics 365 sandbox or UAT environment, never a live production tenant with real vendor or customer accounts. Copilot's matching suggestions here should only ever see sanitized test data until the client has approved it for production use.
• The client's Dynamics 365 plan includes Copilot for Finance. Availability depends on the specific plan and add-on the client has purchased (included in some Dynamics 365 plans; check Microsoft's pricing page or the client's licensing contact for current terms), so confirm before assuming it is included.
• You have a set of known-good test transactions to compare Copilot's suggestions against. Testing without a known correct answer only tells you Copilot produced something, not whether it was right.

Steps

1. Feed Copilot a batch of test invoices

Load a batch of sanitized test invoices into the UAT environment and let the invoice capture feature suggest which purchase order each one matches. The feature is designed to improve its matching over time as users correct it, so early UAT results are a useful baseline to compare against later rounds.

2. Compare each suggested match against the known-good answer

For every invoice, check whether Copilot matched it to the purchase order your test plan says is correct. Note any mismatch, along with what field or condition likely confused it: a vendor name variant, a split shipment, a partial quantity.

3. Run the same check on collections summaries

If the client is also piloting Copilot's collections features, review a batch of AI-generated collections summaries and payment prediction scores against sanitized customer accounts with a known payment history. Flag summaries that misstate an amount, a due date, or a dispute status.

4. Document results as UAT acceptance criteria

Write up the match rate and any pattern of errors as a formal UAT finding rather than an informal note. If matching accuracy on your test set does not meet a target the client has agreed to, that becomes a go-live condition instead of a rounding error.

Real Example

Scenario: The client wants Copilot's AP matching live for accounts payable at go-live. Your test plan includes twenty sanitized sample invoices with pre-determined correct matches, including three tricky cases: a vendor with two name variants, a split shipment, and a partial-quantity delivery.

What you do: Load the twenty invoices, let Copilot suggest matches, and compare each one against your answer key.

What you get: Copilot correctly matches seventeen of the twenty and misses on the two vendor-name-variant cases and the split shipment. You document this as a UAT finding recommending a vendor name standardization step before go-live, rather than assuming the feature will simply improve on its own.

Tips

• Retest after any correction the team makes. The feature is designed to learn from corrected matches, and behavior can shift between rounds.
• Ask the client's AP lead what "good enough" looks like before UAT starts. A target match rate agreed in advance keeps the go/no-go conversation objective.
• Treat collections summaries the same way you would treat any AI-drafted client-facing communication. Check payment amounts, dates, and dispute status against the source record before it reaches a real customer.

Tool interfaces change. If a button has moved, look for similar AI/Copilot options in the same module.