Eric Kuvykin

By Eric Kuvykin. Substantially revised October 17, 2026. The original publication date and URL are retained.

Pick a small task with an observable answer

An AI project becomes easier to evaluate when the business can state exactly what the system should produce. “Improve merchant services” is too broad. “Sort unresolved deposit differences into a review queue, showing the source records for each suggested match” is a task that staff can test.

Begin with a workflow in which a person already knows how to verify the result. Define what the tool may read, what it may suggest and what it must never change on its own. Keep those boundaries in the pilot plan rather than relying on informal expectations.

Use a traceable pilot file

Create a limited set of historical examples with sensitive information removed where practical. Include straightforward matches, ambiguous cases and records that should remain unresolved. Reserve some examples for testing after configuration so the assessment does not merely reward memorizing the training set.

For every output, require a link or reference to the underlying invoice, transaction or support record. A fluent explanation without supporting evidence should remain an unverified suggestion. Staff need to distinguish a retrieved fact from a generated interpretation before either enters a customer communication or accounting decision.

Measure the work that remains

Track review time, correct suggestions, incorrect suggestions and cases where the tool should have abstained. Also record the cost of correcting errors. A fast first draft may create more work downstream if employees need to reconstruct where its numbers came from.

Compare the assisted workflow with the existing manual process over the same type of work. Do not count an automatically generated answer as a resolved case until the business’s acceptance condition is met. If the pilot reduces sorting time but leaves final judgment unchanged, describe that limited benefit accurately.

Keep control over permissions and changes

NIST’s voluntary AI RMF Playbook offers a framework for organizing risk-management questions. For a small merchant workflow, a practical implementation is a named owner, a written use case, a test record and a way to stop the tool. This checklist is an operational suggestion, not a certification of compliance with NIST guidance.

Require approval before the tool issues refunds, changes payment instructions or sends sensitive customer messages. Review where data is retained and whether a vendor may use it for other purposes. Treat integration permissions as part of the procurement decision.

A business with Florida, New York and New Jersey operations should test whether location labels, time zones and different account identifiers are preserved in the output. The aim is to prevent records from different entities or locations from being mixed. Do not use geography as a proxy for risk or assume one location’s examples represent every other location.

End the pilot with a written decision: expand a specific function, revise the design or stop. Preserve the evidence behind that decision so the next software update can be evaluated against the same standard.

Sources and next steps

Primary reference: NIST’s AI Risk Management Framework Playbook. Use the current resource and confirm implementation details with the provider or appropriate adviser.

Related reading: Payments Data: Build a Weekly Operating Review From Source Records; Payment Cybersecurity: Protect Access, Device Changes and Recovery.

For the wider operating context, continue to the Florida, New York and New Jersey business guides on All State Merchants. Its state and local resources complement the payment workflow discussed here.

Eric Kuvykin writes about payments, fintech and business operations. Browse the article library for additional practical guides.