Bank reconciliation can be a useful first enterprise AI workflow because it is recurring, has identifiable owners, and produces results that finance can check. The opportunity is often to reduce the time spent gathering evidence and preparing exceptions for review.
It is not automatically the right starting point. If bank data is incomplete, ledger access is unreliable, or the process itself is undefined, those foundations need attention first. And where deterministic matching rules already solve the problem, an AI agent may add little value.
Separate matching rules from judgment
Use established rules for straightforward matches. Amounts, dates, references, and known timing differences may support deterministic processing. AI assistance is more useful where a person currently reads descriptions, assembles supporting documents, or drafts an explanation for an exception.
Keep the evidence available to the reviewer. A suggested match should identify the source transactions and explain why they belong together. Ambiguous cases should remain unresolved until someone with the right authority can assess them.
Start with a bounded reconciliation scope
Choose a defined account, entity, or transaction class with reliable source data. Document the process owner, reviewer, reconciliation frequency, and materiality rules.
An illustrative first workflow might read approved bank and ledger data, apply existing match rules, gather evidence for remaining exceptions, and prepare a review queue. Posting changes can remain outside the initial scope.
Expanding into proposed entries requires additional controls over permissions, approval, and duplicate execution. The organization should preserve its segregation-of-duties requirements rather than assigning every responsibility to one convenient reviewer.
Make exceptions easier to resolve
A useful exception queue groups work by the action needed: missing documentation, timing differences, suspected duplicates, or a potential correction. Each case should include supporting evidence, a proposed next step, and a clear route for escalation.
Record reviewer corrections so the team can improve the workflow. Do not treat repeated approval as proof that every future case is safe to automate. Changed source data or business rules can invalidate yesterday's pattern.
Our agent governance approach describes how to manage that progression.
Measure quality alongside time saved
Establish a baseline before launch. Track review time, unresolved exceptions, aging, false matches, and corrections required after processing. Evaluate results on representative periods, including unusual activity.
A high matching rate is not useful if it hides errors. The desired outcome is a reconciled account supported by evidence, with less effort and no loss of control.
Reuse the foundations carefully
Approvals, monitoring, and audit patterns can support later workflows such as cash application or close coordination. Each still needs its own business rules, integrations, and evaluation.
Conduit provides the orchestration layer for that work. Talk with our team to assess whether reconciliation is the right first use case for your environment.
