AI month-end close: a step-by-step workflow

Status: published as a field guide and marked untested by our desk. The steps and prompts are sound, but validate every figure against your source systems before relying on the output. AI must never be the system of record for the close.

The task

Month-end close eats days of senior finance time on reconciliations, variance explanations, and flux commentary. This workflow uses an AI assistant to draft the tedious parts — variance narratives, reconciliation triage, and the first pass of close commentary — while a human and the general ledger stay the source of truth.

Before you start

• Tools: your AI assistant of choice, exports from your ERP/GL (CSV is fine), last period's close file.
• Hard rule: never paste un-redacted customer, employee, or material non-public data into a consumer AI tool. Use an enterprise instance with a no-training guarantee, or anonymize first.
• The AI drafts; the GL decides. Every number it produces gets tied back to a source before it goes in the file.

The workflow

1. Triage the reconciliations

Feed it the trial-balance delta and let it sort the noise from the signal:

Here is this month's trial balance vs. last month (CSV attached). List the
accounts with the largest absolute and percentage movements. For each, tell
me whether the movement looks routine (e.g., payroll timing) or warrants
investigation, and what document I'd pull to confirm. Do not guess at
numbers — only use the figures in the file.

2. Draft variance / flux commentary

For accounts moving more than <$X or Y%>, draft a one-line flux explanation
in our standard format: "<account> moved <amount> <direction> driven by
<cause>." Leave the cause as a [FILL] tag wherever you can't infer it from
the data — never invent a driver.

The [FILL] discipline is the whole game: the model marks its own uncertainty instead of fabricating a cause.

3. Reconciliation checklist and status

Given our close checklist (pasted below) and these account balances, draft a
status summary: which recs are complete, which are outstanding, and the top
three items blocking sign-off. Flag anything where the subledger doesn't tie
to the GL.

4. First-pass close memo

Have it assemble the narrative from the verified pieces — then a human edits and signs.

Verify before you trust

• Tie every figure back to the GL or subledger. The model's arithmetic is not evidence.
• Reject any explanation without a source; [FILL] tags are features, not failures.
• Keep an audit trail of what was AI-drafted versus human-verified.

Gotchas and limits

• AI will confidently invent a variance driver if you let it. The [FILL] instruction is what prevents it — enforce it.
• It is not your system of record. Use it to draft and triage, never to compute the numbers that go in the close.
• Data residency and MNPI rules apply. Enterprise instance or anonymized inputs only.

What to try next

• Browse more AI workflows for Finance.
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Compliance note

This content is for informational purposes only and is not financial, investment, or accounting advice. Verify all outputs against authoritative sources before use in regulated contexts.