Using LLMs to Automate the Monthly Close: A Practical Playbook

Where AI actually helps in the close

The monthly close is mostly reconciliation, variance explanation, and reclassification. That's exactly the kind of structured, repeatable work where an LLM assistant shines — if you wire it to your real data.

A workable pipeline

1. Export trial balance and prior-month actuals
2. Prompt an LLM to draft variance explanations for every line over threshold
3. Cross-check its draft against the GL detail (never publish AI output unreviewed)
4. Post approved journal entries
5. Roll forward the commentary into the board deck

Guardrails

• Treat the LLM as a junior analyst: it drafts, a human approves
• Never feed it PII or raw customer data you can't control
• Version your prompts — output quality is a function of prompt quality

I've written the JavaScript to wire this directly to ChatGPT and other LLM APIs for clients. The time savings on a 15-day close are measured in days, not hours.