Key takeaways
- Start where the agent prepares work and a person approves it.
- Read-only and draft-only tasks carry the lowest risk.
- Log every action and keep an audit trail from day one.
- Expand permissions only after the evidence supports it.
Why finance is right to be careful
Finance operations run on accuracy, controls and audit trails. An error in a payment or a journal entry is not a quirk, it is a problem with real consequences. So when someone proposes an AI agent that can take actions in accounting systems, caution is the correct reaction. The question is not whether to use agents, but where the downside is small, the review is easy and the benefit is clear.
A useful rule is that the agent should prepare and a person should decide. Systems that move money or post to the ledger without review are a later stage, if they are appropriate at all.
Start 1: Reconciliation preparation
Bank and account reconciliation involves a lot of matching, chasing and note writing. An agent can read exports from both sides, propose matches, group likely timing differences and list the items that need human attention with a short explanation for each. Nothing is posted. The accountant reviews the proposals, accepts or rejects them and handles the exceptions.
- Access: read-only on bank and ledger exports.
- Output: a proposed match list and an exceptions summary.
- Review: the preparer confirms each proposal before any entry is made.
- Value: less time on routine matching and more on real discrepancies.
Start 2: Invoice intake and coding suggestions
Incoming supplier invoices arrive as PDFs, emails and portal downloads. An agent can extract fields, compare them with the purchase order or contract, flag mismatches and suggest a general ledger account and cost center based on how similar invoices were coded before. It puts everything into a queue as a draft.
Your existing approval workflow stays intact. The agent's job is to shorten the data entry and highlight the exceptions, such as a new bank account on a known supplier or an amount that does not match the order. Treat any change in supplier payment details as a high-risk event that always requires human verification through a separate channel.
Start 3: Month-end variance commentary
Every close, someone writes explanations for why actuals differ from budget or from last period. An agent can gather the figures, calculate the differences, pull relevant notes and draft a first version of the commentary. The analyst then checks it against what they know, corrects it and adds business context that the data cannot show.
This works because the output is text for internal review, the source numbers are traceable and a knowledgeable person always edits before it goes anywhere. Ask the agent to cite the specific lines behind each statement so the reviewer can verify quickly.
Controls that make all three safe
- Least privilege: give the agent the minimum access it needs, and prefer read-only.
- Separation of duties: the agent that drafts cannot also approve or post.
- Logging: record inputs, outputs, the reviewer and the decision for every item.
- Evaluation: compare the agent's suggestions with final human decisions and track how often they agree.
- Exit path: keep the manual process documented so work continues if the agent is paused.
Earning more autonomy
After a few close cycles, you will have evidence about accuracy and about which categories are consistently right. That is the moment to consider carefully expanding scope, for example auto-accepting the highest-confidence matches under a defined limit, with sampling to confirm quality. Move in small steps, involve internal audit early and keep the ability to switch off any automation instantly.
