AI Accounting Software: What It Automates Today and What Still Needs an Accountant

AI accounting software is good today at the reading and matching work in accounting: capturing invoices, receipts and statements, suggesting categories and GL codes, matching bank lines to the ledger, flagging unusual items and drafting first versions of reconciliations and summaries. Accountants still own the judgment: accounting treatments, estimates, sign-off, anything reported externally and advice to the business. The best setups give AI the first pass and keep people on the decisions.

This post maps accounting work into what AI can automate, what it can draft for review and what stays with an accountant, and shows how an AI agent can sit on top of the ledger you already have. It describes the software category in general rather than ranking vendors. For the wider picture, see our finance IT solutions guide.

What the evidence says so far

The most useful public evidence comes from the PCAOB, the US audit regulator. In a July 2024 staff Spotlight on generative AI in audits and financial reporting, staff described integration as "in its early stages but rapidly evolving." Preparers reported using generative AI to create first drafts of internal documents, and some used it "to assist in the performance of less complex and repetitive processes, such as preparing account reconciliations or to assist with identifying reconciling items."

The same report is clear on oversight. Firms investing in these tools expected AI "to augment, but not replace, humans in auditing or in financial reporting," and preparers stressed that human involvement in supervising AI and reviewing its output continues to be important. That matches what we see in practice: AI earns its place on volume and repetition, and people stay accountable for the numbers.

Three bands of accounting work

Sort every task into one of three bands. The band decides how much review it needs.

	Examples	How AI is used
Automate with checks	Invoice and receipt capture, bank line matching on clear rules, duplicate detection, data entry	Runs on its own; code checks every result; exceptions go to a person
Draft for review	Category and GL code suggestions, reconciliations, accrual lists, variance explanations, client queries	AI prepares with reasons and sources; an accountant reviews and approves
Keep with the accountant	Accounting treatments, estimates and judgments, journal sign-off, tax positions, reported numbers, advice	AI may gather evidence; the decision and sign-off stay with a person

Source: BufferSol; oversight principle consistent with PCAOB staff observations (July 2024)

BufferSol Technologies · buffersoltech.com

Automate with checks

These are high-volume tasks with a right answer that code can verify. Reading documents is the main one. Microsoft's bank statement model, for example, extracts account details, statement details and transactions from US bank statements and returns structured data. Our invoice processing workflow uses Microsoft's invoice model the same way, then checks totals, suppliers and duplicates in code before anything moves on.

"Automate" never means "unchecked". Every extracted value is validated, low-confidence results go to a person, and the exception queue is reviewed weekly.

Draft for review

This is where most of the time savings are, and where AI's mistakes are easiest to catch. The AI prepares the work and shows its reasoning; the accountant reviews it.

• Categorizing and coding: a suggested category or GL code per transaction, with the reason ("same supplier coded to Software on the last eight invoices").
• Reconciliations: balances pulled, supporting detail matched, unexplained differences listed with likely causes.
• Month-end lists: goods received but not invoiced, invoices waiting for approval, items stuck in exceptions.
• Variance commentary: a first draft of why a line moved, with the transactions that drove it.
• Client or colleague questions: a draft reply with the figures and their source.

Keep with the accountant

Some work is judgment by definition: revenue recognition and other accounting treatments, estimates and provisions, impairments, tax positions, journal sign-off, anything that goes into reported numbers, and advice. AI can collect the evidence and draft the memo, but the decision and the sign-off belong to a qualified person.

How an agent sits on top of your ledger

You rarely need to replace your accounting system to use AI. Many packages now include AI features, and for standard work inside one system those may be enough. When work spans several systems, such as the ledger, bank, inbox, document store and chat, an agent on top can do more.

OpenAI's practical guide to building agents describes agents as systems that use tools to gather context and take actions in external systems, within guardrails. In accounting, that design looks like this:

Our invoice workflow follows this pattern: it extracts and checks invoices, matches them to purchase orders and receipts, routes approvals in Slack and posts approved invoices to the accounting system with a full audit trail. The agent never pays anyone on its own.

Where AI goes wrong in accounting

Knowing the failure modes tells you where to put the checks.

• Plausible but wrong values. A model can read a blurred total or a date in the wrong format and return something that looks right. Validation in code (totals add up, dates fall in the period, the supplier exists) catches most of these.
• Pattern-following on a changed situation. A coding suggestion based on history will repeat last month's treatment even when the contract or the accounting changed. New suppliers, new contracts and unusual amounts should always go to review.
• Confident explanations without evidence. A variance comment that sounds right but cites no transactions is worse than none. Require every explanation to point at the entries behind it.
• Instructions hidden in documents. Invoices and emails come from outsiders. Treat their text as data, never as instructions, and give the reading step no write access.

None of these is a reason to avoid AI. Each is a reason to keep the three bands, the checks and the review in place.

How to judge any option

Whether you are comparing accounting packages with AI features or a custom agent, ask the same questions:

1. How does it check its own output? Look for validation rules, confidence levels and an exception queue, not just a claim of high accuracy.
2. Does it explain itself? Every suggestion should come with a reason and a source an accountant can check in seconds.
3. What does the audit trail show? Who or what did each step, based on which evidence, and who approved it.
4. What can it change without approval? The answer should be "nothing that matters".
5. What happens to your data? Where it is processed, who can see it and whether it is used for training.

For governance more broadly, NIST's AI Risk Management Framework is intended for voluntary use to help organizations manage AI risks and build trustworthiness into how AI systems are designed, developed, used and evaluated. Its approach of deciding accountability, mapping where AI acts, measuring accuracy and managing exceptions translates well to accounting controls.

Related reading: our posts on AP automation and on accounts receivable automation software apply the same three bands to payables and collections.

Getting started

List the tasks your team does every month and put each in one of the three bands. Pick the "draft for review" task that takes the most hours, collect a month of real examples with the correct outcomes, and test AI on them before changing anything. At BufferSol, you get a working agent on that data in under 30 days, with full rollout typically in 4 to 8 weeks. Our autonomous workflows team builds finance processes around your ledger, and our AI agents team handles the steps that need reading and judgment.