How accounting firms are using AI to close faster

Demand for accounting services is growing. The supply of accountants isn’t.

The accounting and auditing workforce has shrunk by more than 17% since 2020, according to the Bureau of Labor Statistics. The number of new accounting graduates has hit a 20-year low: 55,152 bachelor’s and master’s degrees in 2023-24, down 6.6% year over year, according to the AICPA.

That shift is already visible. Among accounting firms on Ramp, the share spending on AI vendors grew 39% in six months. And firms that started early are spending more over time, not less: median monthly AI spend among early adopters nearly tripled by May 2026.

AI platforms can now handle the execution work that consumes most of a firm’s time, letting accountants focus on the work that actually requires their judgment.

Where accounting firms are losing the most time

Most of the time that accounting firms lose isn’t lost to complexity. It’s lost to repetition.

Execution work is the default: Transaction coding, bank and credit card reconciliation, journal entry preparation, schedule roll-forwards: these tasks are deterministic. Given the right inputs and the right rules, the output is predictable. But they’re still done manually, line by line, for every client. That’s where most of the day goes.

Month-end close is where the pressure compounds: The close isn’t one client’s problem. It’s every client’s problem, on the same deadline, every month. Coordinating journal entries, accruals, payroll entries, and schedule updates across 20 or 30 or 50 engagements simultaneously is where firms hit their capacity ceiling hardest.

Scaling creates more of the same problem, not a different one: The natural response to growth is to hire. But onboarding a new staff accountant takes time, training, and margin. And as the team grows, maintaining consistent quality across all client work becomes its own management challenge. Firms don’t just need more capacity. They need a way to apply their standards at scale without adding headcount to every new client.

How AI changes the equation

AI addresses each of these problems at different levels depending on the tool. Here’s where the most meaningful impact tends to show up.

For execution volume: An AI agent codes transactions against your client’s chart of accounts rules as they come in, reconciles accounts against statements, and surfaces only the exceptions that need your review. For a firm managing multiple clients on QuickBooks, this means staying current on every book without touching every line item. The deterministic work runs in the background. Your team handles what the AI flags.

For the close: Instead of coordinating close tasks manually across your entire client book, an agent works through a structured checklist for each engagement, pulling from QuickBooks Online, prior-period templates, and payroll data, and surfaces items that need a human decision. Firms using this approach are seeing real time savings. “It is eliminating or reducing the amount of time we spend on a month-end close by 50% on some clients,” says Tyler Otto, President and Owner of Specialized Accounting.

For scaling quality: Purpose-built accounting platforms let you codify how your firm does things. Your chart of accounts rules, your workpaper format, your close checklist become the instructions the AI follows for every engagement. You can define those same standard operating procedures at the client level too, so the AI applies the specific way you work with each account, not just your firm’s general standards. When a new client onboards, that context is already in place. When a staff member leaves, the institutional knowledge stays.

How the review process works

The most common concern about AI in accounting is accuracy. It’s the right concern. The answer isn’t “trust it.” It’s “verify it the way you verify any work.”

When an agent completes a task, it shows its reasoning. After a bank reconciliation, you see each transaction it matched and any items it flagged as unmatched or ambiguous. After a payroll journal entry is drafted, you see the source payroll data it pulled from, the accounts it used, and the period it applied to. Nothing is hidden behind the result. You approve, override, or reject before anything posts to the general ledger. When you override a decision, the agent learns from it.

The audit trail exists at the task level, not just the ledger level. Any decision the AI made can be traced back to the source data. That’s what makes this deployable in an audit-sensitive environment: not because the AI is infallible, but because the review process stays intact and every action is accountable.

An agent flags three transactions it couldn’t match on a client’s bank reconciliation. You open the session, see what it pulled and why it stopped, make the call, and move on. That’s the whole interaction.

That’s the design: the AI handles the execution, your team handles what it flags, and nothing moves to the ledger without your sign-off. The audit trail is intact. The judgment stays with you.

Types of AI tools for accounting firms

AI tools for accounting firms have multiplied fast. Understanding the differences helps you match the right tool to what you’re actually trying to solve.

General-purpose AI (ChatGPT, Claude, Gemini): These tools are genuinely useful for drafting, research, and one-off analysis, and a lot of firms start here. If you need to explain a technical accounting concept to a client, draft a memo, or answer a one-off question, they’re well-suited to the task. For firms that aren’t yet ready to adopt a full platform, starting with general-purpose AI for select tasks is a reasonable first step.

Traditional software with AI features: Many practice management tools and accounting platforms have added AI-assisted features: suggested codings, anomaly flags, automated reminders. Because they live inside the tools you’re already using, the adoption lift is low. The tradeoff is that most of them assist with work rather than execute it. They surface information and make suggestions. A human still does the task.

Purpose-built AI accounting platforms: This category is designed to execute accounting workflows directly: native ERP integration, structured and auditable outputs, and cross-client management. The AI does the work, with your review before anything posts. This is where the biggest efficiency gains are, and where the evaluation criteria get more specific, which is what the next section covers.

Choose the right AI platform for accounting work

Not all tools in the purpose-built category deliver equally. These are some of the questions you can considering while evaluating your stack:

Does it connect natively to your clients’ systems? If every workflow starts with a manual export from your clients' ERP or accounting system, you haven’t automated anything meaningful. Look for direct read/write access to the ledger, not a tool that works alongside it.

Can you see how it reached each output? You need to trace any AI decision back to the source data and show that reasoning to a client or auditor. A result without a rationale isn’t audit-ready.

Does it work across your whole book? A platform that requires rebuilding the workflow per client doesn’t scale. The efficiency in accounting AI compounds when you define your process once and apply it to every engagement.

Can it encode how your firm does things? Your standards are your IP. An AI platform that can’t learn and apply your firm’s rules will produce output that still needs heavy review, which defeats the purpose.

Has it been evaluated against real accounting tasks? General-purpose AI benchmarks test broad reasoning, not general ledger coding accuracy or close checklist completion. Ramp Stack, for example, was benchmarked on 200+ realistic close scenarios built and graded by working accountants, and outperformed every general-purpose model tested. That kind of evaluation is what purpose-built actually means.

How does it handle client data? Accounting firms work with sensitive financial information. Before adopting any platform, understand where client data is stored, how it’s used, and what access controls are in place. Purpose-built accounting AI is designed with data handling appropriate for professional accounting work. For general-purpose tools, check whether pasting client data into a chat interface aligns with your firm’s data policies and client agreements.

Try Stack for free to see how a purpose-built platform performs on your firm’s actual workflows.