AI in Accounting: How to Leverage It Without Losing Control

The following is based on the article written by Matthew Binshtok at Vercel, adapted for accounting teams using AI.

AI accounting tools are improving incredibly fast. They can categorize transactions, generate reconciliations, and propose journal entries in seconds. Internal tools and assistants like Claude Cowork make accounting teams dramatically more productive.

In the hands of disciplined accountants, this is a productivity multiplier. Without rigorous judgment, it becomes a highly efficient way to ship bad assumptions directly into your financial statements.

Our Principles for Using AI in Accounting

As we integrate AI deeper into accounting workflows, we operate under a simple set of principles:

There is a fundamental difference between using AI to accelerate your work and trusting AI to own the outcome. Leveraging means you understand why the AI categorized a transaction, how adjustments affect financial statements, and what assumptions were made. Relying means assuming the output is correct because it looks right.

1. Leverage AI. Don’t rely on it.
   There is a fundamental difference between using AI to accelerate your work and trusting AI to own the outcome. Leveraging means you understand why the AI categorized a transaction, how adjustments affect financial statements, and what assumptions were made. Relying means assuming the output is correct because it looks right.
2. The accountant owns the result. 
   AI can do the work. But you own the numbers. Every AI-generated output must pass through human review before approval. Someone must always be responsible for the final figures — AI outputs are not self-approving, no matter how clean they look.
3. Before you approve, ask yourself:
   Do I understand what this entry or reconciliation actually represents?
   How could this be wrong, and what would the impact be on the financial statements?
   Would I be comfortable explaining and defending this during an audit?
   
   Yes to all → Approve it.
   Any no → You have more work to do.
   

False Confidence

AI-generated outputs are extremely persuasive. They come with well-formatted reports, clean explanations, categorized transactions, and reasonable-looking assumptions. On the surface, it can look like the work of an experienced accountant.

But AI doesn’t fully understand your company’s internal policies, the context behind unusual transactions, tax implications tied to your jurisdiction, or how a decision today affects reporting next quarter.

The gap between “this looks right” and “this is actually correct” has always existed. AI simply makes that gap wider.

Leveraging vs. relying

There is a fundamental difference between leveraging AI and relying on it.

Relying on AI

Relying on AI means assuming that if the system categorized transactions and produced a report, the job is done. No one builds a mental model of what happened. The result is accounting work that looks complete but contains hidden assumptions nobody actually verified.

Leveraging AI

Leveraging AI means using it to accelerate the mechanical parts of the work while maintaining full ownership of the outcome. You understand why the AI categorized a transaction a certain way, how adjustments affect financial statements, what assumptions were made, and what risks might exist in the output. AI can do the work. But the accountant owns the result.

The litmus test is simple: would you be comfortable explaining this output during an audit?

Guarding production

The goal isn’t to stop using AI. The productivity gains are real and will only increase.

When AI can generate large volumes of work, the limiting factor is no longer execution — it’s human judgment and accountability.

Accounting teams should design processes that assume AI will be used and include guardrails like these:

• Verification workflows — Every AI-generated output should pass through human review before approval.
• Explainable outputs — AI systems should provide reasoning for categorizations, adjustments, or recommendations.
• Incremental automation — Automate well-understood processes first (expense categorization, draft reports)

before automating sensitive decisions.

• Clear ownership — Someone must always be responsible for the final numbers.
  

What we’re Investing in

We aren’t just theorizing. Our development team is actively building these guardrails into the AI agents our accounting teams use every day.

At the same time, we’re investing heavily in training and awareness across our accounting and bookkeeping teams. Responsible AI adoption isn’t just about better tools — it’s about better judgment and shared understanding of how those tools should be used.



New AI agents and workflows are introduced gradually, often starting with small groups before broader rollout. This allows us to learn, refine controls, and ensure the systems behave as expected before scaling their use.

Leverage Agents in Accounting. Own the Risk.

Our bar: leverage AI, don’t rely on it.

Accounting work that was incorrect used to look incorrect. That’s not always the case anymore. AI tools are only going to get more powerful. Transaction categorizations will look cleaner. Reconciliations will sound more convincing. Journal entries will appear perfectly structured.

And the temptation to trust the output without questioning it will grow.

The accountants who thrive won’t be the ones generating the most AI-assisted work. They’ll be the ones who maintain ruthless judgment over the numbers they approve.

Before approving AI-generated accounting work, ask yourself:

• Do I understand what this entry or reconciliation actually represents?
• How could this be wrong, and what would the impact be on the financial statements?
• Would I be comfortable explaining and defending this during an audit?

If the answer is yes, you’re leveraging AI. Approve it.

If the answer is no, you have more work to do.

Frequently Asked Questions