Lots of this is already being done (and using computers to check books balanced predates latest gen AI by some decades). But of course what accountants actually get paid for is tax strategy, edge cases, messy history and talking to authorities (or at least having their stamp of approval on it if the authorities come calling). There's plenty of market for writing software to automate aspects of invoice reconciliation or monitoring accounts for exceptions, but competition already exists...

I’m an accountant (CPA, CMA, Big 4), and while there’s some truth in what you’re saying, you’re significantly underestimating the complexity of applying GAAP, or even tax law. What you’re describing is really bookkeeping, not accounting, and there are plenty of tools that already automate that.

There’s a lot of subjectivity in how GAAP is applied and interpreted - creating accruals, deciding when revenue should be recorded, blah blah.

This is roughly what my startup is doing, automating financials.

We didn't pick this because it was super technical, but because the financial team is the closest team to the CEO which is both overstaffed and overworked at the same time - you have 3-4 days of crunch time for which you retain 6 people to get it done fast.

This was the org which had extremely methodical smart people who constantly told us "We'll buy anything which means I'm not editing spreadsheets during my kids gymnastics class".

The trouble is that the UI that each customer wants has zero overlap with the other, if we actually added a drop-down for each special thing one person wanted, this would look like a cockpit & no new customer would be able to do anything with it.

The AI bit is really making the required interface complexity invisible (but also hard to discover).

In a world where OpenAI is Intel and Anthropic is AMD, we're working on a new Excel.

However, to build something you need to build a high quality message passing co-operating multi-tasking AI kernel & sort of optimize your L1 caches ("context") well.

If you look through the documentation for something like Quickbooks, most of what you're discussing is already there.

If you want complex custom rules, and integration with other systems, you're looking at something like SAP.

It is already quite automated to my knowledge.

And it is a very poor fit for moderm LLM based AI. Because accuracy. No mistakes or hallucinations allowed.

I've been doing this for 20+ years, not with AI but just with code and taking time to understand the important parts of the processes. I still see many companies and especially educational institutions spending way too much manpower on payroll and accounts payable in particular. This is often because of unnecessary bespoke processes that people in the organization believe are necessary and are afraid to change.

My friend works in corporate accounting. He said his company is outsourcing a bunch of accounting functions and laying people off (I don't know the specifics). They are outsourcing to India and the outsourced company is already using AI for stuff compared to his company that isn't. He thinks many companies are going to outsource.

As others have said, the actual accounting part of accounting is pretty well automated already. Spend some time with LLM's, though, and you'll quickly realize their non-determinism is a huge hindrance for this kind of work.

I should say upfront I don’t hate humans or CPAs.

What I’m working on is the opposite of that. I want to free humans from boring, repetitive finance work so they can use their time for higher-value and more creative things.

While building an “AI CFO” for small businesses (LayerNext), I’ve learned a few things that changed how I see bookkeeping:

Most of bookkeeping is repetitive and under-optimized. Everyone says “90% of the work is repetitive,” but we still hire bookkeepers and bookkeeping firms. Most small businesses I talk to pay around $300–$800 per month just for bookkeeping. Even after paying that, I really doubt every single transaction is recorded in the most tax-optimized way. There are hundreds of transactions, constant government tax rule changes, and limited time.

Current automation is stuck at rules you manually define. Tools like QuickBooks can categorize transactions based on rules you create. That’s it. As soon as something new comes up, you still need a human to either, create a new rule, or manually enter and categorize it.

And even when you hire a human bookkeeper, you still end up doing half the work anyway: sending receipts, answering clarification emails, chasing missing information.

Invoice and expense capture can be 100% automated, even with edge cases In practice, invoice and expense capture is the easy part. With decent models, you can get 100% accurate capture from receipts, PDFs, emails, etc. Edge cases are solvable with better parsing and validation, not more humans.

Reconciliation is the hard part, but reasoning models are getting very good. This is where things get tricky: - multiple invoices paid in a single payment - partial payments - refunds, chargebacks, etc.

For example, imagine a consulting company issuing several invoices to the same customer and receiving one lump-sum payment. We’ve had success using deep research like reasoning to match payments to invoices and handle those cases automatically.

AI can sometimes care more about details than a human. One moment that surprised me.We had a credit card transaction with no receipt.

The question was whether it should be classified as “office expense” or “meals and entertainment” (in Canada these have different tax treatments). When I checked trace of the agent, it looked up the vendor online to understand what they actually sell, checked CRA tax rules and then picked the GL account that maximized the tax benefit for the company.

I’m not sure many manual bookkeepers consistently do that level of research when they’re trying to reconcile 500+ transactions and half the receipts are missing.

My goal is to build a fully automated financial assistant that can close the books without a CPA or bookkeeper, with ~99% accuracy across all transactions, and with the explicit goal of maximizing tax benefits within the rules.

Other outcome is accurate rea-time books can generate good insights to grow the business.

So I don’t see a good reason why small businesses should pay hundreds of dollars per month for humans to do mechanical work that machines can now do, often more consistently and with better attention to tax details.

Curious how others see this, especially CPAs and engineers who have built accounting tools. Is there a fundamental reason we need humans in the loop for the majority of small business bookkeeping, or is it mostly inertia and habit?

Most women don't think AI is sexy.

The hard part of accounting, and the part that takes all the time, is the reconciling... which can't be automated.

Everything else has been mostly automated since the 90s.

Accounting rules are also not as discrete as programming. There is a lot of discretion. Accounting is basically law with numbers and is corresponding just as difficult/ impossible for LLMs to master.