One thing I keep noticing: compared to programming, accounting often looks like the more automatable problem:
It’s rule-based Double entry, charts of accounts, tax rules, materiality thresholds. For most day-to-day transactions you’re not inventing new logic, you’re applying existing rules.
It’s verifiable The books either balance or they don’t. Ledgers either reconcile or they don’t. There’s almost always a “ground truth” to compare against (bank feeds, statements, prior periods).
It’s boring and repetitive Same vendors, same categories, same patterns every month. Humans hate this work. Software loves it.
With accounting, at least at the small-business level, most of the work feels like:
normalize data from banks / cards / invoices
apply deterministic or configurable rules
surface exceptions for human review
run consistency checks and reports
The truly hard parts (tax strategy, edge cases, messy history, talking to authorities) are a smaller fraction of the total hours but require humans. The grind is in the repetitive, rule-based stuff.
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 want complex custom rules, and integration with other systems, you're looking at something like SAP.
can A.I find some edge case deductions. again people out of their depth about certain fields making authoritative statements.
There’s a lot of subjectivity in how GAAP is applied and interpreted - creating accruals, deciding when revenue should be recorded, blah blah.
And it is a very poor fit for moderm LLM based AI. Because accuracy. No mistakes or hallucinations allowed.
I’ve built some software[0] that analyses general ledgers and uses LLMs to call out any compliance issues by looking at transaction and account descriptions.
Is it perfect, nope. But it’s a hell of a lot better than sifting through thousands of transactions manually which accountants do and get wrong all the time.
[0] - https://ledgeroptic.com
I still wonder why humans getting things wrong is a problem, but LLMs getting more things more wrong more often than humans never is. At the very least you'll need a human accountant around to verify the LLM. Or I guess you could just practice "vibe accountancy" and hope things work out but that seems like a worse idea than a trained human professional. But I'm probably just a Luddite.
Also, I am admittedly not an accountant, but I don't think they manually sift through every transaction to verify compliance issues in every single case. That probably isn't how that works.
Some people hate humanity so much that they cannot wait to replace us all with AI so they never have to interact with another human ever again
That's honestly the only reason I can think that they are so biased toward AI
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.