Outliers, Exceptions, and the complexity of AI in Accounting
A founder’s perspective
During month-end close, how often do you find yourself thinking, “Do I trust this number or do I need to dig more?”
As someone who spends a lot of time learning from, researching, and working closely with accountants while building a tool meant to support them, one thing stands out to me more than anything else. The issue isn’t the workload. It’s how quickly the work changes once an outlier appears.
On paper, month-end close feels straightforward. Teams record transactions throughout the month. At close, they reconcile balances, review variances, and prepare financials. Most teams rely on ERPs, a few subledgers, reporting tools, and spreadsheets to connect everything.
That structure holds up fairly well as long as everything behaves the way it’s expected to.
During close, the focus shifts. Accountants stop recording activity and start validating it. They compare balances across systems, review changes from the prior period, and check whether the numbers match what they expect to see.
This is usually where things slow down…
Balances stop tying back to the subledgers.
A variance appears that wasn’t there the previous month.
A transaction shows up in the wrong period.
Sometimes one of these issues shows up. Sometimes all of them do. It depends on the month.
When that happens, the work stops being about completing tasks and starts being about understanding what’s actually going on. Close turns into investigation. Accountants pull data from multiple systems, retrace entries, and piece together why the numbers look the way they do.
This part of the workflow matters because accountants don’t just aim to close the books. They need to explain the numbers and stand behind them especially when reviews or audits come into play.
Context becomes critical at this point. Without understanding how and why a number was produced, trust breaks down. This is also the point in the workflow where tools including AI either support that understanding or get in the way.
Watching this play out over time has changed how I think about building in the accounting space.
Before I think about automation or efficiency, I think about where accounting teams actually spend their time during close. From what I’ve seen, they don’t spend it on routine steps. They spend it on exceptions, the moments when something doesn’t make sense and they need to rebuild trust in the numbers.
Those moments force teams to slow down and drive investigations into all items that require explanation.
If you’re also working at the intersection of AI and accounting, I’d love to hear your perspective. I share more of these thoughts regularly, feel free to follow along.