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Understanding the Accounting Workflow in Practice

What I’ve learned from spending time in this space is that month-end close rarely slows down because of incompetence. The issue is rarely the quality of the work itself. The issue is everything that happens around it.

In practice, the work of close is spread out. Documents live in folders or shared drives. Data lives in systems. Analysis lives in spreadsheets. Context lives in people’s heads. Because of that separation, even when the work is technically done, teams still have to pause and reconnect everything before they can move forward. They search for supporting documents, confirm which version is correct, and retrace steps to explain how a number was derived. 

Over time, this structure introduces friction that’s easy to overlook at the moment but heavy in aggregate. A document exists, but not where the analysis is happening. An approval depends on an email thread rather than the work itself. A number looks right, but someone asks where it came from, so the team goes back to prove it. Each pause feels reasonable on its own, but together they turn close into a process of stopping, searching, and reconnecting.

This is where close begins to feel slow, even when everyone is working efficiently.

What’s striking is how closely this mirrors what researchers and practitioners have been pointing out for years. Across close process studies, controller surveys, and advisory articles, the same conclusion keeps surfacing: delays during close are driven less by accounting complexity and more by fragmented workflows. Time isn’t lost doing the work. It’s lost validating it across disconnected places.

At a certain point, close stops being about review and starts becoming an investigation. Teams spend more time answering questions about where numbers came from than thinking about what those numbers mean. That shift is draining, not because the work is difficult, but because the system doesn’t make the context easy to follow.

The Birth of Rima

As we spent more time learning directly from accounting teams, this pattern became impossible to ignore. Different companies, different sizes, same core issue. Teams weren’t asking for more tools or more dashboards. They were asking for fewer places to look and a clearer connection between source data, supporting documents, and final analysis.

That understanding is what led us to build RIMA.

RIMA is designed to remove that bottleneck by centralizing the work itself. Uploaded documents and analysis live together in one place, so context doesn’t have to be reconstructed after the fact. Every value can be traced back to its source without jumping between tools or recreating explanations. The goal was never to push teams to move faster for the sake of speed, but to make the workflow easier to follow.

When everything lives together, the experience of close starts to change. Approvals move more smoothly because the information needed to review is already there. Rework drops because teams aren’t second-guessing the numbers. Close feels less like investigation and more like confirmation.

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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.

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Across industries, organizations are experimenting with systems that can reason, act, and execute tasks with minimal human input. These tools promise speed, efficiency, and scale in ways traditional software never could.

Today, the accounting industry is now part of this conversation. But as AI agents move from experimentation to real accounting workflows, organizations should consider slowing down and ask themselves this question:

Is my organization adopting AI because it’s powerful or because we understand what we are trusting?

A robust system with limitations

Most AI systems are probabilistic by design; that is, they predict outcomes based on patterns in data rather than fixed, deterministic rules. Pattern recognition is what makes the system effective at tasks such as drafting text, summarizing information, and identifying trends across large datasets. 

However, this introduces a limitation often overlooked in the industry, which is: 

AI doesn’t usually “understand” the results because it estimates most of the time based on patterns. 

In many industries, this is acceptable, but in accounting, it creates significant risk down the line.

AI in accounting workflows

When I think about how accounting actually works, I think about constraints.

Like, for example, reconciliation doesn’t happen once. It’s recurring. That is, it can be tomorrow, next month, or even during an audit.

And the data isn’t usually the same every time. So there’s no real pattern recognition there. When it comes to data analysis, you really have to understand the context of what you’re working on to get to a solid reconciliation.

That structure leaves very little room for estimation.

Which is why having a proper understanding of the software your team is using is really important. 

Three questions to ask before adopting AI in accounting

Rather than starting with features or demos, we believe organizations should start with three questions.

1. Can I audit the output from this tool?

When an AI tool gives you an answer, the first question shouldn’t be “is this correct?” but “can I explain this to someone else?”

For example, if an auditor asks where a number came from, “Can I walk them through it?”

Let's go even further: “Can I point to the data it used, the steps it took, and why it landed on that result?”

In accounting, that distinction matters because a system that can explain its output helps you build trust.

2. Does the tool’s system understand my context?

By context, I mean this: does the system understand what I’m actually trying to solve?

And I think about that at a very granular level. When the system gives me a result, do I understand how the system processed the data before it gave me an answer or solved the problem I was working on?

That level of visibility matters. It means that when you’re building a presentation, going through an audit, or sharing results with someone else, you’re not just showing an output but also the process behind it. Someone can step in, review the steps, and see precisely how the conclusion was reached.

And that’s what builds trust. 

3. Are the results from this tool accurate?

I think we have to remember that AI is very good at sounding confident when it gives you an output.

When a system like this gives you an answer, it’s easy to assume it’s correct, especially if it matches what you were already expecting. But accuracy isn’t just about the answer. It’s about the process that led to it.

A system can give you the correct result once. The real question is whether the process behind that result is accurate. Can that same process be repeated? Does it work consistently? Can you trust it across different scenarios?

AI agents are here to stay

As someone working at the intersection of AI and accounting, I’ve learned that adoption alone isn’t the goal. The real work is figuring out how to make these tools fit the realities of accounting.

That’s why I keep coming back to the same three questions:

Can I audit the output?

Does the system understand the context?

And are the results accurate?

If we get this right, AI agents can be a real game-changer for the accounting industry, not by replacing human judgment, but by strengthening the workflow process. The agents will make work more traceable, more explainable, and easier to trust.

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