By Hugh O’Neil, EMEA Lead, Solutions Consulting, FloQast
Autonomous accounting is the next major evolutionary step in the transformation of finance. But speedy AI-powered automation means nothing without trust. Why? Because if a number is wrong, people stop trusting everything around it.
The simple reality is that, despite all the advancements made in recent years, every piece of work we produce comes down to personal accountability. As such, every tool or piece of technology we use – by extension – has to meet those high standards.
While AI can afford to be “mostly right” in other parts of the business, in finance, it’s different. It has to be on the money, because if the balance sheet isn’t right, nothing else really matters. Finance leaders need systems that produce repeatable and dependable outcomes.
After all, accountants are naturally sceptical people. Our profession is built around challenging assumptions, validating information and proving that numbers stand up to scrutiny.
Why trust is critical to autonomous accounting
But again, there is a problem. Most finance teams are not operating in a clean, fully integrated AI environment. They are working across fragmented layers of enterprise resource planning (ERP) systems, spreadsheets, automation platforms and emerging AI tools – all producing outputs, but not always producing trust.
I was at an accountancy association dinner recently and somebody asked, “Isn’t autonomous AI just the same thing as robotic process automation (RPA)?”
At first glance, it may seem like that’s the case. Except that autonomous accounting is more than that. It’s the evolution of rules-based automation.
Auditability and governance are key
AI introduces something completely different: the ability to assist with the judgement calls, reconciliations and exceptions at the edge of the balance sheet that traditional automation systems have historically struggled with.
Traditional deterministic systems are built around predictability. AI introduces interpretation and pattern recognition. Autonomous accounting is really about combining those two worlds.
Early AI models in finance largely relied on “human in the loop” processes, where humans still checked every output manually.
Increasingly, however, finance teams are moving toward “human on the loop” models. Rather than reviewing every individual task, accountants oversee the wider process, manage exceptions and ensure controls are functioning correctly.
In other words, the role of the accountant is gradually shifting from manual execution toward supervision, governance and strategic oversight. But this shift only works if finance teams have confidence in the outputs.
Autonomous accounting still depends on human judgement
While AI may be capable of handling increasingly complex finance tasks, accountability still sits with humans. No auditor is going to accept “the computer told me it was right” as an excuse for inaccurate reporting.
That creates a difficult balancing act for finance teams. Humans make mistakes constantly, yet AI systems are often held to far higher standards. If a junior employee gets six things out of ten correct, managers accept that review and correction are part of the process.
But if a computer gets eight things right out of ten, attention immediately shifts to the two mistakes.
That’s why the next generation of finance AI platforms is being built around auditability, governance and control frameworks from the outset. When you have platforms that are designed by accountants – for accountants – qualities such as auditability, review, oversight, structure and the segregation of duties are built from the ground up.
But let’s not get ahead of ourselves. AI-powered autonomous accounting is still relatively new, and people are still working out how best to use it. What’s more, none of this happens overnight. Many finance teams are still operating in a constant cycle of reporting deadlines and reactive work, leaving little time to rethink processes or focus on transformation properly.
This is important because autonomous accounting won’t solve any problem if the underlying processes are broken. So what practical steps should finance leaders take as they begin exploring AI adoption?
Why finance teams cannot automate broken processes
First, do not start with the biggest and most complex problem you can find. One of the biggest mistakes organisations make is expecting AI to immediately solve processes they have not properly understood themselves.
Instead, businesses should start smaller, break work into manageable chunks and bring people along the journey through clear communication and gradual change.
Second, do not expect perfection immediately. AI adoption is a journey, not a switch that gets turned on overnight. The goal early on should be progress, learning and improvement rather than flawless automation from day one.
Third, and perhaps most importantly, question the process before automating it. Many organisations accumulate layers of controls, approvals and workflows over time that nobody ever stops to challenge. In some cases, the right answer is not automating the process at all, but removing it entirely.
So where does that leave us? Autonomous accounting is not about removing humans from finance. If anything, it places greater emphasis on oversight, governance and professional judgement. The technology may be evolving rapidly, but the principles underpinning accounting remain the same: trust, verification and accountability.
The difference is that, increasingly, accountants may spend less time producing numbers and more time ensuring the systems behind them can be trusted.