Too Much AI, Too Soon: Are Finance Teams Setting Themselves Up for Failure?

Finance teams are under immense pressure to adopt AI, mostly driven by boards seeking efficiency gains, CFOs chasing faster closes, and vendors promising unprecedented automation. 

The prevailing assumption is that more AI will naturally lead to better results, since it can efficiently analyze data within seconds. But speed alone is not the only factor that ensures success, especially if the underlying process is flawed or underdeveloped. 

As organizations rush to embed AI into financial workflows, they risk overlooking a fundamental truth: AI is only as effective as the systems it automates. 

It can accelerate tasks, but it cannot compensate for weak controls, fragmented data, or inconsistent processes.

Even so, the technology is gaining prominence within larger enterprises, and more and more companies are hoping it will provide immediate results. 

For 2025, the share of C-suite executives identifying AI conduct risks as a top material concern has increased from 16% over the past three years to 56% looking ahead, making it the leading non-financial risk. At the same time, major business conduct incidents have risen by 55% between 2023 and 2025, with an average cost of $14 million per incident, leading to reactive rather than proactive investments in governance and data quality. 

In no other area is the use of AI more critical than in finance, where month-end closings rely on controls, reconciliation, and verification. AI may increase the velocity of that process, but without a solid foundation, it can just as easily magnify existing weaknesses as eliminate them. 

The question, then, is whether finance has done enough groundwork to be ready for a massive AI takeover. 

AI Is a Multiplier, Not a Fix

While undoubtedly AI is a great tool for making tasks easier to tackle, a common misconception is that AI can fix all broken processes, while in reality, all it does is amplify whatever already exists. 

If the underlying data is inconsistent, controls are weak, or reconciliation practices are flawed, AI will simply execute those deficiencies faster and at greater scale. The old principle of “garbage in, garbage out” hasn’t disappeared; it now operates at machine speed.

None of this means that AI is useless; on the contrary, it automates labor-intensive tasks such as data extraction and presentation, freeing finance professionals to focus on higher-value work. It can allow them to invest more in critical thinking, drawing conclusions, and ensuring oversight over what machines are executing. 

However, over-reliance on AI can create vulnerabilities if systems fail or produce unexpected results. If an AI model used for market predictions malfunctions during a crisis, for instance, it could amplify losses or destabilize markets. 

The Basel Committee on Banking Supervision and other bodies are particularly concerned about systemic risks, where widespread AI failures could threaten financial stability. 

Rather than treating AI as a cure for operational inefficiencies, organizations should focus on building the financial infrastructure that allows automation to work reliably. That means standardized reconciliation processes, stronger internal controls, better data governance, and greater visibility into the close process.

“There is a common misconception that AI is a solution to process problems. It isn’t. It’s an amplifier of whatever’s already there. Finance leaders often come to AI looking for relief from manual work, slow closes, or reporting that takes too long,” said Shagun Malhotra, CEO of automated month-end close software SkyStem, while in conversation with Unite AI. 

But if the underlying process is flawed, she added, AI doesn’t fix the flaw. Rather, it runs it faster and at a greater scale. “The misconception is that technology is doing diagnostic work when it isn’t. That part still needs a professional behind it.” 

SkyStem focuses mainly on helping organizations whose financial closing platforms standardize reconciliations, automate workflows, and strengthen internal controls before layering AI onto critical accounting processes. Instead of  replacing disciplined financial operations with AI, the company builds reliable operational foundations so that automation can actually provide accurate results without compromising data. 

“AI creates enormous value in volume, consistency, and in speed for tasks that are well-defined […] But the places where humans need to stay in control are exactly where experience matters most: risk intuition, pattern recognition under ambiguity, and most importantly, decision-making under pressure,” Malhotra added.  

Foundation Before Automation

Finance has very little room for error. Accurate reporting, reconciliations, regulatory compliance, and auditability are essential; AI should enhance and simplify these processes, not compromise them by accelerating existing weaknesses within the system. 

Kognia Labs, an AI consulting and software development company, takes a similar view. It helps organizations implement systems that detect flaws or incoherences within reports before they become so costly. 

Prior to AI going into a workflow where a customer is waiting for the answer, three things have to be true in the institution: connection between systems, reaching the information that is not filed as a document, and being able to verify where an answer came from, according to Alexandra Ollé, strategy director at Kognia Labs. 

“The information an advisor needs is spread across different systems and different formats, and those systems generally do not talk to each other. That is what makes answers slow and inconsistent in the first place,” Ollé told Unite AI. “AI does not resolve that by being added on top of it.” 

That gap, between accelerating a process and actually improving it, is where the financial stakes get concrete: when funds are on the line, reliability isn’t optional, and mismanaging an AI system carries real legal exposure. 

“AI improves a process when a company uses it to change how the work is actually done. That usually begins with understanding why the process fails, which is something most institutions cannot see clearly because the evidence sits in thousands of customer conversations that no team has the time to go through,” Ollé stressed.   

The Path Forward: An AI-Human Bridge

Before relying on AI to automate critical financial workflows, organizations must first ensure their processes are ready for it. That starts with assessing process maturity, addressing bottlenecks and data quality issues, and introducing AI incrementally in lower-risk areas before expanding to more complex financial operations. 

Building a strong operational foundation first, in sum, allows automation to improve efficiency without compromising accuracy or oversight. And this is the part where humans cannot let their guard down; there are things AI simply cannot do on its own. 

There are already some companies whose focus over AI implementation comes from a “humans first” approach that ensures there is human oversight and credibility over automated systems. Solvd, for example, is a California-based software engineering company that develops, implements, and manages AI systems for enterprises, specialized in building complex workflows that are then refined by humans before scaling through automation. 

“Companies that take AI seriously will learn faster, gather richer customer insights, operate more efficiently, and personalize more effectively. Over time, keeping pace becomes harder because competitors are not just deploying better tools; they’re building better data, processes, and institutional learning,” Skylar Roebuck, chief technology officer at Solvd, told Unite AI. 

An organization is truly AI-ready when its executive team is aligned not only on the importance of AI, but also on how it will create a meaningful competitive advantage for the business. 

“Human oversight should not be viewed simply as someone approving every AI action. That approach does not scale and often creates the illusion of safety without delivering meaningful control,” Roebuck stressed, adding that the system should be able to both operate independently within clear boundaries and recognize uncertainty for escalation when judgement is required. 

Bottom line, AI has the potential to transform finance, but it cannot replace the operational discipline and human judgment that financial decision-making requires. The goal here isn’t to accelerate the close, but to accelerate confidence in it. 

The truly successful organizations won’t be those that automate first, but those that build the right foundation, and keep people at the center of the processes that matter most.