Businesses everywhere are racing to integrate artificial intelligence (AI) into their operations in the hope of slashing costs while at the same time becoming more efficient and boosting profits. But in the rush to modernise, a growing number are finding that quick wins often have hidden risks, leaving them exposed to all manner of penalties.
Automating processes without proper planning or oversight can, experts say, lead to gaps in compliance, erode financial controls and ultimately end up costing a lot more. It is hardly surprising, therefore, that companies are starting to more carefully scrutinise how AI can be adopted in tandem with finance and risk, instead of viewing the rapidly developing technology as a standalone upgrade.
For those that take this measured approach, the advantages are clear, say the experts: improved performance across the board while ensuring costs don’t spiral and control and compliance are adequately maintained.
AI as a ‘Magic Bullet’
Amid the ongoing hype over AI, believing that new technologies can solve all a company’s woes is a fallacy, say those in the know. One expert in this area is Suzy Jackson, a former executive at tech consulting firm Accenture who now specialises in advising firms on “digital health”.
“I’ve observed many leadership teams rushing to install expensive automation tools that lack clear methods of tracking actual earnings,” she says. “They use software as a magic bullet to solve their profit problems rather than a tool that requires a rigorous financial plan.”
As a result, says Jackson, tech that’s supposed to help companies instead becomes a “giant stain on bank accounts”.
That’s because “[p]oorly planned investments result in hidden costs that gutted the bottom line. Firms that do not connect AI with engagement objectives end up with costly tools that no one uses”.
Jackson had a client who encountered problems with the rollout of software that led to glitches. Instead of taking them seriously and doing something about the issues, the client carried on regardless, to their enormous cost.
“They eventually lost millions in revenue because systems weren’t able to handle new data,” she said. “Firms should stop following fad trends and begin to utilise stringent return milestones for each new pilot.”
Going too Fast
As with anything in business, executives should take time to consider and appraise any new addition to systems, especially high-profile tech like AI. But many, observers say, are lured by the headlines, thinking they stand to vastly gain by rolling it out before thinking.
Sira Masetti helps companies transform their culture, with her company Bias for Growth. She has seen firsthand the issues tech can cause.
“A particularly risky area for businesses today lies in aggressive AI adoption without robust risk assessment framework,” she says. “Leaders often prioritise speed over scrutiny, deploying tools for financial forecasting or compliance monitoring that lack proper governance.”
That can lead to regulatory fines, data breaches that can put customers at risk and damage company reputation and also operational disruption, she says. The effect of all that is to “quietly erode profitability and investor confidence over time”.
In Masetti’s experience of auditing global operations, companies that start using AI without first examining the risks “see margins shrink as unexpected compliance costs mount and innovation stalls.
“The bottom line takes a hit when unvetted tech creates more problems than it solves, diverting resources from core growth.”
She recommends that organisations “cultivate an effective review culture where every AI initiative faces a mandatory risk audit before launch.
“Tie approvals to clear financial thresholds and cross-departmental sign-off, ensuring innovation serves stability rather than undermining it. This balanced path turns potential pitfalls into sustainable advantages.”
A Lack of Human Oversight
Another area that can negatively impact a firm’s finances and lead to risks is using AI without adequate safeguards in place, especially if humans are removed from the equation, according to business leaders. The trap lies in thinking the tech is capable of doing everything on its own — intelligent enough to get the job done without human intervention or management.
“Businesses are making particularly risky choices by relying on AI systems to guide financial activity without clearly redefining who is responsible to check and approve each step of the way,” says Yad Senapathy, founder and CEO of the Project Management Training Institute in Dallas, Texas.
“When teams plug AI into their budgets, spending reviews or approval flows, there is often an unspoken belief that the tool is ‘smart enough,’ so human review needs to be lighter even though no one officially agreed to reduce it,” he says.
Senapathy, who advises executives on AI-enabled programmes as well as governance and compliance, warns that when problems arise after AI is rolled out and effectively there is no one in charge, management doesn’t know who to blame or how to swiftly resolve the issue.
“In my experience, that quiet slide in oversight is what is causing real exposure, because if something goes wrong you cannot explain who owned the decision,” he says.
Senapathy worked with a medium-sized enterprise that was using AI to determine suppliers that could be combined and to decide on the timing of discounts for early payments. The company was aiming to boost their bottom line but instead was hit with lower profits.
“The intention was sound,” said Senapathy, “since they wanted better supplier relationships and better use of cash.”
He said, however, that “nobody was given the job of comparing each recommendation against the operational risk and the total contract cost.
“When finance later reviewed the results, they saw that some deals helped while others increased overall cost and constrained flexibility. So the final gain was less than what was promised and so the margins tightened.”
How can firms get around this? Senapathy recommends they “consider every new AI tool that interferes with money as a change to internal controls first, rather than simply as a new software.
“Before switching the system on, leaders should consider and write down who reviews the outputs, what types of results always require a human decision and what records leaders will keep to explain decisions later on.”
And run a trial with AI before implementing it, “with people still making the final call”, so you can address any issues and protect your finances, he suggests.
Legal and Ethical Issues with AI Use
Don’t forget about any legal implications of using new tech, or you could end up in more trouble than it’s worth. That’s the message of Robert Tsigler, founder and lead attorney of the Law Offices of Robert Tsigler in New York.
“Companies assume that their automated compliance tools handle all the nuances of the law without any manual intervention,” says Tsigler. “Many companies use algorithms to screen 90% of contracts for regulatory red flags. Automated bias in these systems can lead to a failure to identify wire fraud signs.”
Tsigler points to examples of companies that lost out after implementing AI in their systems. One, he says, was hit with $750,000 in legal fees they should not have had to pay but the AI the firm was using misclassified a dozen vital documents and so there was no choice.
“These errors resulted in an increase of 300% in discovery costs during a federal audit,” said Tsigler. “In fact, one oversight in a 50-page contract can kill a multimillion-dollar merger.”
He says it’s the same for companies that fail to “implement a mandatory human review for financial decisions of more than $50,000. As it is, a 5% error rate in automated systems can ruin years of profitability in one afternoon.”
Tsigler adds that, in any case, it’s good practice to keep a “strict paper trail” as the “best defense against regulatory scrutiny”.
Michael McCready of US-wide law firm McCreadyLaw adds that organisations should also consider ethics when using new technologies like AI.
“A key area where firms stumble is AI integration into legal and compliance workflows without rigorous ethical guardrails,” he says.
“Eager to automate contract reviews or risk assessments, businesses deploy tools that inadvertently amplify biases or expose sensitive data, inviting lawsuits, regulatory scrutiny and reputational damage that far outstrips initial savings.”
McCready has seen law firms “haemorrhage resources correcting AI-driven errors in discovery processes or client advisories, with cleanup costs easily tripling the tech investment and delaying case resolutions by months. Profitability crumbles under these avoidable crises.”
He suggests that firms “institute a proper governance framework from day one: mandate third-party audits for every AI tool, benchmark against strict accuracy and privacy metrics and train teams on oversight protocols.
Doing this, he says, “harnesses innovation while safeguarding financial control and long-term trust”.
Surprise Penalties with AI Use
Companies can often forget about different tax regulations when deciding to use AI in their operations, and they can end up with a hefty bill from the taxman, experts say.
Dat Ngo of Vetted Prop Firms, which reviews proprietary trading firms — companies that provide traders with capital in return for a share of returns — says bottom lines can be severely impacted if different operational regions are not taken into account.
“One area where businesses make particularly costly decisions is tax compliance during rapid AI scaling,” he said.
“Firms often overlook how deploying AI for financial planning triggers complex jurisdictional rules and transfer pricing issues, leading to unexpected audits and penalties that strain cash flow.”
He said that without proactive structuring, these oversights compound, turning compliance from a routine cost into a major drag on profitability.
“I have seen companies lose millions in avoidable tax exposures because they prioritised AI speed over regulatory mapping, forcing reactive fixes that divert funds from innovation,” he said. “The bottom line suffers as short-term gains evaporate under mounting liabilities and eroded stakeholder trust.”
To avoid such charges, Ngo advises that firms “build an adequate cross-functional tax review into every AI rollout. Map compliance risks upfront against clear financial benchmarks, then iterate with quarterly audits.”
“This ensures technology amplifies stability, not undermines it, creating resilient growth for the long haul.”
