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Alex Zank

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CFO Brew joined a crowd of corporate finance practitioners who recently descended on the Gaylord National convention center in Washington, DC, for Workiva’s 2025 Amplify conference.

Surprise, surprise: The prevailing topic this year was AI. Thought leaders and attendees waxed poetic on the technology’s potential to transform all facets of corporate finance. But existential talk is one thing. Practical use is another.

That was one of the prevailing questions during the three-day conference: How exactly are accounting, audit, and reporting teams 

? As it turned out, the use cases flowed as freely as the coffee.

 Amplify was like a Golden Corral of use cases—there was a little something for everyone. Here are some of the highlights.

The internal audit team at financial services firm StoneX uses AI tools to help write data queries and then check the results for accuracy and completeness, Jeanne Cline, StoneX’s chief audit executive, said during a panel session.

Consumer loan fintech Best Egg’s risk management team used AI to write first drafts in a series of eight policies, Chief Risk Officer Heather Holding said. “I was actually able to do them in a day,” she told session attendees. “Of course, obviously my team had to look at it and refine it and make sure that it was good for the organization.”

AI also helps Holding’s team sift through competitors’ SEC reports to identify emerging risk trends. This type of analysis can help organizations game-plan “what you can actually do to get ahead of the risk that your other partners are already seeing,” she said.

Alexander Davis, deputy CFO of Pie Insurance, told CFO Brew that he uses AI to easily tweak commentary based on the intended audience.

“If I’m writing the same slide for the finance team, leadership team, and the board, it’s the same numbers with three different levels of story,” Davis explained. AI makes it easier and quicker to tailor that information to different recipients, he said.

Pie’s finance team is also using AI to more quickly analyze new guidance or standards from regulators like FASB and NAIC.

“We’re using less of our time and less of the auditors’ time, because we have a pinpointed approach,” Davis said.

 By and large, organizations have gotten good at using AI for “mundane tasks,” but are thinking bigger for future implementations, Mike Callino, customer engagement platform Braze’s senior director of internal audit, told eventgoers. Audit professionals will, over the next 12 to 18 months, start identifying where they can deploy AI tools into regular workflows “for planning, field work, reporting, [and] remediation…to make that process so much more efficient and drive greater gains in the audit function.”

“I think there will be a big play on agentic solutions in each part of the process,” Callino said.

In the meantime, finance leaders want to show off the potential of AI through some bite-sized immediate improvements.

Junko Swain, chief accounting officer at Workiva, said she encourages her team to “start small” and get a “quick win, and a quick benefit from using AI in a secured environment.” This strategy, she explained, helps workers become comfortable with the technology before the bigger changes take hold within the organization.

“We are taking baby steps,” Swain told CFO Brew. “We talk about AI, but actual adoption of AI itself takes time, because it requires such huge change management.”

Similarly, StoneX is in the “individual production stage” of AI, where employees are getting comfortable with using the AI tools, according to Cline. The company’s next steps will be to embed AI more broadly into various departments and workflows, “making a whole process more efficient and effective than just the individual,” she said.

Experts cautioned that AI is only as good as the data it’s being fed. The next installment in CFO Brew’s chronicling of all the hot AI topics at Amplify will dive deeply into that challenge, and relay the best practices as told by experts.

Photo collage of an AI icon in between two hands pointing at each other contained within abstract shapes.

AI in finance offers quick wins and big promises

Finance leaders said their companies are taking baby steps now, but thinking big for the long run.

The hazards may appear fewer, but don’t get caught asleep at the wheel on the information superhighway.

Or at least, that’s one way to interpret the findings of Resilience’s 2025 midyear outlook. The cyber insurance and risk management firm analyzed its claims portfolio and found that first-half cyber claims dropped by more than half (53%) compared to the first six months of 2024.

Yet the claims data also contained a stark reminder not to sleep on ransomware attacks, which got more expensive, on average. Resilience found that ransomware claim severity increased 20% in the first half of the year.

“Fewer companies are paying ransoms, so more companies are prepared to execute their incident response plans and recover from these types of attacks without feeling like they have to pay the threat actor,” Ann Irvine, Resilience’s chief data and analytics officer, told CFO Brew. But among the companies that aren’t able to stave off ransomware attacks, “costs can still be very large,” she added.

 Irvine said it was “awesome” to see fewer organizations falling victim to ransomware schemes. What separates them from those that racked up ransomware losses was their ability to “restore systems from backups quickly and comprehensively,” Irvine said.

“If companies know where their backups are, the backups are recent, and they’re comprehensive, then that’s really the key to executing an incident response plan and getting back to business really quickly,” she continued.

Some companies found out the hard way that, while they had almost everything backed up, one department or large system was either not backed up at all or not recently enough, Irvine said. That can be the difference between being unaffected or being down a long time.

To avoid falling into that trap, Irvine said Resilience recommends that organizations put their incident response plans to the test through tabletop exercises. These practice runs should help “make sure all departments are covered [with system backups], not just nine out of 10,” Irvine said. “That last department can really matter.”

According to Resilience data, ransomware accounted for three-quarters of cyber losses in the first six months of 2025. Business interruption from ransomware attacks on a vendor accounted for 15% of losses, and another 8% came from transfer fraud.

Vendor-related cyber losses may sound familiar.

, while not caused by a criminal cyber gang, grabbed a lot of attention due to its widespread impact. But ransomware was the culprit behind the Change Healthcare and CDK Global incidents.

These incidents haven’t been as much of a nuisance so far this year. Per the Resilience report, vendor-related cyber claims made up just a quarter of all claims notices in the first six months of this year, and accounted for 8% of all claims where organizations incurred losses (meaning required a payout). This was an improvement on both counts compared to 2024, when vendor incidents accounted for 34% of all claims and 18% of those with incurred losses throughout 2024.

Vendor incidents from last year showed “how exploiting a single point of failure in one company can lead to cascading disruption downstream, affecting entire industries and economic sectors,” Resilience noted in its report.

“These incidents are spotty,” Irvine said. “When they happen they can be super impactful to a lot of companies, and it’s very hard to predict when the next one will occur.”

A view of Earth from space with red warning triangles floating around it.

Cyber insurance claims are down this year

But ransomware attack severity is up. 

Jesse Klein

Want to know more about risk management best practices? Join us virtually or in-person for our upcoming live event, Risky Business: Navigating Compliance, Risk, and Regulation in Finance, in NYC on September 16, for more on navigating through these risky times. 

 has reached a fever pitch. But in the rush to adopt the new tech, companies might be forgetting to ask an important question: What’s getting overlooked and left behind? If you ask Amy Worley, managing director and head of management consulting firm BRG’s digital compliance practice, the answers are privacy, security, and money.

CFO Brew asked Worley some of the deep AI questions CFOs might whisper to themselves at night when they can’t sleep, as they hurtle toward an AI future.

This interview has been edited for length and clarity.

What in the AI landscape is concerning you?

What I’m seeing is a lot of senior leaders are pushing for rapid adoption. There is a widespread view that adoption of AI will create efficiencies and drive down costs. And so for a lot of our clients, CEOs, COOs, [and] boards have given the directive, “Do AI and do it right now.”

For many organizations, this requires a level of data governance that they may not have had before, and so there’s a lot of change management and organizational readiness issues that are coming up. And then also, because this is a new area, legally, there’s a whole lot of questions about how to appropriately manage risk, and how to even really talk about quantifying that risk and setting the controls to mitigate it.

What are the big mistakes that you’re seeing companies make?

A lot of companies I’m seeing are underestimating how much time and effort it’s going to take to really get their data in a format and in locations and managed in a way to get the value out of AI, to pay back the investment in purchasing different AI systems.

That’s not exactly a privacy mistake, though, is it?

No, it’s a more general mistake that leads to privacy problems. Let me give you an example. There are a lot of cool tools on the market that will use an agent to do enterprise search and efficiency, and most of these tools default to your organization’s access control, deletion, and existing networks. Many organizations haven’t really focused on that for unstructured data, so their SharePoint environment might not have tight access controls or tight user permissions. Google workspace might not have those. You put the AI on top of that, and all of a sudden, you may be pulling unstructured data up that contains personal information or sensitive personal data about people, and you didn’t intend that.

Governance, governance, governance. We are quickly changing the structure of how data governance works in organizations…AI governance is cross-functional, and so it is requiring multiple stakeholders to be at the table for the entire AI lifecycle.

If you’re telling me you want to use agentic AI on a database where there are existing privacy controls in place, that is a much faster yes. But if you tell me you want to set up an agentic AI to improve worker efficiency and give it access to a bunch of unstructured data, that is a much longer step-by-step process. And we’re going to look at things like access controls…The right way to do it is then to go back and test, at some designated point in the future, to make sure those controls actually got implemented…It isn’t going to sit with just one team. It’s going to require a cross-functional team.

Where are the surprise costs coming from?

One is the money cost, in that the price tag for purchasing and implementing an AI system may look like it’s $X, but once you do the actual implementation work to get everything assessed, to get the controls in place, to get it integrated appropriately; often, redaction tools and things like that can be an upcharge. You buy a privacy package, it can be more expensive than you think. So it can straight up just cost more.

The risk is incredibly amorphous…I would call it old-fashioned risk in a new package…If we train AI models on old information, we can bring in old biases. And so we just want to make sure that we’re not doing that.

One of the things I get asked by my CFO direct report people is “put a number on this risk.” And that is really hard to do for AI, in a way that’s very different than data protection. So for privacy, I can pull up five years’ worth of opinions, whether it’s class actions or a supervisory, and I can say, “Okay, well, the monetary regulatory risk is X.” And you really can’t do that yet here, but I expect that we’re going to see some creative lawsuits like we've seen from class action plaintiffs' lawyers bringing privacy lawsuits based on cookies and tracking technologies and things like that.

The biggest mistakes companies make when onboarding AI

 A digital compliance expert weighs in on what companies are doing wrong with AI implementation.

Natasha Piñon

Gee, what a strange, random coincidence: When industries go all-in on AI adoption, people might lose their jobs because of it.

The US “may be witnessing the early stages of AI-driven job displacement,” according to 

 published at the end of August by the Federal Reserve Bank of St. Louis.

Fed researchers were interested in the sharp slowdown of the labor market in July’s jobs report, and, more broadly, they wondered to what degree “widespread deployment of large language models” was contributing to rising unemployment.

Perhaps you remember that particular jobs report: That’s the one that inspired President Trump to take the 

 of firing the Bureau of Labor Statistics’s commissioner hours after the July results were released. July wasn’t a blip, though: August’s jobs report was 

So, where does AI factor in? To determine which industries had the highest AI prevalence, Fed researchers analyzed more than 19,000 tasks from the Department of Labor’s O*NET database, which lists occupational characteristics and worker requirements, and determined “whether LLMs could reduce task completion time by at least 50%,” ultimately aggregating results “at the occupational level to create comprehensive AI exposure scores.”

They also looked at “real-world uptake patterns” from the Real-Time Population Survey “in which workers reported the intensity of their actual generative AI usage and time savings.”

Either way you slice it—theoretical or actual AI usage—researchers found “both measures reveal a striking correlation between AI’s prevalence and unemployment increases since 2022.”

Take it all with a grain of salt: The report’s authors were careful to stress that their findings “represent correlation, not causation,” considering that many factors, from 

 to monetary policy, could be driving “rising unemployment in AI-exposed occupations.”

Between 2022 and 2025, occupations with higher AI exposure saw a higher unemployment rate.

“Computer and mathematical occupations—predictably among the most AI-exposed, with a score around 80%—saw some of the steepest unemployment rises,” the report noted. “Meanwhile, blue-collar jobs and personal service roles, which have limited AI applicability, experienced relatively smaller increases.”

Industries with high AI adoption similarly “showed the largest unemployment gains” in that time period. “Again, computer and mathematical occupations, which adopted these technologies most heavily, experienced substantial unemployment increases,” the report’s authors said.

“These patterns are particularly striking in technology sectors, where workers might expect AI to augment rather than to replace their roles,” they continued. “Unlike previous technological revolutions that primarily affected manufacturing or routine clerical work, generative AI can target cognitive tasks performed by knowledge workers—traditionally among the most secure employment categories.”

All the while, the researchers noted that because AI is such a new technology, potential longer-term benefits that could offset job displacement, like creating new jobs and industries, are currently unknown.

“If current trends continue, new approaches to workforce development, social safety nets and economic support for displaced workers may merit greater consideration,” the researchers said. “As we navigate this technological revolution, understanding potential early warning signs could prove crucial for protecting US workers and maintaining economic stability.”

Industries with most AI prevalence could be showing early signs of job displacement, according to St. Louis Fed

Fed researchers sought out to what degree “widespread deployment of large language models” was contributing to rising unemployment.   

As much as finance professionals and tech evangelists love to tout the power of generative AI to positively change the workplace, there’s a downside that doesn’t get talked about as much: AI is supercharging 

 that is costing organizations trillions.

It’s a problem taking many forms. Fraudsters are creating 

 impersonating CEOs to fool employees and grab millions. Malignant foreign actors are using AI to get hired for remote jobs with US tech companies, giving them access to sensitive company processes and data. Business email compromise, already a 

 for finance departments, is accelerating with the use of AI.

Companies are feeling the impact of the fraud increase in very expensive ways. A March study by TrustPair found “a 118% year over year increase in 

 like deepfakes and deepaudio,” according to CFO.com. A 

 predicted that “GenAI could drive a substantial increase in fraud losses in the United States: from some $12 billion in 2023 to $40 billion by 2027.”

So it makes sense that CFOs are increasingly interested in protecting themselves against AI fraud, according to Patricio Munoz, director in forensic practice at KPMG.

“We’re seeing an uptick in client interest with regard to AI fraud, what they can do to mitigate that risk, what they can do to get prepared for that risk,” Munoz told CFO Brew.

To help figure that out, CFO Brew spoke with experts about what CFOs need to understand about the changing AI-enabled fraud risk landscape, and what companies can do to protect themselves.

Fraud is nothing new, but AI is putting the speed and scale of scams into hyperdrive. The velocity, breadth, quality, and quantity have all increased dramatically, according to the experts we spoke with.

AI-enabled fraud is making it harder for companies to believe their own eyes and ears. Fraudsters are 

 to dupe companies out of money, hack into their systems, and steal data, according to Munoz.

Munoz has seen executives being impersonated in virtual meetings and voice calls. For example, a CFO might receive a voicemail, short Zoom call, or voice note that purports to be from the CEO. It says—in the CEOs voice—“I’m about to get on a flight, but I want to transfer money to this bank to start heading up our new office abroad. I’ll send you the details via text.”

This is exactly what happened to “a finance worker at a multinational firm” who transferred $25 million dollars to scammers posing as the company’s CFO, 

. The scam included a video meeting with deepfakes of company staff real enough to convince the victim to wire the money.

“Normally, we would have seen this via email, but we’re seeing this now moving to other messaging and communication platforms,” Munoz said.

The call is coming from inside the house.

 With AI, scammers are likely to uncover and exploit even more holes within organizations. Jim Wetekamp, CEO of Riskonnect, has seen even more devious tactics to get inside companies, including 

 created with deepfake impersonations. With AI, that task is simpler for scammers, according to Wetekamp.

“They can code faster than they’ve ever coded,” Wetekamp said. “They can adapt faster than they’ve ever been able to adapt. They can exploit and find cracks in the infrastructure faster than they’ve ever been able to.”

 will catch up, but according to Wetekamp, that’s going to take a minute. But the crooks will be moving quickly and AI will probably create more scam artists, Munoz said.

“[AI’s] going to make fraudsters more sophisticated and harder to detect,” Munoz said. “I think we’re also going to see traditionally less tech-savvy fraudsters use AI tools.”

 will need to be layered on top of controls already in place. The standard security protocols “will still be relevant, but they just won’t be as effective,” according to Munoz.

One of the most important security features companies should add are unique passwords or keywords for 

. With deepfake voices and video, a password only known to executives can be used to identify if the person in a video or audio is real or counterfeit.

Companies are also taking other preventive measures. Some of Munoz’s clients are automatically kicking unknown numbers out of conference calls and forcing everyone to have video on to identify anomalies like suspicious face-covering shadows or delayed responses.

For the past decade, we’ve lived with “triangulated multiple points of validation,” as Wetekamp puts it. But he thinks that a post-Covid snapback to face-to-face interactions and hard copies is imminent because of AI.

“[AI fraud] is going to probably create new standards for verification,” he said.

A detected deepfake displayed on a glitchy computer screen.

The next era of fraud is here

CFOs may not be able to believe their eyes. 

While most AI agents have been underwhelming so far, KPMG has built one that may ease the collective shudder that comes with the words “tax day.”

 aren’t the magic wand AI companies have been selling. A study from Carnegie Mellon University 

 that the best-performing model completed the intended task just 30.3% of the time. Just a few months ago, Glenn Hopper, head of AI at VAI consulting, 

 that AI agents “are not ready for primetime” and are still mostly “vaporware.”

But the Australian branch of KPMG is claiming that a 100-page prompt for AI they’ve written can autonomously create tax advice for clients “​​far faster than humans,” tech publication 

The company created its own AI bots, using models from all major AI players, including OpenAI, Microsoft, Google, Anthropic, and Meta. It fed them as much historical tax advice as it could, along with the Australian tax code, according to The Register.

“It is very efficient. It does what our team used to do in about two weeks, in a day. It will strip through our documents and the legislation and produce a 25-page document for a client as a first draft,” KPMG Chief Digital Officer John Munnelly said at a recent tech summit, The Register reported.

But don’t uninstall your TurboTax software just yet. Apparently the AI tax output is only understandable by tax experts, not your average Joe trying to file last minute.

KPMG creates an AI agent that does taxes

KPMG Australia’s chief digital officer says the AI does in a day what it takes his human team two weeks to do.

Courtney Vien

On August 7, OpenAI’s latest LLM, GPT-5, came out, and landed with more of a whimper than a bang.

Its previous models, GPT-3 and GPT-4, each improved dramatically upon their predecessors, raising hopes that GPT-5 would prove a similar advance. Instead, GPT-5 seemed to be more of an incremental improvement. It still hallucinated, and it still 

 basic reasoning tasks: The model that Sam Altman likened to having a “team of 

 in your pocket” couldn’t tell you the 

The GPT-5 fizzle was the start of a rough month for generative AI’s reputation. In a New Yorker article, Cal Newport 

 whether the technology was ever going to get that much better. The New York Times 

 on McKinsey research showing that ~80% of companies were seeing “

” from GenAI, despite having spent billions on it. And a 

 by MIT’s NANDA showed that 95% of companies’ pilots of enterprise AI tools fail.

All of that is hard to square with the bold claims GenAI proponents made early on: that the technology would be a “

, that it was a step that would lead humanity to artificial general intelligence (

 a wide swath of white-collar jobs. Almost three years after the public release of ChatGPT 2, where is the technology really headed?

GenAI sentiment has had about as many eras as Taylor Swift. There was the “free your workforce up for 

 with bots” cycle. Now, we may be entering the decidedly less-sexy era of modest efficiency gains.

For instance, JPMorgan’s employees use an AI assistant to help them with tasks such as research and report writing, the New York Times reported. About half of the 200,000 staff who use it save up to four hours a week, the bank said. As CFO Brew 

, Salesforce and PayPal’s COFOs both said that AI has let them “reallocate” staff away from redundant tasks.

Outside the office, AI can help with productivity and even safety. Technicians at Johnson Controls were able to shave 10–15 minutes off hour-long calls by using an AI that suggests repair options. Metals and mining company Phoenix Global installed AI dashcams and apps on tablets that can 

 and alert them to drowsiness or inattention. The system cut unsafe driving incidents by 40% in just its first month in operation. Given the high cost of an accident, the system could potentially save the company millions, CFO Jeff Suellentrop previously told CFO Brew.

Much of the buzz around AI this year has centered around agents—programs that can 

—such as sending reminder emails, generating PowerPoints and Excel spreadsheets, categorizing expenses, or booking flights. Theoretically, one day, agents could be linked together to complete entire workflows.

But don’t clean out your desk just yet. Developers give agents 

. Researchers at Carnegie Mellon University 

 agents’ performance in a model company and found that even the best of them independently completed office tasks just 30.3% of the time. (The worst had a 99.9% failure rate.) The agents could be stymied by something as simple as pop-up ads on a website. A Salesforce 

 similarly found that agents only have a 35% success rate in “multi-turn interactions,” in which they respond to more than one prompt.

Agents aimed at consumers are likewise still pretty wonky. A ChatGPT agent took almost an hour to 

 of five Japanese-style lamps sold on Etsy.

That’s not even getting into the security risks. The Salesforce study noted that agents “demonstrate near-zero inherent confidentiality awareness.”

Agents might become a threat to jobs some day (though there’s always that pesky 

 to contend with), but what’s happening right now?

There, the picture is murkier. Individual companies have made headlines with 

 about replacing people with AI, and large employers like Amazon and JPMorgan have claimed AI-related layoffs are coming. A 

 by Revelio Labs found that job postings for roles with “high exposure” to AI disruption, such as database administrators, data engineers, and IT professionals, dropped 31% over the past three years, compared with a 25% drop for jobs with “low exposure,” such as restaurant managers and mechanics.

But it’s hard to disentangle the effects of AI on the job market from other macroeconomic factors, like inflation, tariffs, reversing pandemic-fueled 

, and the changes to the tax treatment of R&D that have led to around half a million tech layoffs since 2023. Tech and media/communications are the only two sectors that have seen “clear signs of AI disruption,” the MIT report said. The “jobs most impacted were already low-priority or outsourced,” Aditya Challapally, research contributor for NANDA, told Axios.

Instead of laying people off, companies seem to be holding off on 

 to see whether they can be performed by AI. More of the gains companies are seeing come from “replacing BPOs [business process outsourcing] and external agencies, not cutting internal staff,” the MIT report stated. Those are changes, to be sure, but hardly a “

Even Altman, who once asserted that AI would “

” and drive the value of labor “toward zero,” is walking back some of his claims. On a recent episode of CNBC’s 