Accounting
AI costs go beyond AI systems themselves
Major accounting firms have been placing huge bets on artificial intelligence, having invested billions upon billions of dollars in the past few years alone. This is done with the understanding that AI will ultimately reduce expenses and drive profits. Yet, as always, it takes money to make money: fully realizing the potential of artificial intelligence can come with a hefty price tag, encompassing both short and long term expenses for not just the AI systems themselves but everything else that enables their effective use.
The AI models themselves, of course, represent a significant R&D expense. Whether for internal efficiency, client engagements or both, building and training these models is no casual affair, requiring skilled specialists operating sophisticated software to create, something with which Doug Schrock, managing AI principal for top 25 firm Crowe, is well familiar. His own firm has spent a great deal of money developing custom AI solutions for things like tax and audit that are now used by staff every day, as well as Crow Mind, a gateway portal for all of the firm’s AI solutions. It has also devoted significant resources towards building bespoke AI solutions for clients, particularly in cases where they need something that simply does not exist in the market today. He compared it to making a custom Excel spreadsheet but far more complex.
“It’s like you buy Excel. Here’s Excel. But you’ve got to configure it to your business case, so there’s a whole lot of customization to make the actual spreadsheet do what you need it to do. We see that a lot: you buy the suite, but you need a bespoke solution… Configuring the hardware, chaining together multiple agents to do the tasks, automating it, that takes work,” he said.
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Chris Kouzios, chief information officer for top 50 firm Schellman, added that developing an AI system may appear to be a one-time spend at first, but considering things like maintenance, integrations and upgrades, each model can also represent an ongoing expense.
“If you think of the initial build, you could call the initial build one time, although like any piece of software it will be continually approved over time, so I look at it from both perspectives,” he said.
Big data, big costs
But the development costs of AI models are only one part of the overall expense. Just as significant, perhaps even more so, are the fees that come with hosting and accessing these models in the cloud. Running AI, especially generative AI, is very data intensive, which has served to accelerate cloud costs that have
“Your compute will go up at least exponentially over time and one of the things I think we’re going to see, and this is just future forecasting a little bit, I think clients will in general, not just in my space, be more comfortable when they feel they’ve got a little control over what they’re doing and what is done. In the cloud at the beginning people were terrified of putting their stuff there, we’ll see the same stuff with AI, we’ll probably have additional costs for spinning up instances for clients nervous about what goes where,” said Kouzios.
Crowe’s Schrock reported similar things, noting that the major cloud hosting companies saw the opportunity for revenue generation via AI hosting and are already capitalizing on the situation, as evidenced in the fees they charge. The reality is that generative AI uses a lot of data, which means higher data costs from cloud providers who run the infrastructure it rests on. He talked about a recent meeting he had with Microsoft, a strategic partner with Crowe.
“They’ve got 4 million servers across the US. They’re super interested in AI, not just because of Copilot but because we’ll be using Azure, using their server computing power to run the LLMs we write. They want to drive more Azure service dollars. So… we’ll be having more computing power costs for us through Azure,” he said.
Accounting solutions vendors have noticed this too. Brian Diffin, chief technology officer for business solutions provider Wolters Kluwer, also noted that generative AI has indeed led to higher cloud costs, which has challenged the company to find ways to release AI-functional products in an economically sustainable way.
“Gen AI is very CPU intensive, so one of the challenges we face—we’re doing a lot of experiments with this— is there’s so many approaches on how you would implement a gen AI based piece of functionality in software. We’re evaluating not just the LLMs in terms of what those capabilities would produce but what is going to be the cost of that feature when we go to production,” he said.
Data shows that this is happening not just in the accounting space but across the economy as a whole.
“GenAI is creating a cloud boom that will take IT expenditures to new heights,” said Chris Ortbals, chief product officer at Tangoe. “With year-over-year cloud spending up 30%, we’re seeing the financial fallout of AI demands. Left unmanaged, GenAI has the potential to make innovation financially unsustainable.”
The report noted that cloud software now costs businesses an average of $2,559 per employee annually. Large organizations spend an average of $40 million on cloud fees annually, with very large organizations worth more than $10 billion spending $132 million annually.
However, while cloud costs are rising due to AI, leaders are also confident that they can be managed. Schrock said his own firm has controls in place specifically to monitor data usage to avoid outsized costs. For instance, recently they tried a new LLM tool from Microsoft that caused a short 3,000% spike in usage, but firm leaders received an alert and quickly stepped in.
“It’s not like when you get surprised by the electric bill. You put controls in place to do things smart,” he said.
Further, while the costs have increased, he said they have still gained more than they lost in terms of increased efficiency and productivity. The extra fees are still lower than the cost of hiring an entirely new human, and the quality of work is better than what humans would accomplish alone. So while their Microsoft Azure bill is higher, they’re also able to deliver more for less cost overall, so it has been a net positive.
“What we’ve been talking about are the costs to run AI. I’ve got the cost to run a car but it also gets me places more easily. The cost will be a thing but used appropriately it will be great,” he said, adding that it’s important to use the right tool for the right situation; maybe you don’t need to access the high-data AI model to solve a problem, maybe Copilot would work fine.
Diffin raised a similar point. While he conceded overall costs have gone up, the money has been well-spent in terms of product development.
“Certainly gen AI capabilities are increasing in cost, and overall costs have gone up because we’re using more and more of what [Microsoft] offers, and so what translates into for us is developing and releasing products faster than if we were to develop everything ourselves,” said Diffin.
On top of cloud fees, subscriptions and licenses were also mentioned as a significant ongoing expense. This includes subscriptions not only for the tools used to create and maintain AI systems but also for AI solutions that the firm chooses to buy rather than build. While the individual subscriptions may not be much, when considering the size of certain firms, like Crowe, they can quickly add up, especially considering there are multiple products the firm subscribes to.
“Everything is a subscription. So you have all the different types of subscriptions. Crowe is making significant investments in ongoing software licensing for the leading enterprise AI solutions, things like Microsoft Copilot for example. We expect everyone in the firm to be using that in 2025. It’s over half right now … We’re also buying specialty AI based applications to fit particular needs and things like copy AI for marketing and search, and there’s a whole suite of specialty apps that we sign up for with specialty use cases, so that becomes the ongoing expense,” he said.
Labor costs, training costs
And then there are the people who create and maintain these models, often software engineers and data specialists. While often touted as a labor saving device, AI can come with surprisingly large labor costs, according to Schellman’s Kouzios.
“I would say in general, probably as close to 15-20% of my IT budget will be spent on AI, closer to 25% for the first year [of deployment]. Of that, if you take that number and break it out, 85-90% is labor,” he said.
The firm, which already hosts a large number of technical specialists, recently hired more to support the firm’s AI ambitions, seeking to shore up its machine learning, data analytics and product management expertise, which allows its staff to focus on “building what it is we want to do.” While this does represent a spending increase, he is confident that the efficiencies they uncover will increase firm-wide capacities over time.
“I think we’ll get to a point where, [though] we know the costs will go up, ROI on this should be deferral of cost or deterrence of cost, not having to spend money in the future we’d otherwise have to spend. For example, peak season comes up and you need to either hire employees or temp employees,maybe we can avoid that in the future,” he said.
Another component of labor costs is training the non-technical staff in using the AI systems the technical staff develops and maintains. Schrock, from Crowe, said that, in addition to hiring more experts, the firm has dropped cash on in-depth training and development in things like how to use Microsoft Copilot and other generative AI tools and incorporate them into a workflow. With this training has also come changes in business processes and job descriptions that needed time to properly digest. While there is some learning curve involved, he felt education like this was essential to fully implement the firm’s AI vision.
“These tools don’t inherently have value, they derive it only through their application to solve problems. So there is one time cost of upskilling and process redesign to incorporate that into the business,” he said.
And it is not just the humans who need training. Kouzios said one idea he has been exploring lately is assigning those trainers who’ve been educating the human staff to the AI models themselves, which often begin in an almost child-like state and require data input to be effective.
“I’ve been exploring talking to them about training the models because, this is my experience in IT, nerds are very good at the tech, but here are some things we lack and teaching—when I brought it up to them, I meant teaching the models—the tech people hated the idea, so I might tap into some of [the trainers’] time too,” he said.
Heat vs light
Yet, while big money is being spent on AI at accounting firms, they should not necessarily take too much stock in the marquee headlines of this firm spending that many billions on AI or that firm spending many more billions still.
“The billions of dollars here, is more bragging about an investment level. Well, investment level can be measured in a number of different ways. It can be measured by some ginned up cost where you reallocate peoples time and come up with some marketing number on costs, but I don’t put a lot of confidence in those as an expert in the field,” said Crowe’s Schrock.
Kouzios, from Schellman, raised a similar point, noting that there are a lot of people making big dramatic announcements that, upon closer inspection, are not that significant.
“You’ve seen those press releases, saying we bought chatGPT for our 85,000 employees, we’re AI enabled. Yippee, well done. For 20 bucks a month I could do that too,” he said.
When looking at what firms are spending on AI, Schrock said to look not at the jaw-dropping number they announce but in actual deliverables they produce.
“What I wanna understand is how many people are utilizing it, what unique IP they have created, how aggressively is it being incorporated into service lines, how aggressively do they take this into market—that is a measure of your investment level in AI more so than some number,” he said.
But what about smaller firms? Turns out, their experiences with AI costs are much different than large scale firms with international footprints. We intend to explore this issue more deeply in another story soon.
Cruise operators may yet avoid paying more U.S. corporate taxes despite threats from U.S. Commerce Secretary Howard Lutnick to close favorable loopholes.
Lutnick’s comments on Fox News Wednesday that U.S.-based cruise companies should be paying taxes even on ships registered abroad sent shares lower, though analysts indicated the worry may be overblown.
“We would note this is probably the 10th time in the last 15 years we have seen a politician (or other DC bureaucrat) talk about changing the tax structure of the cruise industry,” Stifel Managing Director Steven Wieczynski wrote in a note to clients. “Each time it was presented, it didn’t get very far.”
Industry shares fell sharply Thursday. Royal Caribbean Cruises Ltd. closed 7.6% lower, the largest drop since September 2022. Peers Carnival Corp. and Norwegian Cruise Line Holdings dropped by at least 4.9%.
All three continued slumping Friday, trading lower by around 1% each.
Cruise companies often operate their ships in international waters and can register those vessels in tax haven countries to avoid some U.S. corporate levies. It’s exactly those sorts of practices with which Lutnick has taken issue.
“You ever see a cruise ship with an American flag on the back?,” Lutnick said during the interview which aired Wednesday evening. “They have flags like Liberia or Panama. None of them pay taxes.”
“This is going to end under Donald Trump and those taxes are going to be paid.” He also called out foreign alcohol producers and the wider cargo shipping industry.
The vessels are embedded in international laws and treaties governing the wider maritime trades, including cargo shipping. Targeting cruise ships would require significant changes to those rule books to collect dues from the pleasure crafts, analysts noted. The cruise industry represents
They also pay significant port fees and could relocate abroad to avoid new additional taxes, according to Wieczynski, who sees the selloff as a buying opportunity.
“Cruise lines pay substantial taxes and fees in the U.S. — to the tune of nearly $2.5 billion, which represents 65% of the total taxes cruise lines pay worldwide, even though only a very small percentage of operations occur in U.S. waters,” CLIA said in an emailed statement.
Should increased taxes come to pass, the maximum impact to profits would be 21% on US earnings, Bernstein senior analyst Richard Clarke wrote in a note. That hit wouldn’t be enough to change their product offerings, though it may discourage future investment. Recently, U.S. cruise companies have
Cruise lines already employ tax mitigation teams that would work to counteract attempts by the U.S. to collect taxes on revenue generated in international waters, wrote Sharon Zackfia, a partner with William Blair.
Royal Caribbean did not respond to requests to comment. Carnival and Norwegian directed Bloomberg News to CLIA’s statement.
Artificial intelligence took the business world by storm in 2024. Content creation companies received powerful new AI-powered tools, allowing them to crank out high-quality images with simple prompts. AI also helped cybersecurity companies filter email for phishing attempts. Any company engaging in online meetings received an ever-ready assistant eager to show up, take notes and highlight the most important talking points.
These and countless other AI-driven tools that emerged during the past year are boosting efficiency in virtually every industry by automating the tasks that most often bog down business processes. Essentially, AI takes on the business world’s day-to-day dirty work, delivering with more accuracy and speed than human workers are capable of providing.
For accounting, AI couldn’t have come at a better time.
As companies struggle to do more with less, AI offers solutions that promise to reshape the accounting world. However, putting AI to work also forces companies to accept some new risks.
“Bias” has become a huge buzzword in the AI arena, forcing companies to consider how the automation tools they bring in to help with processing data may introduce some questionable or even dangerous ideas. There are also ethical issues associated with next-level AI-powered data processing that have some concerned that achieving AI-assisted business efficiency also means risking
To make AI worthwhile as an accounting tool, companies must find ways to balance gains in efficiency with the ethical risks it presents. The following explores the growing role AI can play in business accounting while also pointing out some of the downsides that should be carefully considered.
AI upside: Increased accuracy and efficiency
Accounting isn’t accounting if it isn’t accurate. Miskeyed amounts or misplaced decimal points aren’t acceptable, regardless of the company’s size or the business it is doing. When the numbers are wrong, the decision-making that relies on those numbers suffers.
Consequently, manual accounting typically moves slowly to avoid errors. Business leaders have learned to wait on financial reporting prepared by hand. They’ve also learned that because of processing delays, they may not have the numbers they need to take advantage of unexpected opportunities.
AI changes the equation by improving the speed and accuracy of reporting. AI-powered data entry automatically extracts numbers from invoices and other financial statements, eliminating the need for manual entry and the mistakes that can occur when an accountant is distracted, tired or just having an off day. AI can also detect errors or inconsistencies in incoming documents by comparing invoices and other documents to previous records, providing a second set of eyes for accounts as they ensure companies aren’t being overbilled or under-compensated.
When it comes to increasing the pace of accounting, AI’s capabilities are truly astonishing. As
AI accounting gives business leaders accurate financial data in real time, meaning they have relevant and reliable accounting intel when they need it rather than requiring them to wait until the end of the month to have a report on where their cash flow stands. It also has the potential to give a glimpse into the future by drawing upon historical data to drive predictive analytics. AI can look at what has been unfolding in a business and its industry to plot the path forward that makes the most financial sense. It’s not exactly a crystal ball, but it’s as close as most businesses should expect to get.
AI upside: More time for high-level engagement
As AI began to make inroads in the business world,
The manual work typical of conventional accounting is tedious, tiresome and time-consuming. Doing it well eats up much of the energy accountants could otherwise apply to higher-level activities. By using AI automation for those tasks, accountants gain the resources needed for high-level engagement.
Accountants who partner with AI gain the capacity to shift their role from bookkeeper to financial advisor. Rather than focusing all of their energy on preparing reports, they are freed up to interpret the reports. Delegating data entry and other day-to-day tasks to AI allows accountants to become strategic partners with the businesses they serve, whether as in-house employees or external advisors.
Financial forecasting becomes much more doable when AI is in play. Accountants can develop comprehensive financial models that forecast future revenue and expenses. They can also assess investment opportunities, such as determining the viability of mergers and acquisitions, and help with risk management and mitigation.
Tax planning and optimization will also become more manageable once AI automations have been added to the mix. Automating data extraction and categorization streamlines the process of classifying expenses for tax purposes and identifying expenses that are eligible for deductions. AI automation can also be used for tax form completion, adding speed and a higher level of accuracy to a process that very few accountants look forward to completing manually.
AI downside: Higher data security risks
Accountants are well aware of the dangers of data breaches. Allowing financial data to fall into unauthorized hands can lead to financial loss, operational disruption, reputational damage and regulatory consequences. Shifting to AI accounting can potentially increase the risk of data breaches.
Changing to AI accounting often means concentrating financial and other sensitive data and moving it to interconnected networks. Concentrating data creates a target that is more desirable to bad actors. Shifting it to the cloud or other interconnected networks creates a larger attack surface. Both factors create situations in which higher levels of data security are definitely needed.
Addressing the heightened threat of cyberattacks requires a combination of tech tools and human sensibilities. To keep accounting data safe, encryption, multifactor authentication, and regular testing and update protocols should be used. Training should also help accounting teams understand what an attack looks like and how to respond if they sense one is being carried out.
AI downside: Less process customization
Developing the types of platforms that can safely and reliably drive AI automations is not an easy — nor cheap — undertaking. Consequently, many companies choose the economy of “off-the-shelf” platforms. However, opting for a standardized platform could mean closing the door on customized financial workflows a company has developed.
For example, an off-the-shelf platform may not have the option of accommodating the accounting rules of highly specialized industries. It may have a predefined chart of accounts structure that doesn’t fit the structure a company has traditionally used. It also may be limited in the formats that can be used for financial reporting, which could require business leaders to make peace with reports that don’t fit their personal tastes.
To avoid big problems that can surface after shifting to off-the-shelf solutions, companies should make sure to take their time and seek software that can scale with their plans for growth. Like any other technological innovation, AI is a tool meant to support and not supplant a company’s processes. The process of selecting an AI platform to improve accounting efficiency begins with mapping out a company’s unique process and identifying where AI can boost efficiency. If the platform you are considering can’t deliver, keep looking.
AI best practice: Take it slow and learn as you go
The biggest temptation for companies as they begin to embrace AI will likely be doing too much too fast and with too little oversight. Artificial intelligence is a remarkable tech tool, but still in its infancy. Taking advantage of its capabilities also requires managing some risks.
For example, AI has what some experts describe as an “explainability” problem. Developers know what AI can do but don’t always know how it does it. Companies that feel compelled to provide their clients or stakeholders with a solid explanation of the process behind their AI automations may be limited in how they can put AI to work.
Now is the time to begin integrating AI with your company’s accounting efforts, but take it slow and learn as you go. A solid best practice is to explore what is available, experiment with how it can help your business, and expect to make many adjustments before you arrive at an optimal process. Your accounting efforts will serve you best when they combine human and artificial intelligence.
Ascend, a private-equity backed accounting firm, added a vice president of partnerships to its leadership team.
Maureen Churgovich Dillmore will oversee the expansion of Ascend’s growth platform for regional accounting firms into new U.S. markets, effective Feb. 17. She was previously executive director of the Americas at Prime Global. Prior, she was executive director at DFK International/USA.
“I have dedicated a large part of my career to supporting firms that want to remain independent. The dynamics of achieving success in this area are evolving rapidly, and the Ascend model was created so that firm identity would not be at odds with accessing the community and resources needed to prosper. I am genuinely impressed by Ascend’s ability to assist mid-sized firms in making the necessary strides to stay relevant, sustain growth, and provide their staff and clients with top-tier shared services—all while preserving their unique brand and culture,” Churgovich Dillmore said in a statement.
Ascend has added 14 partner firms across 11 states since the company launched in January 2023.
“So much of association work is theoretical, advising member firms on best practices, and you don’t get to see the end game. What excites me about being on the Ascend team is the opportunity to be a force behind the change, to help enact the change and see where and how it comes in,” Churgovich Dillmore added.
“Maureen’s decision to join Ascend is rooted in her desire to serve the profession in a way that maximizes her impact. We are all excited to welcome someone into our Company who has been an advisor and friend to mid-sized CPA firms for over a decade, and it is all the more rewarding when you realize that the community and resources we are bringing to life will allow Maureen to have conversations with firms that she’s never had before. Her curiosity, commitment, and deep care for others are going to stand out in this role,” Nishaad (Nish) Ruparel, president of Ascend, said in a statement.
Ascend is backed by private equity firm Alpine Investors and works with regional accounting firms with between $15 and $50 million in revenue. It ranked No. 59 on Accounting Today‘s
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