CFO best practices in the evolving generative AI era

Nearly eight in 10 corporate finance professionals blame month-end close delays on having to wait for data from other systems or departments, according to research unveiled Tuesday by finance software firm LiveFlow.

Reconciling information across multiple platforms was another barrier, cited by more than half of survey respondents. The findings suggest that despite widespread investment in finance automation tools, key accounting workflows remain hampered by challenges like fragmented data environments, LiveFlow Financial Data Analyst Aaryn Ross told CFO Dive.

“Companies are investing in AI, but they're still not closing the books as fast as they would like,” Ross said.

LiveFlow’s enterprise resource planning platform is designed to automate accounting and finance workflows, including month-end closes. Ross said the New York-based technology vendor conducted its survey in part to better understand where bottlenecks persist in finance operations. 

The company found that while finance teams are increasingly equipped with advanced analytics and productivity tools, those capabilities have not yet materially changed the structure of the month-end close cycle.

Only 16% of respondents said they complete their month-end close in under three days. The largest group, 37%, reported cycles of three to five days, followed by 21% at five to 10 days and 16% extending beyond 10 days.

Ross said AI is commonly applied to higher-value work such as analytical and reporting tasks, while the underlying operational workload has shifted far less, leaving the most time-consuming parts of the close largely unchanged.

About 80% of respondents said they use AI in their work for drafting content, and 65% cited financial analysis. Adoption drops off for more operational use cases, with just 23% relying on AI feaures embedded in finance systems.

“Much of the repetitive work — data entry, transaction categorization, reconciling numbers — is still being done manually,” Ross said.

A November 2025 McKinsey report underscores how elusive meaningful operational impact from AI remains, even as adoption accelerates. Nearly two-thirds of respondents said their organizations have not yet begun scaling AI across the enterprise, with many initiatives stalling at the pilot stage or failing to integrate into core processes.

“To capture AI’s potential in finance, teams will need to do more than add new tools on top of old ways of working,” the authors said. “They must rewire core processes, talent, and technology so that adoption takes hold and creates value.”

Most CFOs expect headcount in their finance functions to either stay flat or decrease over the next three years — but the more crucial change coming for finance may be the skills needed as spending on artificial intelligence rises, a recent survey by Oliver Wyman found.

Though 30% of CFOs expect to reduce headcount over that time, 61% expect a decrease of less than 10% or for headcount to stay flat, the survey released Tuesday by Oliver Wyman, a Marsh company, and the New York Stock Exchange found.

However, “the more significant change could be in the composition of the finance staff,” according to the survey, which noted the integration of AI and automation into the function is expected to eliminate “labor-intensive finance operations.” Sixty-four percent of survey respondents said they expect their finance teams to shift away from more junior roles, a change which “reflects a deeper change in the work itself,” according to the survey.

As companies continue to navigate ongoing macroeconomic and geopolitical uncertainty, many are leaning harder on their finance functions to provide the insights and strategic direction they need to operate effectively in a dynamic environment.

That’s creating the need for CFOs to have a broader skillset behind traditional finance, the survey found. Seventy percent of finance chiefs cited “shaping strategy and transformation” as one of their top three priorities, while 72% expect the importance of that responsibility to grow over the next three years.

While remaining core to the role, meanwhile, less than one in 10 CFOs anticipate reporting, data stewardship and other more traditional CFO responsibilities will rise in importance over that same time period, Oliver Wyman found.

CFOs, therefore, are tasked with both transforming their finance team to provide those insights while navigating their own ongoing evolution from finance stewards to creators of value and strategy.

The finance chief is “simultaneously operator, allocator, and analytical copilot,” the survey of nearly 500 global CFOs found — tasked with ownership over five distinct agendas, from effectively scaling AI in the organization and balancing cost and growth to the redesign and transformation of the finance function.

To effectively execute on those sometimes conflicting agendas, finance chiefs are expecting to change the makeup, as well as the size, of their finance teams — seeking a leaner, but more experienced finance team supported by new technology, Oliver Wyman found.

The “traditional finance pyramid is starting to flatten into more of a middle-heavy diamond,” according to the survey, which found 41% of respondents expect a shift to more midlevel finance roles while 23% anticipate a move to more senior roles.

The recalibrated team, meanwhile, is expected to make further use of AI, which many leaders — 80% see as a key lever for change and transformation within the finance function.

Meanwhile, 61% of respondents expecting their organizations spending on enterprise AI will grow from between 5% to 20% by the end of this year.

However, there still remains a large gap between the pace of AI investment and that of enablement. Seventy percent of CFOs said the use of AI in key finance activities are still at the planning or piloting stage, while only 8% have deployed AI-enabled solutions or agents at scale.

CFOs also need some convincing when it comes to using AI as a way to create value: Only 6% cited AI investment as their top way of driving up enterprise value, “suggesting skepticism around the effectiveness of efforts to date,” according to the survey.

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The allure of AI in finance is undeniable: the promise of autonomous forecasting, real-time risk mitigation and a level of operational efficiency that was science fiction a decade ago. But as the initial hype settles into implementation, a sobering reality is emerging for the C-suite: An AI system is only as credible as the data behind it.

“There is an increasing amount of pressure on business leaders to use data in today’s hyper-competitive environment,” Southard Jones, chief product officer at Tableau, said in a Salesforce study. “Yet, fragmented enterprise data and complex analytics tools are common obstacles leaders face that can significantly hinder confident decision-making.”

For a CFO, the stakes of “good-enough” data have shifted to the point where it poses a fundamental threat to the integrity of the balance sheet.

The danger of subpar data

In the pre-AI era, largely accurate data was manageable. If a spreadsheet had a few inconsistencies or a legacy database contained some noise, a skilled analyst could spot the outlier and correct it before a quarterly review. Human intuition acted as the final filter.

AI removes that filter. Machine learning models don't know when a data point is an entry error versus a market trend and might simply incorporate it into their logic. Feeding mediocre data into sophisticated algorithms can result in scaling inaccuracies at a speed no manual process can catch.

The Salesforce survey of 552 business leaders found a multi-year decline in confidence for various aspects of their data. The trend turns potentially troubling where the finance function is concerned.

From technical debt to fiduciary liability

While often framed in technical terms, data quality is now increasingly a matter of risk management for modern CFOs. Consider the following scenarios where less-than-perfect data creates genuine liability:

• Automated compliance failures: If AI-driven audit tools are trained on incomplete historical data, they may overlook patterns of non-compliance, leaving the firm vulnerable to regulatory fines.

• Skewed capital allocation: AI models used for predictive budgeting can lead to disastrous hallucinations if they aren’t grounded in high-fidelity data, resulting in millions of dollars diverted to the wrong initiatives.

• Erosion of investor trust: In an era where financial transparency is scrutinized by algorithms and humans alike, a single AI-generated report based on noisy data can trigger a crisis of confidence.

The reality check: A model that is 90% accurate sounds impressive—until it’s clear that the 10% margin of error potentially represents millions in misallocated capital or a missed regulatory requirement.

To read more from this perspective, please click here.

Stubborn inflation and shifting demand patterns are upending CFO playbooks in consumer-facing industries, elevating the role of artificial intelligence in areas such as pricing and forecasting.

That was a key theme of a recent panel on how finance chiefs are tapping technology for consumer insight, part of a broader retail-focused virtual event hosted by CFO Dive and Retail Dive.

Retailers are still dealing with the aftershocks of inflation, even as headline numbers moderate, according to Sonja Evans, vice president of business intelligence and strategy at marketing firm Blue Chip. Shoppers remain highly price conscious, and preferences have become harder to predict.

The shift is “not [happening] because they're panicking, but just because they've kind of recalibrated what they're willing to pay and where,” Evans said during the panel discussion.

That volatility is making legacy forecasting models less effective and putting increased pressure on CFOs to adopt more dynamic planning and forecasting models. 

Precise markdowns and synthetic audiences

Sudip Mazumder, senior vice president and retail industry lead at digital transformation firm Publicis Sapient, said AI is enabling companies to move beyond blanket discounting by aligning markdown decisions with the relative performance of individual stock keeping units.

Some retailers have begun to “really go and do some precise markdowns,” he said, pointing to apparel companies such as H&M and Zara as examples.

Evans highlighted product innovation and consumer research as another emerging area for AI adoption.

“Organizations are leaning into AI for their innovation research,” Evans said. General Mills, for example, has used AI to build “synthetic audiences” to better understand and respond to consumers. Such tools are increasingly being used to accelerate early-stage ideation and refine how companies interpret consumer needs before products reach market.

Meanwhile, Mazumder cited Albertsons as an example of a company deploying AI to infer shopping intent. If a shopper adds taco shells and ground beef to a cart, systems can recommend salsa or toppings in real time, increasing basket size while improving convenience.

Brett Narlinger, chief revenue officer at payments company Blackhawk Network, highlighted AI-driven changes in how consumers discover and select products, citing Giftcards.com as an example of a shift away from traditional search toward more conversational, intent-based interfaces. The site is using AI to let consumers describe what they are trying to accomplish, rather than requiring them to search by brand or product name, he said during the discussion.

Narlinger also pointed to broader efforts to deploy agentic AI, which he described as “agent-to-agent communication” across commerce workflows from product discovery through checkout and fulfillment.

He also said many organizations are still struggling to effectively utilize the large volumes of data they already possess. “We have a ton of data. We haven’t quite figured out what to do with it yet,” Narlinger said, adding that AI can help convert that information into usable insights and actions for businesses.

Not a silver bullet

Despite enthusiasm around AI, panelists cautioned that technology alone will not solve structural problems.

“It's not a silver bullet. It really amplifies whatever foundation you already have, whatever data stack,” Evans said.

Mazumder added that poor data quality can lead to “hallucinations and bad decisions.” By focusing on foundational elements like having the right data infrastructure and talent, companies can “see better outcomes,” he said.

In coming years, AI and data analytics will enable CFOs to become even more forward-looking and anticipate shifts in consumer demand more quickly, panelists predicted.

The future will be about “speed of information and insights,” Narlinger said. “That's going to be the rule.”

Editor’s note: The insights above were shared in a panel focused on tapping technology for consumer insight that was part of an April 22 event co-hosted by the editorial teams of CFO Dive and Retail Dive: Is the price right? Understanding consumers and the economy. You can register for free here for access to a recording of the full session.

Making use of artificial intelligence has quickly become not an option, but an expectation for both CFOs and the finance function, Trintech CFO Omar Choucair said.

This means finance chiefs need to be moving as quickly as possible to incorporate AI effectively into their businesses — but it’s also important to keep in mind the “immovable points” or critical responsibilities inside the Office of the CFO, Choucair said.

“I keep going back to, the CFO cannot be ‘almost right,’” Choucair told CFO Dive in an interview. “I tell everybody that the CFO cannot almost be right, and we are responsible for mission critical processes that have zero- to near- zero tolerance.”

Shepherding safe and auditable AI

A provider of AI-enabled software to help finance teams automate the financial close process, Trintech has focused on incorporating AI both into its products and inside its teams. For both CFOs and Dallas, Texas-based Trintech, the need to ensure financial information is 100% correct and accurate at all times is a top focus, Choucair said.

AI has remained the “number one and number two topic” during CFO conferences or discussions between finance chiefs, Choucair said. Finance chiefs are bearing the dual responsibility of ensuring the business’s financial information is accurate, secure and compliant — while also being responsible for effectively utilizing the company’s resources and driving growth.

When it comes to AI, “there's budget to be spent, but I think the real issue is that, is it reliable? Is it part of the controls when the auditors come in?” he said. “Whatever function or process that's being done, is it really pristine? And can it be audited?”

Choucair has served as CFO for the financial close software provider since 2019, according to his LinkedIn profile. Before Trintech, he served as CFO for B2B marketing platform Multiview, a Warburg Pincus portfolio company, and began his career at Big Four firm KPMG.

Trintech has embedded AI throughout its product suite — introducing an AI assistant, Beacon, as well as adding agentic AI into its financial close processes, according to an April 9 press release. The technology can provide benefits such as AI-powered transaction matching, journal entry preparation and reconciliation narratives and documentation, according to the release.

With transaction matching or exception reporting, for example, the tool aims to help solve the challenge of the small percentage of data or information that automation historically fails to match, he said.

“They get kicked out, and they need a person to go track them down. What we're doing is creating an agentic agent to come in and actually solve those problems and then have a human in the loop,” Choucair said.

A marathon, not a sprint

Choucair has also used the technology within his own day-to-day tasks, he said; for example, tapping it to aid with a tax restructuring the company was mulling. The generative AI tool was helping in answering complicated tax questions, he said — with the business also having its tax advisors review its conclusions.

For finance leaders that may still be reticent to use AI, “I would just tell CFOs that the more you can use it, the better off you can be,” Choucair said. “But the other thing I would say is that, I've got all my lieutenants.”

Getting buy-in from team members regarding AI is critical to its effective use. Trintech has made “huge strides” with the use of AI by its own employees, Choucair said. Trinech initially started with ChatGPT and rolled out enterprise licenses across its teams early last year. After introducing the tools, Trintech’s employees quickly split into three groups, Choucair said.

The first were “very, very energetic and focused, and they were our number one cheerleaders” for the technology, he said. Meanwhile, the second group was “very concerned, because they maybe didn't have the skill sets to really benefit from it, and then you had a group in the middle that that needed a lot of training,” he said.

It’s important for finance chiefs to find the right balance between decisive action and moving too fast. The AI journey, especially for those first starting, “seems like it is a sprint, but it's really not a sprint,” Choucair said.

For those CFOs just starting their AI transformation journey, “I tell them, talk to your partners, talk to people that you trust and find folks in your core [group] that are really leaning into the AI,” he said.

Nearly half (45%) of business leaders say they would pay 11% to 15% more for talent with strong artificial intelligence skills, according to KPMG’s latest AI Pulse survey.

The research underscores how quickly AI investment has become a core corporate priority, with respondents projecting average spending of $207 million over the next 12 months — nearly double year-earlier levels.

“AI’s potential value is no longer in question,” the Big Four accounting and consulting firm said in a Tuesday press release. “However, realizing that value depends on how effectively and securely organizations can reengineer work at enterprise scale.”

The report comes as companies with large AI budgets face growing pressure to sharpen their investment strategies and demonstrate returns.

A recent Gartner survey found that CFOs rank acquiring and developing AI and digital talent for the finance function as their top near-term challenge.

“CFOs will find hiring AI and digital talent to meet their top challenge is not easy, and it’s expensive,” Mallory Bulman, senior director analyst in Gartner’s finance practice, said in a press release last week. “In the near term, they should focus on upskilling their existing workforce to close digital capability gaps and drive more value from the tools they already own.”

Despite growing confidence in AI’s potential, execution remains a challenge, according to KPMG. When asked to report primary return-on-investment barriers, nearly two-thirds of respondents (65%) cited difficulty scaling AI use cases, up from 33% last quarter, while 62% pointed to skills gaps, up from 25%.

Most business leaders (87%) are prioritizing upskilling and reskilling to build an AI-ready organization, while 68% are focused on hiring new talent, KPMG found.

The urgency is growing as deployment of AI agents reaches a tipping point, the report said. Just 11% of organizations were using them in early 2024, mostly in pilot programs. That figure now stands at 54%, signaling a rapid shift into production environments, according to KPMG.

“AI outcomes increasingly depend on workforce readiness,” Rahsaan Shears, aIQ program lead at KPMG, said in the Tuesday release. “The limiting factor isn’t the technology — it’s whether people have the skills to direct AI, apply judgment and take responsibility for results.”

KPMG polled 237 U.S.-based C-suite and business leaders representing organizations with annual revenue of $1 billion or more. The survey was conducted between Feb. 17 and March 17.

CFOs did not see artificial intelligence meaningfully impacting employment at their organizations last year, and don’t anticipate a large impact on headcount from the technology over the course of the current year — but are expecting a change in the overall skill makeup of their workforces, according to a working paper published this month by the National Bureau of Economic Research.

Finance chiefs are expecting the introduction of AI will lead to a decline in the number of “routine clerical roles” over the next three years, forecasting routine clerical employment to slump by 2 percentage points by 2028. Additionally, the share of technical roles, including those engineers, data scientists and analysts, is expected to grow during that period — rising by 0.62% in 2026, and 1.35% by 2028, the survey of 750 CFOs conducted by Federal Reserve Banks of Atlanta and Richmond economists found.

Notably, however, the paper also found lowering costs — including labor, as well as non-labor expenses — to be one of the least important motives for AI investments among current firms. “Overall, therefore, in the near-term, the goal of investing in AI is not to reduce workforce or costs but rather to improve productivity,” the research found.  

Dive Insight:

The study examined CFOs’ responses concerning how AI use has helped to improve worker productivity, asking finance chiefs to note a percentage change over the past year and the next two years.

For 2025, the mean labor productivity growth attributable to AI was 1.8%, the survey found. Over the course of this year, productivity gains are expected to “grow considerably,” with mean reported labor productivity expected to reach 3%.

“Labor productivity gains are positive, vary across sectors, and are expected to strengthen in 2026, with the largest effects concentrated in high-skill services and finance,” according to the research, which surveyed four sectors, including finance, manufacturing and construction, high-skill, and low-skill services.  

The company also asked CFOs to report a percentage change in revenue and employment due to their companies’ use of AI, and found that these “implied productivity gains” were also positive, with finance seeing implied labor productivity growth of about 0.8%.

The findings come as focus on AI skills, particularly within finance, is beginning to grow among employers and CFOs: a recent study by Datarails, for example, found approximately one in three finance job postings now explicitly reference AI skills, compared to one in four just a year prior, CFO Dive reported. Finding effective ways to implement AI inside of the finance team itself is also becoming a top priority for many CFOs: Hewlett Packard Enterprise finance chief Marie Myers told CFO Dive last month that her finance team is looking to do more with agentic AI tools.

Finance chiefs also appear to have a positive view of AI’s ability to bolster worker productivity in the future, though gains may not appear in the short-term, according to the survey. The study did identify what it terms as an AI “productivity paradox,” noting survey respondents consistently identified larger AI productivity gains “than those implied by contemporaneous changes in revenue and employment.”  

However, the research also noted that this gap is likely due to “delayed output realization and quality improvements that are not yet captured in measured revenues.

“More broadly, firms’ conceptual notion of productivity appears to extend beyond mechanical revenue-per-worker calculations, encompassing improvements in workflows, task efficiency, and organizational capacity whose revenue effects materialize only gradually,” the paper noted.

As they begin to integrate AI further into their systems and teams, the perspective around the technology among CFOs is also beginning to shift. Gone are the early days of experimentation and testing: today, the “smart, strategic CFO” is thinking about how they can tap AI to solve key challenges, Tabs CEO and co-founder Ali Hussain said.

For instance, one of the priorities often cited by potential clients of Tabs — an AI-powered platform for billing, collections and other parts of the invoice-to-cash process — is “we are scaling, and the systems we have cannot support that scale,” Hussain told CFO Dive in an interview. The business has hit a certain revenue threshold, and its current systems cannot catch up to handle simple processes such as the order to-cash process. Such CFOs are seeking areas where technologies like AI could best alleviate key pressure points.

“If something around spend management or reconciliations is working fine, I think they're just fine with it,” Hussain said of CFOs’ thought processes. “They don't need an AI strategy there. What they're looking for is, where is most of my pain?”

Stewards of technology                             

Today’s CFOs are looking for AI-driven solutions which can open up crucial bottlenecks in areas like the order-to-cash process. The greater attention to where AI can best be applied is due to a number of factors, Hussain said: first, CFOs are still navigating a shortage of qualified accounting talent — and the talent they do have is “just not interested” in chasing down payments, he said. Secondly, there’s “a lot of board pressure right now to keep these [finance] teams leaner than ever,” Hussain said.

Hussain co-founded the New York-based platform, which provides AI-powered revenue and reporting tools to finance teams, in 2023, according to his LinkedIn profile. Prior to Tabs, he served seven years as chief operating officer for IT services and consulting firm DOOR — formerly Latch — and held previous roles at Boston Consulting Group and Google.

Yet another factor influencing the strategic application of AI among finance leaders is the fact that many CFOs today “have to demonstrate that they are being good stewards of the new technologies in this world,” Hussain said.

Bringing in AI to make revenue reconciliation or accounting easier and more efficient can be a “big win,” therefore, as finance leaders are both looking to understand how to use such tools and under more pressure from their CEOs or boards to demonstrate to leaders in other areas of the business that they are “walking the walk” when it comes to AI’s use, he said.

In Hussain’s view, it’s also important to note that much of agentic AI’s current usage today comes down to user comfort, and not necessarily its technical capabilities. For instance, Tabs itself sees AI agent usage areas surrounding billing or collections “are more viable for our customers, and they're more trustworthy using that end to end, than necessarily starting with something more intimate and high risk like audit,” compliance or revenue reconciliation, he said.

 The turning point

Hussain does see 2026 as being a crucial year for AI within finance, he said. For companies such as Tabs, last year was a “massive capitalization moment” where businesses were able to raise large “war chests” of capital to build. In September, Tabs closed a $55 million Series B round led by Lightspeed, aimed at building out AI agents for billing and collections, according to a blog post at the time.

As such, “what I think is really exciting this year is, it's really going to be the year where the technology and also just the infrastructure we've all built is at a turning point,” he said.

That inflection point doesn’t mean the end of the finance team or of certain finance roles, Hussain said, but what it does change is the way CFOs “think about how they spend their time and where they need to try to attract labor on the functional level,” he said.

“I do think there are open questions around, at what point is there a turning point with these technologies where you no longer need a lot of those jobs to be done in the more tactical [finance] roles,” Hussain said, and the focus shifts to enabling those roles to add “value and impact on other parts of their team.”