Finance automation and value creation: A new nexus

Cambium CFO Peter Coats aims to create a “North Star” for his C-suite colleagues by deploying artificial intelligence to build key performance metrics and strengthen other aspects of the company’s finance operations, he said.  

The San Francisco-based Cambium, a business looking to create a more sustainable marketplace for the wood industry, announced Coats’ appointment to its top finance seat on Aug. 25. The move is the company’s most senior finance hire to date and its first external C-suite hire, according to the press release.

“A key aspect of our strategy is to deploy new technology, AI, automation, in an industry that's ancient, and that that applies to the back office as well,” Coats told CFO Dive in an interview.

Making space for thought work

Cambium uses technology to better connect wood buyers and suppliers, and manufactures “Smart Wood”— a product line made from domestic salvaged and underutilized trees that can be traced via barcode. Coats is joining Cambium as the business targets “rapid expansion” of its platform, according to the press release.

His hiring comes shortly after the company announced it had acquired Connecticut-based woods product company General Woodcraft in July — its fifth acquisition in five years, according to a press release at the time.

As he settles into the top finance seat, Coats identified several top priorities on his to-do list, including bringing technology further into the finance team’s workflow.

“What I'd like to do is really build out a lot of the automation tools and deploy AI in ways that make our job a lot more fruitful and effective, in pursuing the fundraising as well as other FP&A activities that we need to focus on,” he said.  

A structural engineer by training, he left the field to pursue finance “really with the aim toward…using my analytical skills to drive impact for an organization that was doing well by the world,” he said.

Coats previously spent six years at Google in roles including head of strategic finance, where he managed financial strategy and planning for its “Other Bets” segment, including Waymo and Deepmind. His past experience also includes stints at Goldman Sachs and Wildcat Discovery Technologies, and he is joining Cambium after serving as CFO and co-founder of an unnamed startup in stealth, “focused on building financial infrastructure for the building materials industry,” according to the press release.

His time working with Deepmind at Google means he is fairly familiar with bringing AI tools into finance, and he is working to find the right balance between tech and talent in his first few months at Cambium. He has asked the team to figure out their day-to-day or month-to-month processes, and differentiate the tasks that can be automated from those that must be conducted by employees, such as filling out bank applications, he said.

“What I’d really love is for everyone to be spending as much time as they can on what I think of as thought work,” Coats said. “Part of that thought work is building out the automated tools and maintaining those automated workflows, but some of it is really important analysis and positioning for the company.”

Building out the North Star

Another of Coats’ top areas of focus as Cambium’s CFO is “building the North Star for Cambium leadership,” developing the tools needed so the company can execute on its ambitious strategy, he said. That entails using existing systems and resources and “pulling the data together that we already have flowing through our plumbing and delivering it in a way that's actionable,” he said.

For example, the business needs to further develop key performance metrics, both financial and non-financial, Coats said, and build out financial models so Cambium can isolate data points or external factors that could benefit the company, such as predicting the impact of changing wood prices. The company currently does not have that capability.

In the near-term, Coats aims to allocate the resources Cambium already has to ensure the business can achieve its goals, pointing to developing a “coherent and effective” capital strategy as an essential, he said.

Cambium secured an $18.5 million Series A funding round in 2025 led by VoLo Earth Ventures, according to a press release at the time. The company is working on lining up several different lines of debt capital, Coats said.

“One of my priorities is really just focusing on framing the value proposition that exists in a way that's digestible for investors,” he said.

Coats has been heartened by the enthusiasm shown by existing investors he has spoken to and has faith that the business will continue to receive support, he said.

AI is reshaping Mina Samaan’s budget season in a number of ways this year, including one that runs counter to the technology’s time-saving promises.

The CFO of the California-based lendtech startup BeSmartee, Samaan said he opted to kick off budget planning in May rather than in September or October because he wanted the extra time to gauge the impact of the AI tools everyone is using and get an understanding of their costs. 

“AI made us start earlier, the reason being over the last six to nine months everybody has been on an AI frenzy so there’s a lot of tools that are being used,” Samaan said during a recent interview in Oak Brook, Illinois, after a meeting of The CFO Alliance, a networking group for financial executives. “This has caused us to take a step back and look at the tools we’re spending on.”

The technology is also changing the calculations that are part of the company’s budget projections, because AI enables BeSmartee to develop new functions faster and so it changes previously held assumptions, he said. “Historically maybe you could come out with a feature or two, but now with AI you can come out with four or five,” he said.

While Samaan acknowledges AI is bringing exciting changes to finance teams, he views himself as conservative in terms of his approach to adoption and to tapping AI to help with developing the year’s budget. 

“Our finance organization is using AI and it has been tremendous, but it’s not something I can put my name on blindly,” Samaan said. “I’m helping my team adapt to it but I always tell my team you have to use it as a junior accountant or a junior finance analyst. You have to triple check it and modify it. It can speed up certain areas, however I can’t say I can trust it 100%.” 

Samaan, who has a bachelor’s degree in business administration and accounting from the University of Illinois Chicago, has been CFO of BeSmartee for about five years. Previously, he served as head of accounting and FP&A at LawnStarter and his experience also includes three years as an accounting manager at Groupon, according to his LinkedIn profile.   

For the first time this year, his finance team is using Anthropic’s Claude AI plugin to help develop the budget in Excel. Ultimately in coming years, he expects to explore using a full AI budgeting system as long as it offers SOC 2 data privacy safeguards, but he’s moving slowly toward full adoption in the budget process. 

With what he terms a “cautious eye,” Samaan currently uses AI for a number of elements of the budget, including the data capturing, validation and trend analysis aspects of the process. One of AI’s strengths is its ability to quickly understand the data you’re feeding it, so a trend analysis of top 10 customers or a margin analysis that would take a human analyst a day or two can now be done with the help of AI in hours, Samaan said.

“Analysts are prompting what they need to see done versus actually doing the formulas and going in there so they become more validators,” Samaan said.

So far the cost of the AI budget assistance has been manageable. Samaan sets up Claude so that he will get an alert once the bill for its use hits $500. Normally, that would pay for a month or two of use, but he anticipates the company might hit its limits sooner during budgeting season.

While he takes a frugal approach to AI that aligns with being a startup, he said the AI cost is currently “not a crazy number.”

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Finance leaders have never had more data available to them. Dashboards update in real time, reporting tools are more sophisticated than ever, and artificial intelligence promises to make forecasting more timely and reliable.

Yet many organizations continue to struggle with a surprisingly common challenge: confidence doesn't always translate into accuracy.

Recent research of 151 U.S. mid-market decision-makers found that 86% of organizations are mostly or very confident in their financial forecasts. At the same time, 65% report forecast variance of ±10% to ±15% when those forecasts miss expectations, and more than half identify cash flow surprises as the most significant consequence.

The gap between confidence and outcomes raises an important question for CFOs: if leaders trust their forecasts, why are so many organizations still experiencing surprises?

The answer often has less to do with forecasting models and more to do with the operational processes that support them.

Many finance teams continue to spend significant time reconciling data, validating reports, and manually assembling information from multiple systems. Research found that 68% of organizations spend more than 11 hours each week on manual reconciliation, spreadsheet maintenance, and workarounds. At the same time, 96% report recurring delays in business decisions because critical information is unavailable, inconsistent, or requires manual assembly before it can be trusted.

When data must be gathered from disconnected systems, financial forecasts become heavily dependent on assumptions, timing, and manual intervention. Teams may feel confident in the final numbers, but that confidence is often built on processes that are increasingly difficult to scale.

The problem becomes even more visible during the financial close process.The research findings show 91% of organizations require four or more days to complete their month-end close. For many finance teams, valuable time is spent validating data instead of analyzing it.

As organizations grow, these inefficiencies compound. New business units, acquisitions, applications, and reporting requirements introduce more complexity into financial operations. The result is a growing gap between how quickly leaders need answers and how quickly finance teams can reliably produce them.

This challenge is also shaping how organizations think about artificial intelligence.

Finance leaders increasingly view AI as a way to improve forecasting, automate reconciliation, strengthen financial reporting, and support faster decision-making. In fact, two-thirds of organizations surveyed are already piloting or actively deploying AI initiatives across finance and operations.

However, the research also revealed a significant obstacle: 58% of leaders identified data fragmentation as a significant or critical barrier to AI adoption. Only 6% described their data as clean, unified, and fully AI-ready.

This highlights an important reality. AI can accelerate insights, but it cannot solve underlying data quality and process issues. If information remains fragmented across multiple systems, the output will only be as reliable as the inputs.

As a result, many CFOs are shifting their focus toward operational readiness. They're examining how information moves across the organization, identifying manual bottlenecks, and looking for opportunities to improve visibility across finance and operations before layering on additional technology.

The research reflects this shift. The top priorities cited by decision-makers include improving real-time visibility, increasing forecasting accuracy, building the data foundation required for AI initiatives, and reducing the time and cost associated with financial close activities.

For finance leaders, the opportunity isn't simply to generate more forecasts. It's to create an environment where those forecasts are built on connected, reliable, and timely information.

Although 77% of finance organizations have deployed artificial intelligence, only 35% of them can confidently gauge the return on their investment, Protiviti found in a worldwide survey.

Measuring ROI is just one big AI challenge: Finance leaders for the third straight year identified ensuring data security and privacy as their top priority, according to Christopher Wright, head of CFO solutions and business performance improvement at Protiviti.    

“Organizations cannot scale AI without confidence in the quality, security and governance of their data,” Wright said in a statement. “That is the major reason why cybersecurity and data governance remain finance’s top priorities.”

The finance and information sectors are most vulnerable to AI-weaponized cyberattacks and four other AI risks posing the highest likelihood of causing the most harm to society by 2030, according to a survey of 272 AI experts worldwide by the Massachusetts Institute of Technology and the University of Queensland.

AI is especially powerful in recognizing patterns, generating code and synthesizing information, making it a prime weapon for cyberstrikes, according to the MIT/Queensland study.

When adopting AI, CFOs and other finance leaders need to prioritize cybersecurity, along with data governance, access controls, model oversight and privacy safeguards, Protiviti said.

Such detailed technical work is challenging amid a pell-mell corporate rush to AI.

Worldwide spending on AI will surge 47% this year to $2.6 trillion from $1.76 trillion in 2025, according to Gartner, exceeding the gross domestic product of both Canada and Australia. By 2030, AI investment will rise 120% more to $5.62 trillion.

Only 14% of finance organizations are deploying AI based on a detailed strategy, Protiviti found in its survey of 902 finance leaders.

Also, only 7% of CFOs and top finance executives say their organization focuses more on AI governance than speed of adoption, and 92% feel pressure to show that investment in the technology yields a decent return, Avalara found in a survey. Avalara is a provider of AI tax compliance software.

Among finance organizations, AI use for financial forecasting surged to 76% this year from 58% in 2025, making the task the No. 1 use case, Protiviti said.

With AI, finance teams can analyze larger data sets, perform more scenario planning and more quickly provide company leadership with insights, according to Protiviti.

Also, 67% of finance organizations are using AI-powered analytics to identify, measure and reduce financial risks more proactively, Protiviti found in the survey. Fifty-six percent of finance teams use AI to streamline repetitive tasks with the aim of reducing errors and increasing efficiency.

Measuring the ROI of AI “requires some unique approaches to the ‘R’ and the ‘I,’” Wright said in an email response to questions.

“The ‘I’ involves time, third party expenses and cost tracking,” he said, but “the ‘tokenomics’ has become a factor as organizations try to make sure they have a handle on who is paying for token usage on Copilot, Claude, etc.,” he said. “So there’s slightly less confidence in the ‘I.’”

The “R” in the ROI can be measured in improved accuracy and time or money saved, he said.

“But where AI improves a user experience, or results in more employee satisfaction in the finance department, that’s less objectively measurable,” Wright said.

Many CFOs do not attempt to justify AI spending on a standard ROI basis, he said.

CFOs are “mindful of the need to establish expectations up front so that measurement of success can have some rigor,” Wright said. “And they’re working, in real time, on ways to develop ROI approaches that satisfy them enough to support investment requests.”

Big Four accounting firm Ernst & Young is tying a dramatic improvement in its latest audit inspection results to a multiyear investment in technology, among other changes.

The Public Company Accounting Oversight Board's 2025 inspection of EY found Part I.A deficiencies, which indicate significant shortcomings, in only 5% of the audits it inspected, down from 28% in 2024. The 23-percentage-point improvement was the largest among the Big Four. The current rate marks a record for EY, according to the firm.

“We previously were at a rate that we believed was unacceptable, so we were very focused on improvements,” Richard Jackson, EY Americas assurance chief technology officer, said in an interview.

The inspection results came as the other Big Four accounting firms also reported lower PCAOB deficiency rates. Deloitte had a 5% rate, followed by PwC at 9% and KPMG at 13%.

EY had the highest deficiency rate among the Big Four in the previous inspection cycle. Its rate has now fallen to a level tied with Deloitte for the lowest among the four firms.

For EY, the turnaround was the “direct result” of a $1 billion investment in technology and talent to “increase audit quality, including expanded use of AI and advanced analytics, continuous learning, and shifting work so teams focus on the areas requiring the highest levels of judgment and insight,” the firm said in an emailed statement.

The technology changes focused heavily on standardizing and simplifying the audit process globally, Jackson said.

EY also worked to eliminate unnecessary audit steps and concentrate more closely on procedures tied to key risks, while investing in employee training and compensation.

The technology side of the transformation is entering a new phase, according to Jackson. The $1 billion initiative launched in 2024 included “traditional AI,” while the firm began introducing generative and agentic AI this year, he said. Those newer capabilities are therefore not reflected in the firm's 2025 PCAOB inspection results.

EY is using its newer AI capabilities for tasks such as project management, searching across audit files and reviewing the quality and consistency of audit documentation.

Looking ahead, the firm plans to use agentic AI to “help prepare audit work products,” while human auditors retain responsibility for reviewing the output and reaching conclusions, according to Jackson.

“You have to see AI as a support tool, not a substitute for humans or human judgment,” he said.

In addition to the spending on technology, EY has sought to invest in developing younger professionals. In 2024, it said it would increase early career compensation as well as funding AI-enabled audit and tax platforms and a new program dubbed 360 Careers, CFO Dive previously reported. This year EY also doubled the bonus that it gives early-career professionals who pass all four parts of the Uniform Certified Public Accountant Examination, boosting it to $10,000 for “June 1 joiners.” 

Artificial intelligence spending is becoming increasingly difficult for companies to forecast and manage, with unexpected costs forcing some organizations to delay, freeze or abandon initiatives, AI cost management firm Mavvrik found in a recent report.

Sixty-two percent of organizations surveyed said an unexpected AI cost materially altered a business decision over the past year. Among those organizations, 40% required board-level escalation, 33% implemented emergency spending freezes and 25% delayed or canceled an AI initiative, according to Mavvrik’s 2026 State of AI Cost Governance Report released last month in partnership with Benchmarkit, a software-as-a-service performance metrics firm.

“Companies are seeing significant AI cost overruns,” Sundeep Goel, CEO of Mavvrik, said in an interview. 

The findings come as AI costs are increasingly tied to the consumption of “tokens,” units of data processed by AI models that can quickly add up as workloads grow.

Mavvrik found 43% of companies cited token costs as a top source of unexpected Al spending.

In a July report, Gartner said token consumption is becoming a “meaningful component” of enterprise operating costs, while its connection to business outcomes often remains unclear.

The research firm said CFOs need greater visibility into how tokens are used, what value they generate and whether consumption is economically justified. Gartner recommends shifting from simply tracking AI costs toward “AI unit economics” that link token consumption to measurable outputs and outcomes.

The goal is not necessarily to minimize token spending but to “rightsize” it based on the value of the outcomes delivered.

A separate Gartner report projects that AI coding costs in particular will exceed the average developer salary by 2028, driven by rising large language model token consumption and the shift to consumption-based licensing.

Volatility in AI pricing is making cost estimates more difficult, according to Mavvrik’s study. Only 11% of organizations said they can forecast AI spending within plus or minus 10%, down from 15% in 2025.

Further complicating matters, “shadow IT” — where employees adopt technology without their company’s knowledge or approval — is also in the mix.

The Mavvrik report found that 98% of engineering organizations use AI coding assistants, yet only 42% include spending on developer AI tools in their AI cost reporting, meaning that many organizations are not fully accounting for these costs.

Goel said the findings reflect a lack of discipline around AI spending that is coming into sharper focus as costs continue to rise. “It's sort of perfect storm of wasted money,” he said.

Goel compared the phenomenon to the early days of cloud computing, when development teams could sign up for services using corporate credit cards without centralized oversight. “We’re seeing the equivalent here,” he said.

That can leave companies with fragmented, decentralized AI spending that is harder to track and control.

“There’s just no way companies can run a budget if there’s absolutely no controls on who can spend money on what,” Goel said.

Vague promises of artificial intelligence’s potential are beginning to wear on finance chiefs waiting to see that potential reflected in their companies’ balance sheets.

“We've been hearing it for over a year now, the skepticism of a CFO that will call us and say, ‘we're a year and a half into our AI journey, and I have nothing to show for it,’” Connor Augustyn, managing director and partner at consulting firm West Monroe told CFO Dive. He cited a conversation with a CFO at a client company who noted AI was a constant part of town halls at their business but that they could not point yet to the technology and say, “AI is making a difference in my P&L,” he said.

Sweeping generalizations about the return on investment AI could provide are failing to sway CFOs: today, “when we say ROI, you have to be very specific about what the ROI metric that you're trying to influence is,” Augustyn said in an interview. “If it is days to close, or it's your [days sales outstanding], you have to get more specific about what [key performance metric] you exactly want to track as an output of that AI use case.”

Establishing the KPI baseline

In Augustyn’s view, many executives and businesses are not taking as structured an approach as they should when it comes to AI spend, leading to frustrations down the line when they look for the quantifiable results of that spending. The AI use case needs to be “prescriptive and precise: it can't be a broad, ‘I'm going to throw AI at this big problem and hope it fixes it,’” he said.   

Augustyn has spent six years at West Monroe, and was promoted to his current role as managing director and partner in late February, according to a post on his LinkedIn profile. Prior to joining the Chicago-based company, he served as a manager for Clerestory Consulting and in numerous top finance and strategy roles at Conagra Brands.

Clients that Augustyn has seen have more success with their AI deployments have taken that disciplined approach, he said, which starts with asking if the existing vendors or technologies the business works with already can solve the use case where they are contemplating adding AI.

For instance, if one’s ERP provider has already begun to roll out AI-powered solutions, CFOs which have already procured that tool need to ask, “can you just get more out of the tools themselves?” he said.

Identifying the “KPI baseline” or specific metric they are trying to influence is a crucial part of that more disciplined AI strategy as well, Augustyn said.

“That's the piece that I think a lot of CFOs are getting frustrated about because either the KPI that they chose was wrong or they didn't even choose a KPI,” he said. “As they headed down the AI path, they were just getting immensely pressured by their board and the rest of the C-suite to do something about AI and here they are a year later, and the board's asking where the savings are.”

Pressing pause

For CFOs who are still waiting to record quantifiable results from their AI integrations or spend, Augustyn advocates for pressing the pause button.

It may not be necessary to completely rehaul one’s AI strategy, but “if the direction that I've been giving you is every quarter, I'm running a competition to see who comes to me with the best AI use case, and I'm five quarters in, and nobody has given me a good AI use case, we're pausing, and we're going to be taking a different approach,” he said.

That could be as simple as shifting to examine AI through a “value chain lens,” he said, or breaking up one’s processes to see where the best opportunity to use the technology lies — whether that’s in order to cash, financial planning and analysis, or working capital.

A good way to shift one’s strategy may also be to start with the “boring,” but tried-and-true areas where AI has had a definite effect, such as bringing into invoicing or reconciliations. That can help leaders  “prove to the organization that, hey, AI, can influence how we operate differently,” he said.

Pressing pause now to be able to identify critical use cases and better track AI ROI is essential, especially as AI and all its unanswered questions aren’t likely to disappear any time soon.

“No CFO is coming to me today saying, AI, I'm not even going to worry about it anymore,” Augustyn said. “Every CFO has to care about it because every shareholder is demanding that they care about it.”

The continued evolution of artificial intelligence is going to “fuel a surge of new AI driven roles,” and “every new job that's created by AI is going to require a baseline of AI and cybersecurity literacy,” Sysdig CFO Karen Walker said.

“There's a lot of considerations around security of data, just the overall AI pipeline, so I think it poses a new challenge for companies and their employee base on just being able to have a certain level of AI and cybersecurity literacy,” Walker told CFO Dive in an interview.

From investment to enablement  

It’s important for CFOs and their fellows in the C-suite to keep a close eye on how AI’s integration into their businesses could change the way their companies operate, Walker said.

“Companies are definitely trying to embrace this, but how do they do it safely, and make sure that all their use cases are secure?” she said of how leadership is thinking about integrating AI into their workflows. That includes both carefully choosing which AI tools — out of an expanding list — to invest in and then, crucially, ensuring one’s employees have that baseline level of competence when it comes to how to securely use AI, she said.

Walker has served as finance chief for the San Francisco-based computer and network security firm since 2021, according to her LinkedIn profile. Prior to Sysdig, she served as CFO for mid-stage tech company Bungalow, and her previous roles include serving as SVP of finance for PagerDuty and as VP, chief accounting officer and treasurer for Pandora.

The need to come up with an effective and secure game plan for AI’s usage comes as the AI industry itself continues to expand. This creates more choice and more competition for businesses that are eying the technology’s potential.

A growing fear of “missing out” on AI prompted a surge in mergers and acquisitions last year, for instance. CFO Dive previously reported that the aggregate value of AI-driven M&A deals in the U.S. reaching $107.9 billion in 2025 — a 9.7% year-over-year change and an 80% jump from 2022 levels.

Picks and shovels  

The 2026 M&A landscape is “off to a strong start, and has all the signs of continuing at the same clip,” Walker said.

“A lot of the companies that flooded the market with point solutions, I think many of those will have the opportunity to be acquired,” she said.

When it comes to AI companies, today’s investors are somewhat conflicted, she said — the “growth at all costs” mandate that fueled technology investments in the years prior to the COVID-19 pandemic has mellowed in recent years, with the pendulum swinging some back to profitability.

AI behemoth OpenAI, for example, completed its shift to a for-profit entity in October — leaving Microsoft with a 27% stake in the ChatGPT maker, as well as rights to its AI models through 2032 — and still faces heightened scrutiny from industry experts and leaders.

While AI technology isn’t likely to fade away any time soon, the question of how to best structure the business model for an AI company remains open-ended, according to Walker. As such, today's investors are “looking a little bit more closely at fundamentals, but probably not with the same lens as they are for other industries,” Walker said. They’re asking what about an AI-first company’s technology makes it competitive: if it’s “just a wrapper around a chatbot,” or the company doesn’t have its own intellectual property or differentiated data at this point, it’s not compelling for investors, she said.

Walker sees investors flocking to companies that craft the infrastructure to support AI, she said. 

“I like to think of them as, if we're in a gold rush, these are the folks that are making the shovels and the picks,” she said. “They don't have to necessarily bet on what large language model is going to be a winner, or kind of what that market share looks like.”