Finance automation and value creation: A new nexus

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.”

Sponsored content

By Versapay

The conversation among finance leaders is changing.

On paper, the numbers look healthy: revenue is strong in many sectors, and sales teams are closing deals. But CFOs are still uneasy.

The issue is not how much revenue is booked but when the cash arrives, and that gap is quietly reshaping how finance leaders think and plan.

We surveyed 400 such leaders, and found that 69% reported increases in late payments. In higher interest rate environments where credit is expensive, that gap between revenue recorded and cash-in-hand forces CFOs to lean on credit lines to cover basic operating expenses. This reliance creates stress that ripples into hiring plans, capital expenditures, and borrowing decisions, with 78% saying accounts receivable surprises force strategic shifts.

This predictability problem starts at the bottom of the supply chain.

When customers delay payment, those delays cascade throughout the ecosystem, unless every business in the chain has deep reserves or easy access to credit. The surge in demand for lending and advanced funding products we’ve seen signals that businesses are reaching for tools to bridge the gap between booked revenue and collected cash because the cash isn’t showing up when expected.

Always-on collections is not a growth strategy

When teams cannot predict payment timing, they inevitably collect more fervently and frequently. Yet our research shows those efforts aren’t always fruitful or efficient, with 74% spending meaningful time chasing payments instead of serving customers.

Many inefficiencies contribute, like poor invoice tracking and cash application, but payments being primarily treated as a back-office function is the larger story. For a long time, sales closed deals, finance sent invoices, and anything that subsequently went wrong was considered a clerical issue. That model no longer holds.

Shifting customer expectations around payment methods, payment terms, and transparency in the invoice-to-cash journey have quietly elevated accounts receivables from an operational concern to a customer experience issue.

Missed or misapplied payments can represent a major crisis for customers. Trust erodes when invoice statuses are unclear, disputes move slowly, and reconciliation is manual. If customers cannot see what they owe, what has been applied, or why discrepancies exist, frustration follows and affects the entire relationship.

For finance leaders navigating this learning curve, it’s become clear that payments are a key driver of satisfaction and retention.

Automated receivables is the foundation

Efficiency has always driven accounts receivable investment, but new priorities have emerged: predictability, strategic control, and value creation.

The next phase of automation is more consequential, as 82% of finance leaders expect to increase investment in accounts receivable automation, with a greater focus on strategic control. The goal is to move beyond isolated automation, and connect the entire payment lifecycle, with every step flowing through a connected ecosystem.

With these steps linked, finance teams can build a receivables engine that reduces days sales outstanding (DSO), restores predictability through accurate forecasting, and gives stakeholders the necessary visibility.

AI plays an important role in this evolution, too. Siloed intelligence inside a single ERP may solve point problems, but won't address the root issue of data silos. A more effective approach involves AI agents communicating across systems, like an ERP and a payment gateway, so that data moves in sync. This frees teams from repetitive tasks like manual application and routine outreach, redirecting them toward the complex, relationship-building work that drives growth.

More than a third of finance leaders expect to adopt predictive analytics within accounts receivable in the near term, enabling earlier payment risk identification, more accurate cash flow forecasting, and more informed scenario planning. Ninety-one percent expect DSO to drop as a result of these investments. These improvements represent a fundamental shift in how payments can be managed.

Predictability is your competitive advantage

Eighty-one percent of businesses say collecting outstanding invoices is a challenge. This reflects something beyond payment behavior: it suggests a failure of transparency, communication, and process design across the entire invoice-to-cash journey.

Automation solutions that improve forecasting accuracy, lower collections costs, and protect customer experiences will be sought after. They will give finance leaders the confidence to stabilize cash flow, regain strategic clarity, and turn receivables into a source of competitive advantage, rather than a persistent source of risk.

Predictability is power. When customers feel clarity and transparency in the payment process, that builds trust, and when teams have the tools to anticipate, automate, and act, rather than react, they begin to shape the business environment that surrounds it.

CEOs for the first time see artificial intelligence as the biggest risk, exceeding the potential harm to their industries from geopolitical turmoil, cyber intrusions, and financial and economic instability, the Conference Board said Thursday.

In the face of challenges from AI, the share of CEOs planning to boost capital spending surged to 35% this quarter from 22% last quarter, the Conference Board found in a quarterly survey. At the same time, their confidence rose to the highest level since Q1 2025.

The increase in CEO confidence signals “restored optimism among leaders of large firms,” Conference Board Chief Economist Dana Peterson said in a statement. “CEOs’ expectations for their own industry improved further, progressing from mild cautiousness to solid confidence,” she said.

CEOs, CFOs and their C-suite colleagues confront a two-pronged risk from AI. If they invest too little in the emerging technology, they may underperform bolder rivals. If they invest too much, they may yield a meager return on investment, fall short of earnings targets and face investor ire.

“AI has tremendous promise,” Fed Governor Lisa Cook said Tuesday. “Nonetheless, I view its general adoption with caution,” she said in a speech.

“AI's emergence is poised to be the latest example of the creative destruction economist Joseph Schumpeter described almost a century ago,” Cook said, adding “we appear to be approaching the most significant reorganization of work in generations.”

While sparking innovation and creating new business opportunities, AI may also bring costs, she said.

“Job displacement may precede job creation such that the unemployment rate may rise and participation in the labor force may decline as the economy transitions,” Cook said. “This outcome could cause hardship for many workers and their families.”

In manufacturing, AI may follow the same pattern as electrification more than a century ago, generating sizable returns slower than forecast, according to Fed Governor Michael Barr.

“Within firms, there is evidence from the manufacturing sector that productivity follows a J-shape after technology adoption: adjustment costs lead to short-run losses before firms that ride it out are able to realize larger, longer-run gains,” Barr said in a Feb. 17 speech.

Sixty percent of CEOs at Fortune 500 companies ranked AI as the leading risk to their industry, an increase of seven percentage points compared with Q4 2025, the Conference Board said.

The perceived risk of AI edged out geopolitical instability and cyberrisks by one percentage point and four percentage points, respectively, the Conference Board said.

Seventy-one percent of CEOs said tariffs imposed by the Trump administration have pushed up costs for their companies, the Conference Board said.

Forty-four percent of CEOs have either passed on the costs of tariffs to their customers or plan to do so, while companies run by 27% of the CEOs absorbed the costs of the import taxes, trimming profits, according to the Conference Board.

Modern enterprise resource planning platforms — increasingly equipped with artificial intelligence and other advanced features — have the potential to deliver meaningful results for CFOs who are prepared to cut through vendor hype, according to a Gartner report announced Tuesday.

Gartner predicts that finance teams using cloud ERP applications with embedded AI assistants could see a 30% faster financial close by 2028.

“Cloud ERP finance applications will deliver additional automation, insight, and efficiency to finance functions in the near future by integrating machine learning, GenAI, and AI agents,” Mike Helsel, a senior research director in Gartner’s finance practice, said in the Tuesday press release. “However, realizing these benefits requires CFOs to navigate vendor hype, organizational change and the evolving economics of AI in the enterprise.”

Gartner forecasts that AI-enabled tools will account for 62% of cloud ERP spending by 2027, up from just 14% in 2024. Today, most CFOs are still in the early stages of adoption, hindered by challenges such as poor data quality, integration complexity and skills gaps, Gartner said. 

Adaptive analytics capabilities emerging in cloud ERP platforms will equip finance professionals with conversational, natural-language interfaces and “context-sensitive dashboards that quickly translate large amounts of data into clear, actionable insights,” according to Gartner’s release.

Other cutting-edge capabilities include AI-driven planning and forecasting features that allow organizations to respond more quickly to market changes.

“CFOs should insist on industry-specific features, transparent pricing, and reference-able customer adoption for AI tools, while investing in data governance and upskilling finance teams to maximize ROI and mitigate adoption risks,” Helsel said in the release.

Finance leaders should resist feeling pressured to find a quick fix to their ERP needs, according to Marcus Harris, a partner at law firm Taft Stettinius & Hollister, who specializes in drafting and negotiating enterprise software-related licenses and agreements.

Companies selling the system may try to inject a “false sense of urgency” into the process, Harris said during a virtual event hosted earlier this month by the editorial teams of CFO Dive and its sister publication CFO.com.

“You’ve got to play it smart,” he said. 

Generative artificial intelligence has transformed the merger-and-acquisition process in just a few years, cutting deal timelines by 10% to 30% and reducing costs by roughly 20%, according to McKinsey.

The technology helps companies identify M&A opportunities faster, including those driven by geopolitical shifts, supply chain disruptions and regulatory change, McKinsey said in a report this month outlining the dealmaking outlook for 2026.

“The many use cases for gen AI keep evolving and growing stronger,” McKinsey said. The time and cost savings figures cited by McKinsey were based on a survey conducted in February 2025, a spokesperson said.

AI is increasingly taking center stage in reports on M&A trends, both as a dealmaking tool and a driver of investments.

In early 2025, Bain & Company reported that about one in five surveyed companies were using generative AI in M&A processes, with more than half expecting to integrate it into their dealmaking by 2027.

“We expect that companies that master the use of generative AI in M&A over the next five years will identify targets faster than their competitors, underwrite more deal value with confidence, execute diligence and integration activities more rapidly with fewer resources and ultimately deliver higher M&A-assisted total shareholder returns,” the Bain report said.

Meanwhile, interest around acquisition targets in the fast-growing AI space helped fuel an overall spike in M&A activity last year, despite economic uncertainty, according to a December analysis by Big Four accounting and consulting firm PwC. During the 11-month period ending in November 2025, more than 20% of U.S. mega-deals valued at $5 billion or more had an “AI theme,” PwC said.

Overall, global M&A deal value grew to $4.7 trillion in 2025, up 43% from a year earlier and 20% higher than the 10-year average of $3.9 trillion, McKinsey said in its report.

Large transactions played a major role in the spike, as top companies absorbed the shock of geopolitical and trade challenges last year. The number of deals valued at $10 billion or more expanded to 60 — the most since the M&A peak in 2021 after the COVID-19 pandemic, McKinsey researchers said.

“Economic effects were lighter than anticipated, balance sheets remained strong, monetary policies lowered the cost of capital and the buzz around AI contributed to growing optimism,” they said.

Finance transformation will be a major priority for Hewlett Packard Enterprise CFO Marie Myers this year as her team looks to do more with artificial intelligence agents, focusing on use cases such as forecasting and accounts receivable processing, the executive said in a CFO Dive interview.

The team is scaling up its use of “Alfred,” an internal agentic AI tool that it developed with engineers at Big Four accounting and consulting firm Deloitte with the goal of boosting finance operations, Myers said.

After successfully piloting the tool last year, the company is now poised in 2026 to drive “far more use of agentic AI inside of finance,” she said.

The agentic AI push reflects a rising trend among corporate finance teams, according to Deloitte’s latest CFO Signals Survey released in January.

More than half (54%) of surveyed finance chiefs said integrating AI agents in their departments will be a digital transformation priority in 2026. “This likely reflects growing interest in advanced tools to augment workflows and decision-making,” the report said. 

For her part, Myers has embraced a “transformation with purpose” mindset, with a focus on using forward-looking, enterprise intelligence to drive growth, HPE and Deloitte said Thursday in a blog post highlighting their joint finance initiative.

Myers became CFO of HPE in 2024 after a three-year stint as finance chief of HP Inc. She previously held a variety of other positions at HP, including chief transformation officer, overseeing the company’s information technology and transformation organizations.

In her current capacity, Myers has prioritized greater technology adoption at HPE, according to the blog post. Beginning in 2025, she launched an effort with Deloitte to transform HPE’s finance operations “and pioneer agentic solutions that can reimagine how CFOs lead their organizations,” the post said.

AI investment race

HPE’s intensified use of Alfred comes as technology giants and Big Four firms are heavily investing in AI to beef up their offerings while experimenting with use cases internally.

Deloitte announced in March 2025 that it was launching a new suite of “ready-to-deploy” AI agents, dubbed Zora AI and built on technology from NVIDIA, for automating workflows across business functions, including financial management. The consulting firm envisioned its new agents being used to complete complex tasks and provide “on-demand insights, analysis, reporting, workflow automation, decision support and data sourcing,” according to a press release at the time.

As part of last year’s announcement, Deloitte said it was collaborating with HPE to offer a finance-specific Zora product to customers of HPE Private Cloud AI, with both companies planning to use the tool internally in the meantime.

The jointly developed finance tool, which is now referred to as CFO Insights, is cutting HPE’s financial reporting cycle by about 40%, while driving “more productive and targeted discussion around operational performance,” according to the Thursday blog post.

“By embedding agentic AI and generative AI into finance, HPE is demonstrating an approach many teams are still working toward: reimagining processes, moving pilots into production at scale, and generating real, measurable value,” Abdi Goodarzi, U.S. chief commercial officer for Zora AI at Deloitte, said in an emailed statement.

HPE affectionately named its own internal version of the tool “Alfred” after superhero Batman’s trusted butler, according to the post. Myers said Alfred is a platform that leverages both generative and agentic AI, bringing together data across key aspects of the company’s finance supply chain.

The tool allows people in finance and other parts of the company “to really interpret data and understand not just what is happening, but why it’s happening,” she said. “So, it’s a real time sort of interface for us on the key stats and performance of the company.”

‘Deterministic outcomes’

HPE worked with Deloitte and NVIDIA to ensure “deterministic outcomes” with Alfred, “meaning users get the same answer every time they ask the same question, according to the blog post.

“That might sound simple, but it solves a long-standing theoretical challenge in enterprise generative AI: making large-language models behave predictably in inherently non-repeatable LLM [large language model] environments,” the post said.

Myers’ deployment of Alfred started last year with a use case involving HPE’s regular operational performance review process.

“We chose really consciously to pick one of the most strategic processes in the company, which is our weekly ops call” that includes people from both finance and sales, Myers told CFO Dive. “Once a week, we come together on a Monday with 40 to 50 people who sort of really look at what’s happening with the business, and that call is the heartbeat of the company.”

Before the deployment of Alfred, “it was a very manual-driven, rear-vision mirror look-back call. We would spend literally days beforehand pulling together... about a hundred pages of PowerPoint … on what had happened in different aspects of the business.”

Alfred is capable of instantly analyzing data and generating insights, eliminating the need for tedious manual work and allowing the focus to shift from figuring out what happened to a more forward-looking conversation on “what we plan to do about it,” Myers said.

AI agents “now perform a lot of the analytical work that analysts were doing before,” she said. “So, analysts who were previously doing calculations around shipment data, shipment accuracy — those tasks are now performed by agents.”

Myers said the ops project gave her team “the chance to really position ourselves as finance helping to inspire and lead a broader enterprise transformation.”

She said the company is now looking to deploy Alfred on the “transactional side of finance” — from credit and collections to accounts payable and receivable processing.

“We’re in production right now with Deloitte on many of those initiatives,” the finance chief said. Forecasting could be the next major focus, she said.

For SAP Americas CFO Sonja Simon, artificial intelligence and its potential to change the way we work represents “a great opportunity to help get the finance organization ready and excited about what will come,” she told CFO Dive.

AI very much remains a “fast-moving train,” she said, with the technology continuing to evolve at a rapid pace and with much of its potential still left unclear or untapped.

When “there is such a dramatic change in what technology has to offer, it is only human to initially say, ‘Wow, what does it mean for me? How is it going to impact my job? Will I still have a job tomorrow? And if I have a job, then what will it look like?’” Simon said in an interview. “I feel, as a leader in finance, we really have to help the teams” work through that response, she said.

Getting comfortable

Strategically leveraging technology, including automation, is a critical part of how the Walldorf, Germany-based company approaches running finance. Over the course of her 18 years at the multinational software provider, SAP has grouped the tasks of its finance function by creating dedicated teams for different areas, such as human resources, compensation and revenue accounting.

The move has allowed SAP to become “very, very lean,” and to leverage technology: the finance team Simon oversees in her current role is comprised of 30 people and manages both the revenue and associated expenses of approximately 40% of SAP’s global revenue, she said.

Simon has held various roles during her near-two decade career with SAP. She was previously responsible for external reporting before being appointed to the role of CFO for Latin America and the Caribbean in 2020. She took her current role as CFO of SAP Americas in April 2025, which added the U.S. and Canada to her remit, she said. Prior to SAP, she worked for seven years at the Big Four firm Ernst & Young.

As AI and automation continue to evolve, it’s important for the finance function to become familiar with new tools. Within Simon’s team, “there are some small things that we do to ensure that people get comfortable” with AI, she said. That includes bringing AI up in team meetings  and asking for volunteers who are willing to talk about how they have used the technology lately, she said — whether in a professional or personal setting.

Simon also has an individual on her team that is “tasked with continually working on what is available for us within finance at SAP today, already, to ensure that we are really at the forefront of the adoption,” she said. As a global entity, there are numerous teams at SAP which are all examining how AI could be tapped within their functions, and it’s important that finance stays top of mind.

“We always want to make sure that they don't forget about us, so that…they ask us, ‘Okay, do you want to be the first user? Do you want to be the early adopter?’” she said.

Creating a ‘failure culture’

To drive early adoption of emerging tools and technology, however, it’s key to create the room for professionals to experiment and make mistakes. Simon said she is “a strong believer in creating a failure culture,” which can particularly be a challenge in finance. As the numbers crunched by the function make their way into the reports that govern executive decisions on, there’’s little room for error.

However, opening up space for financial professionals to experiment with new and emerging tools, and to make mistakes, is also crucial to driving creativity and finding new solutions, she said.

“The tricky thing here is you really have to celebrate failure, and you really have to make it stick, because it is only human,” Simon said of her approach.

For instance, Simon challenged the team at SAP to find better insight per deal for a key performance metric at the company: current cloud backlog, a KPI which is relied upon by investors and analysts to evaluate its cloud journey.

The team did come up with a tool that helps to simulate the impact on that metric, she said.While that tool may not give the right figure 100% of the time, “that is an example of where I say, ‘Okay, what you have put together is a lot better than what we had, which was, in that case, nothing,’” she said. “’And so the fact that this isn't 100% correct, keep in mind that you still improved the process significantly.’”