Spend management innovation yields equity value

When Bristol Myers Squibb set out to overhaul its procurement function using artificial intelligence, it made a deliberate choice: move forward without waiting for perfect data.

“We’re getting our data right as we go along,” Rhonda Griscti, executive director of digital strategy and global process lead at the pharmaceutical company, said in an interview.

The gamble has paid off, resulting in a much faster process for soliciting and evaluating bids from suppliers, among other improvements, according to Griscti.

“We’ve gone from six to nine months on average to less than 30 days for our RFP [request-for-proposal] process,” she said.

Bristol Myers moved quickly with its AI-driven procurement transformation, even as data challenges have complicated deployments of the technology more broadly.

Just 10% of CFOs fully trust their data quality, and more than a third cite data trust as their top barrier to AI return on investment, according to a December 2025 report from global professional services firm RGP.

On the question of data readiness, Scott Rottmann, president of consulting services at RGP, takes a more cautious approach compared with Griscti.

“If the data is clean and of a quality that allows you to make good decisions, I think that's fine,” Rottmann said in an interview. “There’s an 80–20 rule here. You’re never going to be perfect at anything you do. So, you get 80% of the way there and sort of move forward.”

Data readiness debate

The differing perspectives highlight a broader debate in enterprise AI adoption: how much data preparation is enough amid pressure to deliver AI results quickly.

Skeptics of faster deployment caution that moving too quickly can introduce operational and governance risks if inaccurate, incomplete or inconsistent data sets are used to train or feed automated decision systems at scale. Proponents argue that earlier deployment can accelerate time to value, delivering measurable operational improvements sooner.

In an April Forbes article, AI strategist John Sviokla argued that organizations often over-invest in data cleaning and risk delaying value creation by prioritizing perfect data quality over extracting insights from imperfect data.

“You don’t sequence the cleaning before the processing,” wrote Sviokla, co-founder of AI advisory firm GAI Insights and an executive fellow at Harvard Business School. “Instead, you build the processing capability and let it reveal what is worth cleaning.”

Griscti said a key step is having a data lake — a centralized repository designed to store and process large volumes of raw data. “You don’t need your data to be perfect to start,” she said. “You just need to know where it is. Have it in a data lake. Be able to tap into it, and you can clean as you go.”

Griscti started at Bristol Myers in November 2021 as a senior director in R&D procurement and was promoted to her current role four years later. The procurement function was already under pressure to speed up its processes when she joined. Momentum for change accelerated after a new chief procurement officer arrived with a mandate to modernize the function.

The company launched a procurement transformation effort in June 2024 focused on standardizing processes, improving capabilities and eliminating fragmented, email-based workflows.

Rapid AI rollout

The next phase involved AI adoption. Griscti and her team conducted a “soft launch” in November 2024 with Palo Alto-based Globality, whose platform leverages AI to automate key parts of the process for finding and selecting suppliers. The technology was later scaled organization-wide in February 2025.

As a result, sourcing volumes increased sharply, with more than $1 billion flowing through the platform in the first year — far exceeding initial projections, Griscti said. The transformation also expanded procurement activity significantly, enabling roughly 10 times more RFPs while bringing previously outsourced work back in-house with about 50% fewer resources, she said.

In-sourcing freed up cash previously spent on external providers, which Griscti redirected toward technology investments.

Griscti said her advice to other executives leading similar transformations is to avoid getting bogged down by data concerns.

“People will argue with me about this, and I’ve had this discussion at many of the conferences,” she said. “But if you wait for perfect data, you’ll never get started.”

Coca-Cola is using artificial intelligence and scenario modeling tools to support marketing and commercial investment decisions as part of a broader finance transformation at the company, according to a presentation at Gartner’s 2026 Finance Symposium/Xpo.

Past investment decisions have been hindered by fragmented analysis, with different teams interpreting the same data in different ways, speakers said during a May 29 session at the event in National Harbor, Maryland. This led to extended cycles of discussion before decisions could be made, delaying responses to changing market conditions.

Meetings often focused on “coordinating follow ups, more analysis, more deep dives… and by the time the team would produce the answer, the market had moved,” said Shelley Kench, Coca-Cola’s global resource allocation lead. She was joined by Noah Museles, a partner at Bain & Company, which supported the transformation.

The effort reflects Coca-Cola CFO John Murphy’s broader focus on tightening how resource allocation is handled across the business.

“Resource allocation is not a new idea… however, we think there's an opportunity to gain even more leverage from it — something I think about as potentially our new secret sauce,” Murphy said at the Consumer Analyst Group of New York Conference in February, according to a transcript.

The consumer goods industry faces an increasingly complex and volatile marketing environment brought on by factors such as economic uncertainty, softening consumer demand and “an explosion of media touchpoints and purchasing pathways,” according to a February report by Boston Consulting Group.

The firm reported in early 2025 that three-quarters of marketing leaders at consumer goods companies viewed omnichannel investment allocation and activation as their biggest challenges. Of those respondents, 68% cited organizational silos and poor internal collaboration as particularly significant barriers.

At the center of Coca-Cola’s push is “Fuel Light 360,” the company’s proprietary platform designed to support teams across the business as they evaluate potential marketing and commercial investments.

Murphy highlighted the tool in his CAGNY conference remarks, saying it allows Coca-Cola to “hone in on what's going to deliver the most value for our buck, and it allows us to collectively think about how to improve our allocation of capital over time to support the business.”

Speaking at the Gartner event, Kench described a situation where the company faced increased advertising pressure from a competitor in the sugar-free soda category. In the past, teams might have debated whether to respond by increasing spending on television, digital media or in-store promotions, often without a common framework for evaluating the trade-offs.

She said Fuel Light 360 allows teams to model different investment scenarios in real time, helping shift discussions from competing interpretations of the data toward decisions about how to respond.

“What we've done is really moved from a meeting that's all about reconciling and arguing about whose data is right to making choices,” Kench said.

Museles said the effort was ultimately about reshaping the decision-making process, not just deploying new technologies. The result: what once required up to two weeks of follow-up analysis can now be accomplished during a single meeting.

“We redesigned the meeting, designed the tool accordingly for what the users needed in the meeting and then rolled them both out at the same time,” Museles said. The company “went from two weeks to one hour … in terms of the decision cycle.”

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By Brex

It's the last day of the month, and the finance team is staring down a backlog of incomplete expense reports, missing receipts, unanswered documentation requests and charges no one can identify. For most companies, that scramble is the routine. In a recent Brex survey finance leaders, 73% said their expense processes are too manual and one in three companies still runs on spreadsheets and paper. By common industry estimates, a manual expense report takes about 20 minutes and $58 to process, and nearly one in five contains an error that has to be fixed.

The root cause is traditional corporate spend management. It adds layers of controls: long approval chains, dense expense policies, paper receipts. This slows employees and finance teams down with excessive manual work. But a growing number of finance teams are choosing agentic finance, where AI agents handle the manual work that shouldn't exist in the first place so humans can redirect their time to the business.

They’re rebuilding backward-looking workflows, such as closing the books, reconciling cards and flagging out-of-policy dinners, around automation that enforces expense rules at the moment of purchase, audits spend continuously and reconciles in real time.

The impact is starting to show up at scale. Over the past year, AI-native spend platform Brex says it freed up more than $163 million in annual salary costs and saved customers over 208,000 hours a month by eliminating manual expense and accounting work. Today, the company says, nearly 70% of all expenses on Brex are handled entirely by AI.

From catching problems to preventing them

The clearest payoff is in AI handling the parts of the job no one enjoys: completing expense reports, chasing receipts, coding invoices, onboarding vendors and reconciling accounts. AI can read a receipt, match it to a transaction, code it to the right general ledger and check it against policy. All without a person touching it.

Instead of finance teams having to review transactions after the money is gone, automated controls and AI agents can block a non-compliant purchase before it clears, route an unusual invoice for review and approve the rest instantly. The finance team is freed to handle the exceptions that actually carry risk. On Brex, the company says, that lets managers review expenses up to six times faster. Results vary by organization and data maturity, but the direction is clear: routine finance work is moving off people's plates.

When agentic finance helps CFOs make the call

Forward-thinking finance teams are now advancing to automate not just tasks but also decisions. Brex argues that finance is well suited to this because its workflows are structured, rules-based and verifiable, which makes them reliable to automate at scale. The vision is to rebuild those workflows to be agentic from the ground up, creating a spend platform that doesn't just record financial decisions but helps make and execute them against a company's own rules.

That is taking concrete form today. Brex's Audit and Review Agents now handle much of the expense-review workflow: the Review Agent auto-approves low-risk expenses and follows up with anyone out of compliance, while the Audit Agent monitors transactions and categorizes potential policy violations by risk level. Both escalate exceptions to people only when human judgment is required.

Underpinning that globally is the payments infrastructure itself, which in Brex's case spans more than 200 countries and territories and 60 currencies, which makes automated, real-time spend control workable for multinational, multi-entity teams. 

The result is a system where spend policies enforce themselves at the point of purchase, compliance runs continuously and finance gains leverage without adding headcount, which, for most CFOs, is the part that matters. Learn more in this CFO guide to AI strategy from Brex.

Finance departments with successful artificial intelligence results are more likely than their peers to assign people to roles that directly support the technology’s scaling and execution, according to a presentation at Gartner’s 2026 Finance Symposium/Xpo.

“These organizations have realized that we need to have people specifically owning AI initiatives,” Marco Steecker, a senior director in Gartner’s finance research practice, said during a May 29 session focused on strategies for ensuring AI success in finance.

According to research presented during the session, finance organizations report that their AI initiatives succeed only about 50% of the time. To better understand why some organizations outperform others, Gartner segmented respondents into two groups: those with AI success rates below 60% and those above that threshold.

The analysis found that outcomes were less about spending levels or access to tools and more about how organizations structured AI execution. 

Below are four tips from the session on building successful AI teams in finance:

1. Assign a dedicated AI leader

High-performing finance organizations tend to establish a dedicated AI leader to accelerate AI-driven innovation and ensure alignment with the broader enterprise AI vision, Steecker said.

The role is responsible for defining how AI is delivered within the function, including the types of teams required and the processes used to develop and deploy tools. It also involves managing the organization's AI portfolio, allocating resources, and overseeing investments in both near-term AI initiatives and longer-term foundational capabilities.

Another key responsibility is driving AI adoption across the finance function. This includes overseeing AI literacy initiatives and supporting change management to ensure broader uptake of AI tools.

In terms of qualifications, organizations typically look for individuals with 10 or more years of business or technology management experience, along with at least five years leading multidisciplinary teams, as well as a background in operating model innovation and change management.

“You're not going to find many folks who have all of these boxes checked. But from my experience, the last two are much more important than the first two,” Steecker said. “I've seen a lot of people with enough ambition leap into these AI leader roles, provided that they have enough competencies around an understanding of how to innovate and how to drive change.”

AI leadership roles do not need to be defined by deep technical expertise, Steecker said. He suggested providing structured development time — about one day per week over three months — for the selected leader to complete AI training and build foundational understanding.

2. Build out your team with key execution roles

After installing an AI leader, successful finance organizations then begin to build a small, execution-focused AI delivery team that can grow as use cases scale over time.

An essential member of the team is the product manager, responsible for driving AI initiatives from end to end and accountable for delivery and outcomes. This role serves as the tactical counterpart to the AI leadership role, translating strategy into execution.

AI product managers spend significant time refining products and shaping development roadmaps by staying current on industry developments and collaborating on new ideas. They also coordinate closely with development teams through regular stand-ups, and continuously review usage data and end-user feedback to identify opportunities for improvement and guide iteration.

Another key component for the team is technical expertise, which may come from a data scientist for analytics-driven use cases or a developer or coder for automation-focused initiatives. The exact profile depends on the type of AI work being pursued.

3. Tap into in-house IT talent

When it comes to acquiring technical AI and data science expertise, successful finance organizations start by leveraging existing technical staff from elsewhere in the company rather than relying primarily on external consultants.

This “borrowing” model provides an immediate boost in capability and helps finance teams move faster by tapping into established expertise. It also brings three key advantages: stronger understanding of shared data systems, standardized methodologies for building AI tools, and familiarity with existing centralized governance and IT requirements.

By relying on in-house experts — at least in the short term — finance organizations can boost success rates by avoiding some of the technical hurdles they may face early on, according to Steecker. Hiring outside talent can be used as a longer-term strategy, he said.

4. Design for scale as AI adoption expands

As AI usage increases across finance, team structures tend to become more specialized.

Organizations often split responsibilities across product management, technical development, integration and end-user engagement. This helps manage growing portfolios of AI tools while maintaining alignment with business priorities.

The approach reflects a broader principle highlighted in the session: AI should not be treated as a one-time implementation, but as an ongoing capability that requires continuous oversight and refinement.

“It's far too easy to treat AI as a side-of-the-desk activity that we come to when we have some time to dabble,” Steecker said. “It's this kind of mindset that actually dooms a lot of AI initiatives.”

Companies struggling to generate meaningful returns from artificial intelligence often share the same problem: too many disconnected pilots and not enough focus on transforming core business processes, according to Neil Dhar, senior vice president and leader of IBM Consulting Americas.

Many organizations are now under pressure to move past AI experiments and toward a sharper focus on measurable outcomes.

The shift is forcing companies to take more disciplined AI steps. It starts with redesigning legacy business processes prior to AI deployment and having strong leadership to ensure real results, Dhar said in an interview.

“Otherwise, you're just doing science experiments, and you're not going to go very far,” he said.

CFO Dive: How have CFOs’ priorities around AI investment evolved in recent years?

Neil Dhar: If you think about the AI shift we’re seeing, it really came on the main stage just a few years ago. And the initial focus was clearly centered around education, because most people had no idea what it was all about, right? 

In the early stages, there was a lot of FOMO — fear of missing out — and companies were doing a lot of silo-oriented proof of concepts that really didn't go anywhere and had marginal return on investment. In many cases, they started racking up decent-sized costs while chasing the AI dream, so to speak.

But I think over the last six to nine months, there's been a lot more focus on return on investment, and the CFO is in the middle of driving that.

CFO Dive: Does AI introduce new ROI challenges for finance chiefs compared with traditional types of corporate investment? 

Neil Dhar: I think it's absolutely trackable, but I think you have to have discipline, just like you've had to have discipline in previous transformations. I think the companies that have discipline will be able to capture the value, communicate the value and reinvest the value.

If you look at IBM’s AI journey, for example, we identified workstreams that were critical to the company, where there were opportunities to drive real AI gains. And we made an effort to actually measure those gains, along with customer and employee satisfaction.

CFO Dive: Are some areas easier to measure than others when it comes to AI ROI?

Neil Dhar: I think there are certain things on the productivity side that are relatively straightforward to measure. For example, if it took X amount of time to do something, does AI allow you to do it in less time?

It’s always harder to underwrite growth, underwrite innovation. That's just a fact. 

We recently published the results of a CEO survey that had some really interesting data points. Eighty percent of executives expect AI to drive significant revenue by 2030, but only 24% know where it’s going to come from. So, just simply put, revenue's harder.

CFO Dive: Some surveys suggest that many companies still aren’t seeing meaningful ROI from their AI investments. What are the biggest mistakes holding them back?

Neil Dhar: First, I think, it’s because of leaders at the top who are not really oriented in a tech first-mindset; it’s farmed down to someone three levels down, and the executive team is not living it. Secondly, I think it’s because of an approach where you’re jumping into AI projects instead of first ripping your business process flows apart. I think those are the two most common errors.

You have to be willing to look at your processes from end to end and make sure you have strong leadership to actually drive real results. Otherwise, you're just doing science experiments, and you're not going to go very far.

CFO Dive: What role should CFOs in particular play in ensuring that AI investments generate real returns?

Neil Dhar: My view is very simple: The CFO is a steward of the company's assets. For every dollar of capital you spend as CFO, you’d better show a return, right? And, in my opinion, that return should be somewhere in the neighborhood of 2.5x to 3x. 

AI is no different. CFOs have to be really focused on making sure that AI journeys yield real, tangible benefits. 

The CFO, in many ways, is responsible for setting up the processes to track if you're making any difference. And the CFO is the one that is responsible for making sure that whatever is communicated to the board and investors is backed up by auditable data.

Newly appointed AlphaSense CFO Samantha Greenberg tries to dedicate at least 10 hours each week outside of her job to learning and honing artificial intelligence skills, she told CFO Dive. Greenberg took the top finance seat for the AI-powered market intelligence platform in April, according to a press release at the time.

That includes everything from doing workshops, reading research and building with the technology, and “making sure I am right on top of everything that is happening, how AI can be used for internal productivity, for business strategy, to run a truly AI-native finance organization,” she said in an interview.

A new CFO archetype

The 10-hour a week practice comes as Greenberg considers how finance leaders with decades of experience can continue to grow and hone their skills as new technologies disrupt finance and business models.

The “archetype of a CFO in the age of AI has changed so dramatically from what a [software-as-a-service] CFOs’ skills needed to be just a few years ago,” she said.  

Greenberg is joining New-York based AlphaSense with 25 years of experience in the technology business, having recently served for three years as CFO for identify verification company ID.me, according to her LinkedIn profile. Her tenure there ended last month.

She founded and served as chief investment officer for Margate Capital Management, which became the third-largest female-run hedge fund before it was sold to Citadel in 2019, with Greenberg taking a role as a portfolio manager for technology, telecom and media. In that role she honed her “abiding passion” for the intersection of data science and forecasting, she said. After leaving Citadel, she served as CFO for hospitality tech firm Mint House.

As AlphaSense CFO, Greenberg aims to channel her passion for data while leading the company’s capital markets strategy and financial operations as the company continues to rapidly expand, according to the press release.

Greenberg aims to to ensure the finance team gains new skills, she said.

Finance is “fundamentally a service organization” to teams such as product, engineering and go-to-market strategy, she said. Gaining new capabilities helps ensure “we're staying at the forefront of the skills we need to best serve those functions,” she said. 

Greenberg’s thoughts on the upskilling of her AI competencies aligns with her approach as a finance leader.

“I consider myself extremely hands on, more like a player coach, not just a coach,” she said. “I like to do the thing, not just coach the thing.”

For instance, she’s been in steady communication with the finance team at AlphaSense, making sure that anytime a report or analysis is filed, she’s sitting down with the function and “making sure I understand every line of it,” she said. Getting “deep in the numbers” is critical for mining the insights that generate revenue and profits and add value to an organization, she said.

Maintaining hypergrowth

Gaining a firm handle on AI skills is not only part of how Greenberg pursues her own professional development, but how she thinks about maintaining the pace of growth at the AI-powered market intelligence platform.

Describing herself as a seven-year “power user” of the AlphaSense platform, Greenberg has deployed the platform in her past CFO roles. At ID.Me, she used it to the point where “it had become a product that our teams couldn't do our jobs without,” she said.

The company has seen a spike in demand during the AI boom. In October it announced that it had surpassed $500 million in annual recurring revenue, according to a press release at the time.  

The company — valued at $4 billion after its latest funding round in June 2024 — also recently announced plans for further international expansion across the Asia-Pacific, Europe and the Middle East amid rising demand, according to an April 7 press release.

That expansion comes with its own set of hurdles, however. “The challenge for any business when you grow so rapidly is like every two years — if you're doubling every couple years, or even less than that, every 18 to 24 months, you have a brand-new company,” Greenberg said.

Overcoming that challenge requires hitting the right mix between innovation and sustainability when it comes to areas such as capital allocation. It’s important not to spend all one’s time on simply sustaining one’s offerings or on innovation which just drives incremental profits: “that's great, but you'll be leapfrogged” by competitors, Greenberg said.

“Conversely, if 90% of your R&D dollars and your effort and your time is spent on disruptive innovation, but you're not investing in the important sustaining things, to keep up with things that might be a table stake feature...you have also ceded a valuable market opportunity,” she said.

Similarly, in examining growth, it’s crucial to look at the right metrics, Greenberg said.

For a company like AlphaSense, which has low customer turnover and where users are “expanding with you,” the goal is to continue to invest in areas that will continue to generate value, “but thankfully, there are metrics that give you a lot of information as to whether that growth is efficient, and that's just as critical,” she said.

“Even though growth is more important than profitability, the efficiency of that growth is just as important a lens,” she said.

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.”

Partnerships are a critical way for businesses to drive material growth or create value — and in her role as chief partner officer for AI-powered financial close solution provider Trintech, Mekaela Davis is focused on doing just that.

That requires having a seat at the table across from other key business leaders like the CFO, and for those leaders to understand the unique perspective on key performance metrics a specialist on partnerships can bring to that table. That doesn’t mean that the KPIs surrounding partnerships necessarily differ from traditional metrics, but that a role like a CPO brings “maybe a little bit of a new flavor,” Davis said in an interview.   

“The CFO and the CPO really need to be in lockstep on that, because we have to make sure the CFO understands, ‘why are we looking at these things a little bit differently, and how are they nuanced?” she said.

Getting granular

Davis has served as CPO for the Plano, Texas-based provider of financial close solutions since 2024, according to her LinkedIn profile. Prior to joining Trintech in 2021 as VP of its global advisory practice, she logged an eight-year tenure at Grant Thornton in roles such as managing director, and has held various positions for companies in the SaaS industry including MarketSphere Consulting — an Oracle practice acquired by Grant Thornton — and Data Systems International.

The chief partner officer role represents one of the fastest-growing positions in the modern C-suite, with its rise outpacing the growth rate for roles such as the chief marketing officer or chief revenue officer, a 2025 blog post by Hockey Stick Advisory found.

Companies including Microsoft and Dell Technologies have appointed leaders to CPO roles in recent years, with Denise Millard having served in the CPO seat at Dell since 2024 and Nicole Dezen assuming the then newly-created CPO chair at Microsoft in 2022, according to their respective LinkedIn profiles.

Partnership-focused executives or CPOs “almost feel like we're running a little mini company inside of a company,” Davis said. “And we feel that way because it's important that we master; what are the KPIs and what are the core points for every other functional leader inside of the organization? We have to understand that.”

However, it’s equally important for the finance chief to understand how CPOs are approaching KPIs inside of the business. While standard KPIs don’t necessarily change, “the flavor changes a little bit when we're talking about partnerships,” Davis said. “And really what we want to do is get even more granular on some of the standard KPIs that you would track in a sales function, for example.”

For example, when talking about pipeline, “something that we'll do on the partner angle of things is that we'll get very specific about how that pipeline occurred,” she said. “So was it sourced by the partner? Is it simply being influenced by the partner? And if it's being influenced by the partner, how is it being influenced?”

Relieving partnership pain points 

Ensuring there’s an open dialogue between the CPO and the CFO can help alleviate many of the pain points or stressors that can come up as businesses move forward with new strategic initiatives or decisions. On the CFO side, for example, the unpredictability of external partners or third-parties can often be unsettling, “so when you have a strong and experienced partner executive by your side who's really mastered that pattern recognition over the years, it can make the CFO’s job a lot less stressful,” Davis said.

Additionally, on the partnership side, open communication with the finance chief is essential to ensure partnerships can continue to run smoothly. Many CFOs today are taking on the responsibility for purchasing decisions within their company: a 2024 report by software company G2 found 79% of finance chiefs have final authority surrounding software purchases, for example.

Oftentimes, the tools under consideration are solutions that are offered by the company’s partners, “so when at all possible, we really want to make sure that we're selecting vendors and solutions that we're already in partnership with and not their competitors,” Davis said. “And so having a really close relationship with the CFO helps you get to that conversation a lot more quickly, so that you can suss out that risk and prevent one of those major missteps that, frankly, can kill a partnership pretty quickly.”