The CFO Strategy for Artificial Intelligence

In case you didn’t already know, AI is here to stay.

According to a broad new survey of 2,360 senior executives worldwide, companies expect to spend approximately 1.7% of their revenue on AI investments this year. That would more than double the 0.8% average for 2025.

Every industry tracked in the study, conducted by Boston Consulting Group, plans to increase its AI spending, led by technology companies, which expect to allocate 2.1% of their revenue to it.

What’s more, companies are willing to be patient. Only 6% of the executives said they would pull back on AI investments if current initiatives don’t pay off in 2026.

Of course, significant concerns about using AI remain, although the top ones seem to be receding a bit. Just more than half (53%) of those polled cited data privacy and cybersecurity as a top-three risk, although that was down 12 percentage points from BCG’s similar survey a year ago.

Similarly, 41% placed lack of control or understanding of AI decisions as a top-three concern, down from 48% last year. Regulatory and compliance challenges declined from 44% to 39%.

At the same time, some other concerns are gaining ground. Technology failure was a top worry for 38% of the surveyed executives, 6 percentage points more than last year, and the environmental impact of AI leaped to 17% from 7%.

Another takeaway from the research is that transforming companies through AI has moved from a CIO-led initiative to one directed by CEOs. Almost three-fourths (72%) of the 640 chief executives who participated in the survey said they are the main decision-maker on AI in their organization — double last year’s proportion.

In fact, half of the CEOs said they believe their job stability depends on getting AI investments and strategy right. And they’re not alone in that plight: 60% said they believe that by 2030, more than half of the roles in their organization, including the C-suite, will be gone or transformed by AI.

However, there is a clear geographical divide: CEOs in the East are much more confident that AI will pay off than are their Western counterparts.

Additionally, many more Western CEOs said they feel pressured to act on AI or risk falling behind, compared with those on the East side of the globe, who were much more likely to be driven by confidence that AI will deliver an acceptable ROI.

What’s the ROI of AI?

That’s the question dogging finance and HR teams around the world. Even as tech giants tout the time-saving potential of large language models and companies continue pouring more money into the nascent technology, its purpose in the finance function is still to be determined.

Now, new data indicates that in some cases, the technology is forcing workers to spend hours fixing faulty outputs.

That’s one of the key findings from a new report released last week by enterprise cloud software provider Workday.

Though 85% of the workers surveyed said they’re saving between one and seven hours a week using AI tools, they’re also losing some of that time correcting or validating the technology’s outputs. By Workday’s calculations, 37% of the time employees saved using AI tools was lost to “rework,” which the company defined as “correcting, clarifying or rewriting low-quality AI-generated content.”

“For every 10 hours of efficiency gained through AI, nearly 4 hours are lost to fixing its output,” Workday officials wrote in the report. The company described this as the “AI tax on productivity.”

Workday surveyed a total of 3,200 respondents, with half comprising leaders and the other half employees. The company partnered with Hanover Research to conduct the online survey, which was administered in November.

The report, much like a similar study issued by Harvard Business Review in September and another by MIT in July, calls into question the long-heralded proposition that artificial intelligence will free people up from mundane tasks and enable them to focus on higher-level, strategic endeavors.

As Workday’s report puts it: “Instead of reallocating time toward judgment, creativity and decision-making, employees spend it correcting low-quality output. At scale, this pattern compounds, translating into millions of lost hours each year in large organizations.”

If using AI tools requires some unanticipated additional labor, it probably doesn’t come as a surprise that the most frequent users “feel the most strain,” as Workday put it in the report. Among workers who use AI every day, 77% say they review AI-generated work “just as carefully as work done by humans, if not more,” according to the report.

For business leaders, Workday’s report suggests “rethinking how AI productivity is measured” by “accounting for both time saved and time lost to rework.” Contrary to the steady drumbeat to adopt AI at all costs, Workday’s report calls instead for shifting focus away from simply “accelerating usage and more on improving how AI-supported work is measured, supported and designed.”

In an email to CFO.com, Andrew Kershaw, Workday’s group general manager for the office of the CFO, noted that such an approach “should not be about slowing down AI adoption.”

“It should be about protecting ROI and managing risk by changing how the work is set up,” he said. “Many organizations are introducing AI into finance without redesigning the work around it. When that happens, your most engaged people end up doing two jobs at once: using AI to generate more output and then spending a lot of time validating it. It feels like a burden because structurally, it is.”

The report also recommended that leaders update job descriptions to “clarify where AI is expected to assist, where human judgment is essential and how success is measured.” Almost nine in 10 of the organizations surveyed said that fewer than half of their roles were updated to include AI-related skills, according to Workday.

“AI has been layered onto roles that were never updated to accommodate it — forcing employees to use 2025 tools within 2015 job structures,” the report stated. “Rather than reducing effort, this mismatch often increases it, as employees are left to reconcile faster production with unchanged expectations around accuracy, judgment and accountability.”

As Kershaw put it: “AI only delivers real value when people know how to use it well.”

“It’s not enough to stand up new tools and expect productivity to follow,” he said. “When finance teams are trained to frame the right questions, validate outputs, and interpret insights, AI stops being a gadget and starts becoming a force multiplier.”

And while most leadership respondents (80%) agreed with the notion that businesses should reinvest productivity gains into workforce development, their actions to date don’t fully reflect that belief. Per Workday, organizations are allocating about 39% of cost savings to “technology and infrastructure” compared to 30% to workforce development

“Time savings follow a similar pattern: leaders prioritize increased work volume and digital infrastructure over employee development,” Workday officials said.

Editor’s note: This story was updated Jan. 22 to include additional comments from Workday’s Andrew Kershaw.

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Cash flow depends on how fast — and how accurately — money moves through accounts receivable. But for many finance teams, AR is still weighed down by manual processes, late payments, and limited visibility. AI-powered automation is changing that, turning AR into a smarter, more proactive driver of cash flow.

Instead of reacting to overdue invoices, AI analyzes payment history and real-time behavior to predict which accounts are likely to pay late — and when. That insight allows teams to intervene earlier, prioritize the right accounts, and reduce Days Sales Outstanding (DSO) before problems pile up.

AI also transforms collections. Machine learning helps teams focus outreach where it matters most, while generative AI enables personalized reminders at scale — improving response rates without adding work. The result: faster collections, better customer experiences, and less time spent chasing payments.

On the operational side, AI brings accuracy and speed. Automated invoice processing and anomaly detection reduce errors, speed reconciliation, and minimize disputes that stall cash flow. Teams spend less time fixing issues and more time driving results.

Finally, AI improves forecasting. By combining historical data with live insights, finance leaders gain clearer, more reliable cash flow projections — and the confidence to plan ahead.

Smarter AR isn’t about working harder. It’s about using AI to get cash in faster, with less friction and more certainty.

At this point, it may be unsurprising to learn that finance professionals see AI and generative AI as the force that will exert the greatest impact on their work over the next five years. 

Perhaps more eye-opening is the predicted size of the gulf between AI and the next big, change-making forces​​​​​​, judging by research from Thomson Reuters. The legal services and content conglomerate, which incorporates AI in many of its products, polled 2,275 global professionals across the risk, compliance, tax, accounting, audit, global trade and legal functions.

Among those surveyed, 44% said the rise of AI and generative AI will have a “transformational” impact on their profession over five years, and an additional 36% said the impact will be “high.” 

By comparison, just 22% of respondents forecasted that the velocity of change in the regulatory environment will have a transformational outcome, and 20% said so about the explosion in data volumes. The impact is expected to be “high” for 37% and 41% of those respondents, respectively. 

Survey respondents also projected that professionals using AI will save five hours weekly within the next year, up from four hours as predicted in a similar Thomson Reuters survey a year ago. The five saved hours per week will unlock an average of $19,000 in annual value per person, according to the study results. 

More meaningfully for the broad financial impact of AI and generative AI, 81% of organizations that have a specific, tailored plan for AI implementation are already experiencing ROI from AI, according to the research. That’s about 3.5 times more than the 23% of organizations with no significant plans for AI adoption, where AI usage is having such an impact. 

Despite the benefits, however, as of this spring, only 22% of surveyed organizations overall had such a specific, tailored AI strategy, and about four in 10 were adopting AI without an overall strategy.

“Professional work is now being shaped by AI, and those who fail to adapt risk being left behind,” said Thomson Reuters CEO Steve Hasker in a news release. “By developing a strategy to drive both AI adoption and redeploy the productivity gains … organizations will achieve sustained innovation, greater operational excellence, and revenue growth.”

It should be noted that while 80% of the survey respondents expect AI and generative AI to have a transformational or high impact on their work over the next five years, most don’t foresee huge immediate movement toward adoption. 

Only 38% of the professionals said they expect to see transformational or high levels of change this year. About three in 10 said their organizations were moving too slowly on AI adoption

Artificial intelligence is now a part of everyday life and is making its presence felt more and more in the business world. However, akey question for finance and technology leaders looms: are companies, employees and executives really prepared for AI?

Deploying AI and machine learning applications can mean a lot of changes and the introduction of risk, and enterprises need to be ready.

“While organizations see the transformative potential of AI, executives and employees often struggle to prepare for its workplace integration,” said Rajprasath Subramanian, principal enterprise architect, business and technology innovation, at enterprise software company SAP.

“This is largely due to a lack of comprehensive understanding and training about AI capabilities, especially given the significant advancements in areas such as agentic AI and large language models,” Subramanian said.

In addition, there is a prevalent concern regarding job displacement due to AI adoption, leading to resistance or apprehension among employees. He mentioned in detail how this fear can hinder proactive engagement with AI tools and limit opportunities for upskilling.

Subramanian advised CFOs to stay aware of the rapid advancement of AI and how it often outpaces organizations' ability to provide necessary training, leading to a potential skills gap.

AI can be a disruptive technology, and that naturally presents challenges for companies. A survey of 3,450 C-suite leaders and 3,000 non-C-suite employees conducted by IT and business services firm Accenture found that many C-suite leaders and employees anticipate change will continue at a high pace in 2025 and both groups feel less prepared to respond to it than they did a year before.

More than half of C-suite leaders (57%) said they feel their company is not fully prepared. And while 2024 was the year of generative AI, Accenture said, after 12 months of rapid adoption only half of C-suite leaders say their organizations are fully prepared for technological disruption. Only 36% say they have scaled generative AI solutions.

“Most companies lack a common AI foundation, making it hard to balance the right speed with the right controls that an enterprise needs to move to scale,” said Lan Guan, chief AI officer at Accenture.

Guan mentioned how nearly one-third of the C-suite executives surveyed by his organization said limitations with data or technology infrastructure is the biggest hurdle to implementing and scaling gen AI.

“Many CIOs are still hesitant to deploy and scale new AI tools because AI costs are a moving target,” Guan said. “With breakthroughs happening every week, AI can quickly become the new source of technical debt, and the abundance of choices can be overwhelming and can paralyze decision-making.”

He added companies need to customize AI with their specialized data, “and most companies are struggling to find easy ways to do this.”

When asked about what might cause a lack of preparedness for AI and what factors contribute to an organizations’ ability to get the most value out of AI, Guan said it comes down to the investment strategy and the implementation process.

“Generative AI is projected to improve productivity by more than 20 percent over the next three years, so a lack of preparedness for gen AI means lost productivity and failure to achieve meaningful ROI on the investments companies are directing to gen AI,” Guan said. “Companies lagging in AI adoption and proficiency may find it challenging to compete with industry peers who have effectively harnessed AI for innovation and decision-making.”

In addition, a workforce unprepared for AI might struggle to adapt to new workflows, resulting in disruptions and decreased productivity during the transition period. “Without proper understanding and training, employees may not fully leverage AI tools, leading to suboptimal performance and missed opportunities for efficiency gains,” Subramanian said.

Another potential setback is the impact on employee morale and retention. “Fear and uncertainty about AI's impact on job roles can lead to decreased employee morale, engagement, and increased turnover rates,” said Subramanian.

Devopling an AI strategy

CFOs can take steps to help prepare their organizations for the broader use of AI.

One is to develop a clear AI strategy. “CFOs should collaborate with other C-suite executives to establish a comprehensive AI strategy that aligns with the organization's overall vision and mission,” Subramanian said. This involves identifying areas where AI can add value, setting realistic adoption timelines and allocating necessary resources.

Another step is to invest in employee training and upskilling. “To effectively bridge the skills gap, CFOs must champion initiatives focused on developing employees' AI literacy and competencies,” Subramanian said. “Pharmaceutical companies like Johnson & Johnson have implemented mandatory generative AI training for over 56,000 employees, ensuring their workforce is equipped to integrate AI into various business processes.”

CFOs also need to ensure their organizations have robust data governance and infrastructure. “Effective AI implementation relies on high-quality data and the infrastructure to process it,” said Subramanian. CFOs should work closely with chief data officers and CIOs to establish strong data governance practices, ensuring data accuracy, security, and compliance with regulations.”

Subramanian stressed CFOs should invest in scalable IT infrastructure to support workloads. “For instance, companies are advised to have a robust IT infrastructure in place to process large volumes of data, as larger datasets require greater computing power,” he said. “By proactively addressing these areas, CFOs can facilitate a smoother transition into AI adoption, positioning their organizations to fully capitalize on the benefits of AI technologies.”

To access the value of AI, companies should rethink work processes Guan advised. He added that CFOs should build a strong digital core that is dynamic rather than static, and to collaborate in building a talent pipeline and development system that relishes opportunities to explore with generative AI.

To support this, Guan said CFOs must take a multifaceted approach. “CFOs must encourage their organizations to build a strong technology foundation, enforcing data governance and operationalizing AI to drive scalable, high-impact outcomes,” he said.

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Today, most leaders agree generative AI will benefit their organizations, and early research shows that AI adoption has been broad and swift across different sectors of the economy. In one new international study, for example, 76% of organizations said that they were already using AI in some capacity and this number is expected to grow.  

Exact estimates about the benefits of AI vary, but the World Economic Forum suggests it may add over $15 trillion to the economy. McKinsey, meanwhile, is much more conservative, and projects that AI will generate anywhere between $2.2 and $4 trillion per year in corporate profits. Even though estimates vary by trillions of dollars, the financial benefits of AI are expected to be immense. Profits will grow as companies learn how to maximize the value of their AI investments.

However, as more companies and more CFOs venture into the world of AI, decision-makers need to understand what realistic outcomes look like in the short term and the strategies needed to support long-term success. AI comes with an unavoidable upfront cost, and it will likely take time to realize its full benefit, but, with proper preparation and enablement, it’s a worthwhile investment that CFOs should champion.  

Invest early and manage expectations

According to MIT Sloan, over 90% of companies have found a way to make money with AI in 2023 already, but we’re far from seeing game-changing revenue growth. That’s why CFOs need to manage expectations, including their own. Everyone in the C-suite (and those responsible for or those heavily scrutinizing the profitability of an organization) must remember that just like any other business investment, AI needs time and support to fully pay off.  

One way to help manage expectations is by identifying an ROI timeline. That way, you can keep your C-suite colleagues, employees and shareholders if you’re a public company informed as to when you expect to see the most tangible benefits from this technology. 

Engage employees and invest in training

While many organizations have invested in AI, for example, buying Copilot for Microsoft 365 licenses, only 46% have offered employee training resources to support AI use and implementation. This suggests that many organizations may be underprepared to implement AI despite their desire to adopt it quickly. That’s why AI adopters need to train their employees and invest in engagement strategies to get their workforce up to speed on the new technology.    

To ensure that your AI is worthwhile — because it’s not cheap — your organization needs to prioritize upskilling your people before, during and after implementation. As the CFO, you control the budget, but not always the implementation or deployment of certain resources. In the case of AI, it’s imperative that you work with departmental leaders to make sure AI engagement is encouraged and employees have the right training to succeed. The bottom line is you can’t just invest in software; you must also invest in support to get the job done right. With enough time and training, AI can change the way that your company operates. 

Take stock before investing further

On top of current investments in AI, a new report found that more than 80% of organizations are planning to increase their AI spending in the future, with 60% of organizations reporting that they plan to allocate at least a quarter of their technology budget to AI in the next five years. 

While the enthusiasm is great, I encourage all leaders to make sure that their users are getting the most benefit from this technology. Measure usage against your goals so that even if you do not see a direct correlation to profitability or revenue growth, you can see productivity gains. If certain users are not using their licenses, reassign them. It’s all about making the most of what you have first, proving the value and then making calculated decisions from there.

Ultimately, there is no disputing the huge potential for AI and why CFOs should get behind the investment. As you get ready to accelerate your AI strategies, remember to be patient, invest in training and enablement and measure success early and often. With those three elements in mind, your AI investment will pay off — even if it takes time.

The following is a guest post from Ankit Chopra, director of FP&A at Neo4J. Opinions are the author’s own.

Artificial intelligence is moving beyond experimental pilots or isolated use cases toward reshaping how companies operate, compete and grow. The investments in this space are rapidly becoming a core consideration in capital planning. For financial leaders, AI now represents a significant investment, and their teams are increasingly tasked with distinguishing hype from financial reality.

AI holds immense potential, but realizing its true value demands a new level of financial discipline, precise metric alignment and robust risk management. A finance executive's role is to ensure every dollar spent on AI propels the organization closer to measurable and scalable business outcomes.

AI offers undeniable revenue upside. Embedded in digital products, it enables smarter personalization, faster customer response and more predictive sales cycles. Yet, projected revenue gains can often be overstated or disconnected from actual adoption. The risk for financial teams lies in approving AI projects based on ambitious growth narratives and vague attribution logic.

While AI investments might boost conversion rates or reduce churn, unless these gains can be isolated and tracked over time, they remain assumptions and not proven outcomes. Finance leaders should collaborate closely with business heads to clearly define where AI is expected to drive the intended uplift, ensuring adoption metrics and revenue attribution models are firmly in place before declaring early success. Truly transformative projects often succeed or fail based on real-world usage. 

The quiet erosion of ROI

While the initial build or license fee for an AI system may seem contained, the true costs are likely to emerge over time. Retraining models as inputs evolve, maintaining compliance with new AI governance standards and monitoring for performance drift all demand ongoing investment. Specialized tools, data labeling platforms and observability stacks also contribute further to the costs, many with subscription costs that scale with usage.

Data infrastructure represents another significant hidden cost. AI thrives on clean, secure, well-structured data, which all organizations don’t possess readily. Building and maintaining robust pipelines, ensuring data governance and avoiding silos require both technical resources and substantial financial commitment. Financial leaders must assess the total cost of ownership across the full lifecycle of the investment. These include development, deployment, maintenance and scaling. A technically sound model with a poor cost structure is likely to diminish margins over time.

From technical KPIs to business value

Organizations can’t improve what they cannot measure. Many AI initiatives are judged solely on technical criteria like model accuracy, inference speed, or latency. While these are necessary, they are rarely sufficient for assessing business impact.

Strategic AI investments demand business-aligned metrics. For instance, "productivity lift per full-time equivalent" quantifies how AI augments the workforce, measuring additional output or efficiency gained through AI copilots or automation. "Adoption velocity" measures how quickly internal teams embrace AI features thereby signaling whether the solution is creating practical value.

Other emerging measures include "cost per decision" or “cost per outcome,” which reframes AI as a decision enhancer or outcome driver. Revenue metrics like "AI-attributed revenue contribution" measure cross-functional collaboration to determine how much of a new sale or upsell is genuinely driven by AI. 

These metrics can foster accountability and also shape future budget allocations by building the narrative for long-term value. The guiding principle is that none of the individual metrics are perfect and organizations need to focus on a combination of key performance indicators that drive the most value in their specific business context  

Reshaping the returns landscape

The economics of AI are rapidly shifting. Inference costs, once a limiting factor, are declining due to hardware optimization and more efficient models. However, as costs fall, usage often rises unchecked, leading to new budgeting challenges around overuse or redundancy. The right set of optimizations and alerts on usage can keep these costs in check. 

Simultaneously, providers and cloud platforms are adjusting their pricing structures. APIs may become more expensive, rate-limited or bundled differently, catching unprepared teams off guard. AI investments that were viable under one pricing structure can suddenly become uneconomical.

Another evolving trend to watch out for is related to policy shifts. From the EU AI Act to pending U.S. regulations, compliance requirements are expanding, often adding new layers of documentation, oversight and internal governance. Adjusting to policy changes usually comes at a cost that is rarely factored into initial forecasts.

Collectively, these trends point towards the importance of agility. Financial models must be flexible enough to accommodate shifts in regulation, supplier pricing, and usage behavior. 

Risk factors and policy considerations

As artificial intelligence integrates more deeply into enterprise processes, the risk landscape expands. Data privacy violations, regulatory penalties and model misuse are tangible liabilities, especially in sectors like healthcare, finance and public services. The absence of a coherent AI governance policy can expose companies to not only brand risk but also financial and legal consequences.

Vendor lock-in is another growing concern. Once AI systems are integrated into core workflows and fine-tuned for domain-specific performance, switching vendors becomes expensive. Over time, this can limit negotiating power and inflate operating costs. Financial executives must also anticipate emerging risks related to taxation, intellectual property rights and the attribution of synthetic content. Depending on the jurisdiction, the development and use of generative models may have implications for transfer pricing, licensing or audit exposure.

Additionally, industry dynamics and business sector trends matter. For instance, a media company might face low risk deploying AI for content generation, whereas a financial institution introducing AI into credit decisions needs to meet much higher thresholds for explainability, audibility and compliance.

Managing AI as a strategic portfolio

Artificial intelligence should not be evaluated as a single asset; it should be managed as a portfolio of strategic bets. Some projects will deliver rapid return on investment, others will require longer gestation periods, while some may fail. What truly matters is establishing the right governance to track performance, applying a shrewd financial lens to evaluate cost and risk over time and maintaining the discipline to divest or reinvest accordingly.

Ultimately, the companies that lead won't necessarily be those with the most advanced models or access to huge capital. They will likely be the ones where finance teams ask sharper questions, track the right outcomes and align every dollar of AI spending to measurable and enduring business value.

Artificial intelligence is all the rage these days. Given that the technology can lead to potential business benefits such as greater efficiencies, advanced data analytics and enhanced customer experiences, the rising use of AI is not surprising. 

Rushing into AI too quickly and without a cohesive plan can lead to bad investments in tools and services as well as other problems, however, and CFOs need to be just as involved in decision-making as CIOs and other business and technology executives — if not more so. 

“CFOs play a pivotal role in guiding their organizations' AI strategies and investments,” said Alexander Bant, chief of research for CFOs at research firm Gartner. “They possess a deep understanding of the true costs associated with AI investments, which often exceed initial estimates by 500% or more.”

This knowledge empowers CFOs to make informed decisions and effectively communicate the financial implications of AI initiatives to stakeholders, Bant said.

Recent research shows companies are spending a lot on the technology. For example, a February 2024 report by Gartner, based on a survey of 302 CFOs and other senior finance leaders, shows that 90% of the respondents are projecting higher AI budgets in 2024. None are planning a reduction.

Seventy-one percent of the finance leaders surveyed plan to boost spending on AI by 10 percent or more compared with last year. Generative AI plays a big role in this increase, Gartner said, with 81% of CFOs projecting to spend more in this area. 

How can CFOs best help guide their organizations' AI strategies and investments to ensure success? Here are some suggestions.

Make AI a team effort 

AI can impact virtually every aspect of a business, and CFOs should work with others in the C-suite to establish an organization’s vision for the technology, Bant said.

This means deciding whether the organization will use the technology to improve the existing business and operating models or go even further and create new ones. It also means determining whether AI will be used internally to improve operations, as something to enhance services to external customers — or both.

Recognizing the cross-functional nature of AI initiatives, CFOs need to leverage their relationships with other C-suite executives, such as the CIO, chief human resources officer (CHRO), and chief data officer (CDO), Bant said.

“These collaborations are vital in driving successful AI implementation,” Bant said. “CFOs understand that AI initiatives require a holistic approach, involving finance, technology, human resources, and data governance. By fostering strong partnerships with these executives, CFOs ensure that AI initiatives are integrated seamlessly into the organization's overall strategy.”

Emphasize data governance

If data isn’t accurate, secure and handled according to the requirements of privacy regulations, AI initiatives can run into problems. That’s why data governance is so important. 

“Data governance, led by the [CDO], is a critical aspect of AI adoption,” Bant said. “CFOs recognize the importance of data quality, integrity, and accessibility for AI to function effectively.” 

As such, they need to collaborate closely with the CDO to establish robust data governance practices, ensuring compliance with regulatory requirements and promoting data quality and integrity, Bant said. “This collaboration also involves assessing data readiness for AI adoption, identifying any gaps or limitations in the organization's data infrastructure and implementing necessary improvements.”

Have a data backbone in place to support AI

To be successful with AI, companies need to have several data-related elements in place, said Anthony Lam, CFO at healthcare technology company Healwell AI.

One is access to an abundant steady stream of data. “The continued usefulness of AI is dependent on the ongoing access to a steady stream of relevant data,” Lam said. Another is to use high-quality data, which falls under the data governance area. “Accurate data underpins the results that AI will yield,” he said.

And perhaps most central to the data strategy, companies need to have a robust IT Infrastructure in place to process data. “Larger volumes of data will require a larger investment in computing power to process the volume on an ongoing basis,” Lam said. 

CFOs should work closely with CIOs and other technology leaders to ensure that IT infrastructure supports the use of AI now and into the future.

Be vigilant about AI spending and ROI

Whether it’s because of AI hype or a perceived need to keep up with change, companies are spending a lot on AI. CFOs need to keep close tabs on investments and their returns. 

CFOs need to manage the significant capital investments required for AI implementation while effectively communicating the rationale and expected returns to investors, Bant said. “By striking the right balance, CFOs ensure that AI initiatives contribute to the organization's financial health and long-term sustainability.”

There are common mistakes CFOs make within the finance function when it comes to AI adoption, Bant said. One is dramatically underestimating AI costs. “CFOs often underestimate the true costs of AI implementation, with estimates being off by 500% to 1000%,” he said.

Another mistake is rushing into long-term contracts with the wrong vendors. “Lack of experience in AI leads to hasty decisions,” Bant said. A large majority of CFOs have less than two years of AI experience, according to Gartner research, and this results in potentially unfavorable vendor contracts.

Find business tasks that can be automated with AI 

Business processes include many repetitive tasks that AI could enhance through automation, and this can save companies time and money. 

“It’s likely that you can think of common use cases in your support [or] IT, finance, and sales and marketing organizations that come up almost daily,” said Kevin Rhodes, CFO at Extreme Networks, a provider of network technology.

“For instance, opening a trouble ticket, responding to simple customer questions about products [or] even forecasting future bookings and revenue; these are all things that AI can automate and can free up human employees to do things that add more value to the organization,” Rhodes said. 

The ROI CFOs should be looking for with automation include increased speed and efficiency in completing these tasks and reducing the manpower necessary to complete them, and allowing employees to spend time on more important matters, Rhodes said.

Financial professionals generally aren’t known for embracing the prospect of additional government regulation. However, a notable exception is coming vividly into view.

Given rampant concerns over the various security and ethical risks associated with the proliferation of artificial intelligence technologies, should governments regulate AI?

Absolutely, according to deal-makers, who suggest they’re wary of incorporating AI into their businesses without such regulation. In a new survey of 500 M&A professionals worldwide, almost three-quarters (73%) favored government involvement.

While there are many ways to categorize AI technologies, in the M&A sphere, the greatest concerns relate to “generative AI,” or GenAI, which creates brand-new content (high-quality text, images, code, data, 3-D renderings) from the vast quantities of data it’s trained on. 

Deal-makers are well aware of GenAI’s promise, according to Datasite, a provider of M&A technology that conducted the survey. That covers everything from organizing and categorizing files in preparation for due diligence to powering data analysis to increase value and accelerate M&A integration.

“AI is already reshaping various phases of the deal-making process,” said Datasite CEO Rusty Wiley. “However, there is a healthy awareness of the challenges associated with GenAI, and acknowledgment that establishing systems, processes, and regulatory frameworks is critical to effectively harnessing its benefits and mitigating risks.” 

While 90% of the deal-makers claimed moderate to extensive familiarity with AI, more than 60% said the adoption of GenAI at their organizations was low or that they were using it experimentally.

Although almost half (42%) of survey participants said productivity was the biggest benefit of using GenAI in their businesses, almost as many (36%) identified data security and privacy concerns as the biggest obstacle. That was a much bigger barrier than lack of competence or expertise (26%), GenAI’s immaturity or need for more validation (20%), and unclear use cases (12%). 

One in seven deal-makers (14%) said they’ve seen a deal derailed because of concerns about GenAI.

Almost half of those surveyed said GenAI could speed up M&A deals by 26% to 50%. On the other hand, just 15% said it would help reduce M&A costs or improve risk identification.

Might M&A professionals have factored their personal circumstances into their dim view of GenAI aiding with efforts on costs and risks? More than half (52%) of the survey respondents anticipated that GenAI would increase their daily workload, and 51% expressed concerns about its impact on their employment.

According to Wiley, the employment concerns aren’t necessarily well-founded. “M&A is ultimately a relationships business, and people are essential to driving deals forward,” he said.

Almost half (43%) of deal-makers said they expect GenAI to be most disruptive to the technology/media/telecommunications sector, followed by financial services (24%) and life sciences (12%).

“With the [M&A] industry on the precipice of a massive shift in technological adoption, it will be important for deal-makers to ensure their business models are primed to leverage AI,” said Wiley