CFO best practices in the evolving generative AI era

Product enhancements and productivity gains are among the top benefits that business leaders expect to achieve from their planned investments in generative artificial intelligence, according to the results of a recent survey by KPMG.

The vast majority (97%) of executives indicated plans for GenAI investments over the next 12 months, with 43% expecting an allocation of $100 million or more. When asked what they saw as the greatest value creation that might come from these investments, half of the respondents said improving products and services via customer data analysis, while 48% said “enhancing efficiency to generate greater productivity.”

“We’re entering the next phase of GenAI, moving from pilots to transformational programs,” Steve Chase, KPMG’s vice chair of AI & Digital Innovation, said in a press release. “Early experimentation has proven the potential of GenAI, sparking a readiness for greater investments that will deliver enterprise-wide productivity gains, reshape business models and create new revenue streams.”

Despite the growing frenzy around GenAI, many companies are taking a measured approach toward adoption, according to Sanjay Sehgal, head of markets at KPMG. 

“They want to make sure they do this in a thoughtful way,” Sehgal said in an interview.

Given the size of the companies polled by KPMG, the dollars that are being invested into GenAI are “probably in line” with the types of outcomes that executives are hoping to achieve at this stage — if not low in some cases, he said. KPMG surveyed 220 U.S.-based business leaders representing organizations with an annual revenue of $1 billion or more.

The proportion of S&P 500 companies mentioning AI on earnings calls rose to 36% in the fourth quarter of last year, an all-time high, Goldman Sachs analysts wrote in a February research note shared with CFO Dive.

The report highlighted several companies, including Microsoft, Amazon, and IBM, that are expecting AI to produce results such as enhanced productivity, reduced costs, and improved product offerings.

In one example, IBM CFO James Kavanaugh told investors during a January earnings call that the company remains “laser-focused on our productivity initiatives as we digitally transform our business processes and scale AI within IBM.”

“This includes simplifying our application and infrastructure environments, streamlining our supply chain, aligning our teams by workflow, reducing our real estate footprint and enabling a higher value-added workforce through automation and AI-driven efficiencies,” Kavanaugh said.

KPMG predicts that GenAI performance indicators will continue to evolve over time, with early productivity boosts giving way to more transformative investments. However, enabling long-term business value first requires “meaningful investments” in areas such as data security, governance frameworks and workforce preparedness, the firm said in its release.

“When you put all of that together, that’s an investment that companies have to make to then ultimately be able to reap the value that’s going to be on the other side,” Sehgal said.

Top GenAI investment priorities currently include workforce training and capability building as well as developing governance programs to implement the technology in a responsible way, according to the release. Over half (58%) of organizations said they plan to provide mandatory GenAI skills training in the workplace. 

The research comes on the heels of a Deloitte study showing mixed views from CFOs on the outlook for GenAI investments. That poll found that finance leaders are planning to spend modestly on GenAI next year as they navigate some of the risks and uncertainties surrounding the technology.

Thirty percent of the Deloitte survey respondents indicated either uncertainty about the appropriate metrics to use for gauging the value of their investment in GenAI or a lack of current measurements. Those responding to a question about top barriers to GenAI deployment within the finance function in particular cited technical skill gaps, adoption risks, and culture and trust issues, among other concerns. 

Digital transformation was a top focus for companies in 2023, especially following the launch of OpenAI’s ChatGPT generative artificial intelligence tool — which sparked a flurry of investment among businesses looking to capitalize quickly on the new technology’s potential.

However, it’s critical for companies not to fall into the trap of investing in technology simply for the sake of it — rather, companies should be approaching digital transformation and technology integrations with a CFO mindset, said Raphael Ouzan, founder and CEO of engineering and technology talent platform A.Team. That means creating an outcome-focused digital transformation strategy, rather than assessing company-wide implementations that can often fall short of their lofty goals.

“When you think about product engineering… you think outcome first,” Ouzan said in an interview. “And outcome, that's the ROI language of the CFO.”

Taking the CFO approach   

Companies have continued to place digital transformation high on their priority lists, but many digital transformation initiatives are driven by a need to respond to hype rather than a clear business strategy — a pitfall contributing to the failure of over half of these initiatives, according to a September 2023 study by KPMG.  

The GenAI spending spree exemplifies the challenge faced by many companies, which need to keep pace with emerging technologies, but also need to ensure they are driving efficiencies as they come out of an uncertain economic environment.

“On one hand, you have to get to ROI very quickly, you need to increase margin in an inflationary environment,” Ouzan said. “On the other end, there's an acceleration of the pace of technology that we've never experienced in our lifetime.”

Applying a CFO point of view throughout a company’s digital transformation strategy can help achieve that balance, said Ouzan, who founded the New York-based A.Team in 2020. Taking an outcome-focused approach where companies can zero in on individual teams — bringing in skilled experts as needed — can potentially provide greater benefits, “so instead of doing those massive projects, you could do some things that are much smaller and get to much more transformation, much faster,” Ouzan said.

“When we come in, we come in basically with the CFO mindset of, what are the biggest problems right now,” he said. “And we have this approach which is basically, you don't jump to product innovation. You start with efficiency, because that is the first most immediate value proposition and business case for AI.”

Keeping the focus on efficiency

CFOs themselves also play a crucial role in helping their organizations respond to these types of challenges, “because ultimately CFOs know better than most how to value and how to put a premium on optionality,” Ouzan said.

However, many CFOs are working with budgets that are likely to remain flat or potentially decline given economic uncertainty, making it key to keep an even closer eye on the organization’s cost centers and where they will be spending to innovate. The key issue for CFOs tasked with balancing efficiency and innovation in this type of environment will be to learn what Ouzan called “iterative agility;” being able to swiftly experiment with new technologies in a way that creates value.

“It's important to realize that there's a discovery element to this, that you cannot foresee all future revenue and risk of a particular technology project or product capability building until you actually do it and iterate on it,” he said.

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By GEP

• Generative AI is transforming procurement and supply chain management, driving innovation and efficiency.
• AI enables knowledge capture, risk mitigation, contract management, and inventory optimization.
• Adopting an AI-first approach is crucial for staying competitive and resilient.

Procurement and supply chain leaders face the daunting challenges of making sense of the complexities — from managing intricate global supply networks to mitigating risks, ensuring sustainability, and driving cost efficiencies.

These challenges are made more complicated by an uncertain macroeconomic climate, extreme weather phenomena, geopolitical upheavals and other supply chain disruptions.

Leaders have attempted to harness the power of technology to navigate their organizations through these storms, which has enabled digital supply chain transformation and supply chain modernization.

While there has been much interest in artificial intelligence (AI) development over the last few decades, it’s generative AI, and its rapid advancement in only the last few years, that has caused executive leadership to speed up AI adoption.

The rise of generative artificial intelligence (AI), exemplified by large language models like OpenAI’s ChatGPT, Google’s Gemini, and others, is nothing short of a revolution. This groundbreaking technology promises to spark significant creativity and innovation, ushering in a new era of enterprise intelligence that will impact every business function, including procurement and supply chain operations.

For procurement and supply chain leaders, embracing generative AI is no longer an option but a necessity.

As a recent survey by Foundry and GEP found, 65% of senior tech decision-makers believe AI/ML-based tools can significantly augment or revolutionize human decision-making within supply chain and procurement. Failure to adopt these cutting-edge technologies could leave organizations at a significant competitive disadvantage.

Applications and Benefits of Generative AI

The use cases for generative AI are advancing extremely quickly, but the following are some of the capabilities enterprises have been able to take advantage of this new technology:

Knowledge Capture and Information Retrieval:

Leverage a broad knowledge base and continuous learning to provide accurate, relevant insights for effective knowledge management and information retrieval across complex supply chains.

Contextual Understanding and Cross-Domain Knowledge:

Comprehend and retain context across logistics, sourcing, procurement and more to enable coherent decision-making.

Creativity and Language Generation:

Generate creative, contextually appropriate responses to foster innovative thinking, problem-solving and tasks like contract creation.

Supplier Selection and Risk Mitigation:

Analyze vast data on supplier performance, financials, compliance to identify the best suppliers and mitigate risks proactively.

Contract Creation and Management:

Generate nuanced contracts with specific requirements to ensure compliance and reduce errors/disputes.

Inventory Optimization:

Leverage data, trends and forecasting to optimize inventory levels and reduce excess stock/stockouts.

Supply Chain Resilience and Adaptability:

Facilitate simulations, scenario planning and risk analysis to improve resilience and enable proactive responses to disruptions.

Sustainability and Ethical Sourcing:

Evaluate supplier sustainability practices, carbon footprint and social responsibility to drive ethical, sustainable sourcing strategies.

Using AI’s Full Potential

Organizations must take quick action to adopt AI solutions — watching from the sidelines risks falling behind competitors.

Rather than piecemeal bolt-ons, an AI-first approach unlocks scalable, flexible and future-proof intelligent solutions that apply AI's full potential to drive real value.

Generative AI is emerging as a transformative creative collaborator, working alongside procurement and supply chain professionals to amplify their skills and capabilities.

It simplifies supply chain management, mitigates disruptions, optimizes inventory, creates nuanced contracts and reshapes operations for constant improvement — all seamlessly.

Crucially, organizations must build knowledge to effectively engage AI, enhance creativity through controlled AI deployment, and make informed decisions that avoid risks.

By doing so, companies can leverage the power of generative AI to enhance human creativity while maintaining control and oversight, unlocking a future of efficiency, resilience, and competitive advantage.

Download the 2024 GEP Procurement & Supply Chain Tech Trends Report 

The buzz around artificial intelligence has only intensified in recent months, leaving business leaders with the unenviable task of separating the technology’s potential from its reality,

While determining AI’s true applications is a must, the larger question CFOs and accounting leaders need to ask themselves is, “do you truly understand how AI is going to affect the people you hire…as well as those that are going to serve your customers?” said Susie Duong, senior director of research and thought leadership at the Institute of Management Accountants.

A CPA, Duong joined IMA two years ago. Her previous roles include a three-year stint at professional education firm Becker, and she has also served as a visiting assistant professor of accounting at University of Michigan’s Ross School of Business and an assistant professor of accounting for the NUS Business School at the National University of Singapore, according to her LinkedIn profile.

Making AI welcome

While they don’t occupy the top seat when it comes to driving generative AI strategies at their companies — a study by Gartner found CFOs were fourth on that list, behind chief technology officers, chief information officers and CEOs — finance chiefs are nonetheless intimately involved in the decision process when it comes to how and where to incorporate such technologies.

CFOs are also displeased with how digital investments across their organizations are performing, Gartner’s study found. Implementing AI is pricey, Duong pointed out; if businesses are choosing to incorporate it, “at the same time, you have to give up something else that you could have done,” she said. “This is the opportunity cost.”

To make that cost worth it, finance leaders need to ensure they’ve developed a forward-looking AI strategy that provides a return on investment or strategic benefits for the business. CFOs face challenges in four major areas when it comes to bringing AI into the finance and accounting space, according to a recent study by IMA authored by Duong: understanding the technology itself, ethics and governance, operational issues and lastly, the human element.

To take full advantage, finance leaders will need to foster seamless collaboration between the technology and their employees the IMA study found. While AI won’t replace humans, it will “tremendously impact the future of work for professionals in accounting and finance,” the report argues, and CFOs need to be fully prepared for that future. That means educating both themselves and their employees on the technology itself.

“Working with AI is more like working with a new colleague,” Duong said. “To build the trust, to some extent, you have to understand the algorithm behind it.”

That might be challenging for finance and accounting professionals, who are not typically trained on the technical side, she said. It’s important when bringing AI into the business for CFOs to “pay more attention to your people, at all levels,” she said.

“Tell them exactly why you're doing what you're doing, and why this is beneficial to them, not just to the organization,” Duong advised when it comes to educating one’s employees about the company’s use of AI. “And also provide plenty opportunities for upskilling, or re-skilling your employees.”

Bringing one’s workers into the AI conversation is critical not only because it can help smooth the technology’s way into the company, but also because workers remain wary over the potential threat AI represents to their jobs, Duong said.

In the short time since ChatGPT was launched by OpenAI in November 2022, AI job loss has already abounded, with 37% of business leaders claiming the technology replaced workers in 2023, according to a recent CNBC report. Large tech companies like Google, meanwhile, have laid off hundreds of staff members as they continue to invest further in AI, stoking replacement fears.

Leaders should “invite your employees into this whole process, especially the function that is going to be affected,” Duong said. That way, they feel as though their opinion is valued, which can help alleviate some of their fear of replacement as well as make it more likely for them to use the AI technologies that are incorporated, she said.

Prepping for the new accounting age

Finance chiefs also can’t rest on their laurels when it comes to AI education: such a thing has to be done continuously to ensure the company and its talent are keeping pace with the rapid evolution of this and other emerging technologies, Duong said.

It’s also important for CFOs to take a forward-looking approach when it comes to how AI and employees will interact with each other as the technology becomes more commonplace in the finance and accounting sector.  

Finance chiefs are already dealing with a shortage of talent in the accounting world, and while they hunt for skilled talent — or new tech to fill the gap — those that are aspiring to enter the accounting world have different expectations than the old guard, Duong said.

Today’s college students are already learning how to use AI as part of their work, Duong said — and when those students graduate and enter the accounting profession, “their perception of accounting and finance will be completely different,” she said. Finance chiefs need to consider such changing perspectives in an AI-driven world, she said.

Companies that deploy generative artificial intelligence in financial reporting face 12 audit risks that range from flaws in governance and compliance to failures in preventing fraud and cyberattacks, according to the Center for Audit Quality.

“It is critical that companies have a governance model in place that enables them to know where and how these technologies are being used in their organization,” Dennis McGowan, vice president of professional practice at CAQ, said Friday. Given the rapid rise of generative AI, “we’re only beginning to see — and understand — how the use of this technology might be impacting financial reporting,” he said.

Auditors for companies that use generative AI often confront a “black box” challenge when they can neither interpret nor explain how the technology generates information, CAQ said. The problem grows when “financial reporting processes and ICFR [internal control over financial reporting] become more sophisticated and outputs from the technology are unable to be independently replicated.”

One out of three auditors see companies in their industry deploying or planning to deploy AI in financial reporting, CAQ found in a 2023 survey. The proportion will likely grow as companies explore how AI “can streamline or enhance accounting and financial reporting,” the center said.

Accountants are increasingly using generative and other forms of AI to prepare technical accounting memos, to smooth routine tasks such as writing Excel formulas or footing financial statements and to analyze large volumes of data for unusual transactions or unauthorized system access, McGowan said.

Financial executives also use AI to summarize contract terms, prepare the initial suggestion for the accounting treatment of a transaction, and analyze data for budgeting or for understanding variances and costs, he said in an email reply to questions.

“CFOs see the opportunity that generative AI presents, including the streamlining of processes and systems and opportunities for innovation,” McGowan said. “What is less clear is their awareness of risk,” even though audit committees now regularly include AI on their meeting agendas, he said.

CFOs should consider focusing on 12 hazards from generative AI, CAQ said, including:

• Governance – the failure to identify and manage AI applications throughout a company;
   
• Regulation – use of generative AI in ways that violate regulations, laws or contracts;
   
• Skills – employees lack the knowledge to oversee or use generative AI effectively and safely;
   
• Fraud – management, employees or third parties use generative AI to commit or conceal crimes;
   
• Data privacy – confidential data is erroneously entered into a generative AI application;
   
• Security – generative AI is vulnerable to cyberattacks, the intentional insertion of flawed data, or deliberate efforts to prompt bogus conclusions from the applications;
   
• Flawed selection or design – the choice of a generative AI application that does not achieve the desired objective;
   
• Error-prone foundation model – the company adopts an unreliable large language model that generates inaccuracies or biased information;
   
• Flawed training – faulty training of the generative AI model generates repeated output errors;
• Weak performance - due to inadequate testing, the generative AI application “hallucinates,” or provides incomplete, inaccurate, unreliable or irrelevant information;
   
• Defective prompts – employees fail to ask generative AI accurate questions, yielding unintended or irrelevant information;
   
• Inadequate monitoring – after deploying generative AI, companies fail to closely track output to ensure the technology is functioning as intended. 

“Generative AI has the potential to be transformative to business,” McGowan said.

“CFOs have a role to play in assisting their organizations in establishing governance structures that are set up for success,” he said. “They can do this by establishing a strong foundation by linking the governance over AI to their organizations AI strategy and by building literacy among employees around emerging technologies.”

The vast majority (96%) of U.S. companies were targeted with at least one payment fraud attempt in the past 12 months, a 71% increase from the prior year, as criminals step up their tactics, according to automated fraud prevention services provider Trustpair.

To dupe organizations, fraudsters primarily used text messages (50%), fake websites (48%), social media (37%), CEO and CFO impersonations (44%), hacking (31%), business email compromise (BEC) scams (31%) and deepfakes (11%), according to a report on the findings, released Tuesday. More than 260 senior finance and treasury leaders were polled, Trustpair said.

“Our research shows fraudsters are becoming increasingly more sophisticated in their tactics and their reach is expanding,” Trustpair CEO Baptiste Collot said in a press release.

Total potential losses from cyberattacks and cyber fraud rose 48% in 2022 to $10.2 billion from $6.9 billion in 2021, according to the FBI. The FBI’s Internet Crime Complaint Center received 21,832 complaints involving fraud attempts via “business email compromise” scams in particular, with adjusted losses totaling over $2.7 billion.

Such attacks have accelerated as generative artificial intelligence tools like ChatGPT have made it much easier for scammers to create “close-to-perfect” texts, emails, phishing websites, and deep-fake voices at scale, according to Trustpair, which is based in Paris, France with a U.S. headquarters in New York.

“ChatGPT-generated text messages, hacked websites, and deep-fake phone calls are now the norm as fraudsters use cutting-edge technology and AI to move faster and better than ever before,” the report said.

With a small sample of audio, cybercriminals can clone the voice of nearly anyone and send bogus messages by voicemail or voice messaging texts, according to a 2023 report from cybersecurity firm McAfee.

“The aim, most often, is to trick people out of hundreds, if not thousands, of dollars,” the report said. Of 7,000 people surveyed by McAfee, one in four said they had experienced an AI voice-cloning scam or knew someone who had. Nearly three-quarters of respondents (70%) said they weren’t confident they could tell the difference between a cloned voice and the real thing. 

AI can also be used by criminals to review large volumes of data for the purpose of identifying potential targets and tailoring scam content, according to a report released last December by PricewaterhouseCoopers. “There is no hard evidence that this is currently happening, but there was a belief amongst some of those that we spoke to that this risk will increase in prevalence over time,” the report said.

Of the companies that were targeted by fraud attempts in the past year, most (90%) were hit with at least one successful attack, according to the Trustpair study. For 25% of companies, the average financial loss of successful fraud attacks was more than $5 million.

Financial loss isn’t the only potential risk of such attacks, the report said. The possibility of reputational damage with customers or investors was a concern for 50% of finance and treasury leaders.

Despite the fact that payment fraud is an escalating threat, many companies aren’t adequately prepared to face it, with just a little over half (56%) of respondents reporting an increase in anti-fraud technology spending in the last six to 12 months, according to Trustpair.

“For budget and prioritization reasons, as well as a lack of awareness about market solutions, companies aren’t shifting to automation quickly enough and are still lagging behind fraudsters,” the report said.

Controlling fraud in the finance department has historically involved numerous manual tasks, but this could change as machine learning and artificial intelligence technologies enable dramatic boosts in the scope and scale of both monitoring and deterrence. 

Tod McDonald, a forensic accounting specialist, is well aware of this. McDonald led the investigation into a $120 million Ponzi scheme in 2012, resulting in the arrest of Meridian Group founder Darren Berg. He said the two-year investigation could have been done in two weeks if he had access to modern software tools at the time. 

“There were no other tools to ingest tens of thousands of pages of bank statement data to recreate the books and records directly from evidence,” he said, noting that this was the impetus to co-found his own company, financial intelligence platform Valid8. 

But even now, a lot of anti-fraud work requires a “breathtaking” amount of effort to catch things like financial misstatements, vendor fraud or asset misappropriation, he said. All involve processing huge amounts of financial data searching for anything suspicious, which he said can be a “very manual effort that costs a lot and takes a lot of time.” It gets even more complicated when opening an investigation into potential fraud, as the CFO often needs to involve outside experts and (if relevant) board members. 

But today, continuous monitoring with anomaly detection can be combined in a new category of software called verified financial intelligence that can process financial data in real time and check for anomalies, something he said has only recently become possible through AI and ML. These technologies are ideal for the mass data processing that continuous monitoring and detection need. 

“Financial statements are comprised of accounts. Each one has a big pile of transactions behind it, whether revenue or travel expenses, and the ability to verify each of those transactions in an automated way, backed by supporting evidence, whether bank statements or contracts or purchase orders, will be coming very quickly [to wide availability],” said McDonald. 

He noted that these solutions are very similar to what external auditors use. Like the CFO, the auditor also wants the ability to constantly assess the overall quality of financial data on an atomic level to identify anomalies on a transaction by transaction basis. With this in mind, he predicted that the solutions auditors use and those CFOs use will become more similar, perhaps even to the point of both using the same software. And both, he said, will have AI at their core. 

“So you take a full population of transactions. Say we want to verify or monitor revenue. You click on that line and there’s millions of invoices in support of the total revenue line item. ... What you [appreciate] is the scale that AI is going to allow, especially with a little more time to allow AI to really kick in above this general AI that exists right now,” he said. 

This doesn’t mean that there won’t be a place for humans. The CFO still has a responsibility to put systems in place, recruit competent professionals to oversee those systems, oversee the monitoring, and make the calls on anomalous items.

Leaders also need to know where to apply these solutions, whether payroll, accounts payable or travel expenses, as there is no one size fits all package covering an entire financial system. And they need to make sure the AI solution doesn’t just alert people to anomalies, but provides context and evidence for them. McDonald noted a lot of first-generation AI detection programs didn’t do this, which paradoxically led to more work from staff who had to analyze the flagged transactions. 

“The main thing from a CFO standpoint is really keeping their ear to the ground and having a willingness to try out new software and tools as they get released. And as with any software, you’ll want to do a trial, a beta test, to make sure it’s actually effective and compelling and worth whatever the investment you’re throwing into it,” he said. 

As more companies roll out generative AI-driven products and services, uncertainty around pricing for the AI chips that underpin the technology will likely be top of mind for CFOs. 

Many are eying the pricing by chip maker Nvidia, which commands an estimated 80% of the market share for AI chips, according to The Wall Street Journal, though Amazon, Google Meta and Microsoft are working to build their own AI chips. Nvidia typically doesn’t disclose its pricing publicly, but comments from CEO Jensen Huang around the company’s annual GTC developer conference last week are putting a focus on the costs of AI advancements.

Blackwell, Nvidia’s next-generation graphics processing unit announced at its developer conference in San Jose, California last week, will ship later this year. It combines two chips; it’s 30 times faster than the current generation for large language model inference workloads while also reducing cost and energy consumption, according to the company.

A graphics processing unit is a type of computer chip that renders graphics and images. GPUs can carry out many mathematical calculations at the same time, and are seen as critical for artificial intelligence systems. 

“Generative AI, as you know, requires a lot of flops… an insane amount of computation,” Huang said at the conference. “It can now be done cost effectively [now that] that consumers can use this incredible service called ChatGPT.”

Pricing considerations

Huang told CNBC last week that the cost of Nvidia’s Blackwell processor will hover around $30,000 to $40,000 per unit. That compares to current generation H100 chips that range in price from $25,000 to $40,000 per chip, CNBC reported, citing industry estimates. 

At GTC, however, Huang walked back the chip price estimates he gave CNBC, noting that price points for chips and enabling tools are part of a larger, overarching platform cost.

“The Blackwell system includes NVLink [connectivity technology], and so the partitioning is very, very different… we will have pricing for all of them,” Huang said. “The pricing … will always as usual come from total cost of ownership.”

A spokesperson for Nvidia declined to comment when asked for more details about chip pricing.

Michael Fauscette, CEO and founder of Arion Research, said most companies rolling out AI won’t be paying Nvidia directly for the GPUs. Instead, they would access them as part of a subscriber package from a third-party enterprise technology provider. If prices go up, the costs could be passed along to customers, but savings and efficiency gains would likely offset the higher prices.

Third-party cloud vendors offering access to Nvidia AI chips include Amazon Web Services, Oracle, Microsoft and Google, according to Holger Mueller, vice president and principal analyst at Constellation Research.

When offered through cloud service providers, chip costs “may go down… faster platforms mean that more workloads can run… so cloud vendors can run more workloads on the same machine,” he said. However, companies that need to procure chips from Nvidia may face higher costs, he noted.

Regardless of the pricing outcome, when it comes to AI, the question is not if but when companies move forward, Fauscette suggested.

“That ship has sailed,” he said. “CFOs are worried about cost…but at the same time, the more it becomes a strategic advantage,  a competitive advantage for the business — the more they're going to have to invest in it.”