The CFO Strategy for Artificial Intelligence

A new study of nearly 200 enterprises that have already adopted some form of generative artificial intelligence (GenAI) found they have judged GenAI to be a strategic priority. As such, many have C-suite leaders involved as part of a centralized strategy of testing, deployment, and governance. But that doesn’t mean these companies haven’t been pushing the boundaries on use cases.

The study by Computer Economics (a service of Avasant Research), Generative AI Strategy, Spending, and Adoption Metrics, found that 40% of the companies had put the chief information officer or IT steering committee in charge of the GenAI effort, and another 34% put the CEO or board of directors in charge. Cross-functional teams were in charge at 14% of the companies, while CFOs had lead authority at 6% of the organizations and chief risk officers at 5%. 

“Having a few early adopters using GenAI individually might lead to a couple of short-term wins, but having a defined set of tools, strategy, governance, and budget can lead to sustainable boosts in productivity and new revenue,” according to the Avasant report.

Budget-wise, centralization also appears to be the common approach. More than four in 10 organizations (44%) said their GenAI budget was a component of their central IT budget. Another 40% said GenAI spending was housed within a different budget, sometimes within a single business unit or department.

For companies whose GenAI spending was within the central IT budget, the median spending was 4% of the budget, a significant number for such a young technology.

David Wagner, director of research at Avasant, said it’s too early to use that number as a benchmark because some companies were only spending a couple of thousand dollars on pilot programs or fractions of a percent of their IT budgets. But he also said it was a surprise to find some companies far enough along that they had seven-figure budgets.

Where will the money come from? While some companies will be aggressive and invest significant dollars upfront, Wagner thinks the “zero-cost transformation” method will be popular, as it was with cloud computing. In zero-cost transformation, the dollars generated by productivity enhancement, once realized, are re-invested back into other cost-savings or revenue growth areas. “So that you’re never actually paying for any of this stuff; it’s paying for itself,” Wagner told CFO. “I suspect a lot of that is happening in GenAI.

While it may be easy for companies to see possible efficiency gains from GenAI, that can be limiting. Gen AI can also boost revenue by being used in product design, prototyping, and finding the market value of an idea — and in general help employees be more creative. Nestlé uses a proprietary GenAI tool to validate new product ideas based on the tastes and preferences of existing products.

Avasant found some exciting use cases and pilot programs occurring inside enterprises. Two companies were using AI chatbots to handle first-round interviews with job candidates. A retailer with only a small business-to-business (B2B) sales component takes B2B orders solely through a GenAI portal. A professional services company replaced their entire procurement team. And a financial services company turned over all writing of employee performance reviews to a GenAI tool. 

While these use cases are impressive, they’re not predominant. The biggest problem with GenAI right now, said Wagner, is understanding “the art of the possible” — understanding how to govern it, what to be afraid of (cybersecurity, factual errors, results bias), and figuring out the use cases in a given industry. (See chart above.)

These organizations already in the throes of experimenting with GenAI see the technology creating jobs not taking them away. One-third of the business leaders surveyed (34%) said it would lead to major job creation, and another 48% said it would create “some” new jobs.

According to Wagner, less-experienced workers have the largest productivity gains from using GenAI. “Senior folks aren’t gaining as big a benefit because they already have the knowledge contained inside the AI,” Wagner said. 

While GenAI has its flaws, it’s here to say, he said. “We're going to have to learn to govern it. We're going to have to learn its limitations. [But] everyone can learn from it and use it.”

As advances in artificial intelligence take noteworthy leaps, CFOs across industries need to not only question how the changes will affect their finance organizations and businesses, but consider the range of opportunities that are opening up. 

AI can look at infinitely more information than any human possibly can, in a shorter amount of time, and make insightful connections much faster than we can on our own. If you haven’t already done so, it’s time to anticipate what the advances in AI mean for your company and your role in it. 

On the Brink of Transformative Changes

In recent years, automated processes have given CFOs a test run for this moment in time. Automation sped up how we process invoices and make payments, for example, and moved employees away from more transactional, and once paper-focused, tasks to more strategic responsibilities. 

No longer having to manually enter customer data from one software program to another, the accounts receivable team could devote more time to improving its collections process and thinking of ways to incentivize customers to pay more quickly. The number of steps involved in onboarding and paying vendors shrunk with automated payment tools and QuickBooks moved small and midsize companies (and the accounting firms that serve them) away from spreadsheets toward more seamless transaction tracking, accounting, and payroll activities starting in the early 2000s. 

Finance organizations have benefited from these advances in technology as every iteration has increased the amount of information we have access to and it continues to get richer and more meaningful. When we moved on from pen and paper ledgers to spreadsheets and then to desktop software to the cloud, every progressive step brought us closer to real-time actionable information on which to base strategic decisions. 

While larger companies are exploring uses of AI today, such as predictive analytics, the technology has not yet been transformative for finance organizations at the level of the tools mentioned above. There’s vast potential, however. Truly understanding all of that information, analyzing it, and translating it into actionable insights is where AI could offer tremendous value. 

Always a Need for Humans

What sometimes gets left out of the discussion around AI and the workplace is that humans will always be needed, to both train and oversee these systems, and to offer a point of view that only they can provide. The human perspective, particularly the mindset of the CFO, is always going to be beneficial. What’s likely to evolve is a symbiotic partnership between humans and technology. AI can process information faster and more competently than the human mind, yet our lived experiences are what companies need to determine the right answers that are in their best interests.

Indeed, humans will continue to be needed for validation of the answers AI offers us. This is evident amid all the viral attention surrounding generative AI tools like OpenAI’s ChatGPT. ChatGPT is certainly impressive for its quick responses, confident and humanlike manner, and extensive knowledge, but it’s missing the ability to provide true analysis. 

The experimentation with ChatGPT thus far re-enforces a habit ingrained in most CFOs, to keep asking questions. Asking the right question and then understanding or being critical of the answer received will be an essential skill. As you ponder the potential for AI at your company, your work life, and your organization, consider these three steps you could take in the meantime. 

1. Start experimenting if you haven’t already. Could generative AI help with drafting hard-to-write emails or summing up important meetings? Could it ease some of the processes involved in investor relations documents or financial presentations? Safeguards and guidance do not yet exist for sharing any material information with a bot, so that is not recommended, but you may be able to use generative AI to help outline a document or presentation. 

For instance, my firm recently used ChatGPT to kickstart a brainstorming session. Participants were asked ahead of time to think of some ideas and ChatGPT was asked too. The answers were surprisingly similar, and it had some ideas we hadn’t considered but were missing a few important ones.  

2. See the potential and don’t foster fear. AI holds the potential to introduce efficiencies, raise productivity levels, and develop new skills within your staff, while also deepening your understanding of what’s happening with the business and where it’s going. 

AI could help you visualize things beyond what’s currently possible, inform your decision-making, give you more time to focus on strategic matters and supplement your knowledge and expertise. These positives can be parlayed to your employees, who may be worried about AI’s eventual effect on their jobs and may need help seeing how their roles could become more fulfilling. 

3. Consider early adoption. Artificial intelligence is poised to affect how many of us work if it hasn’t already, and CFOs who pay attention to it now will position themselves and their company at a competitive advantage, while expanding their own skill sets. 

In anticipation of significant tech advances, there’s usually hand-wringing over job losses, but the reality is more nuanced. Instead, anticipate more of a shift in job responsibilities and opportunities. 

David Roberson is president of RoseRyan, a finance and accounting advisory firm and ZRG company. 

Imagine the world’s best workplace contributors — they have been at the company for 40 years, have access to all the right people, ask the right questions, and know all the answers. They’re at your door and ready to get work, automating administrative and routine processes, freeing up your team’s capacity to focus on strategic, complex, and human-focused work, and making your people better and faster. Their name is artificial intelligence (AI).

The possibilities and the current realities of using AI and machine learning (ML) to tackle everything from customer engagement to running complex “what if” scenarios in this macro environment continue to grow. Based on the number of organizations that have already implemented AI, it is not a passing trend. It’s a major business disruptor that is rapidly being adopted by both large and small companies.

In a recent survey of 1,000 business decision-makers from around the globe, 73% said they are feeling pressure to implement AI in their organizations, as leadership seeks to increase productivity and gross domestic product.

According to a report, AI could drive a 7% (or almost $7 trillion) increase in global gross domestic product and lift productivity growth by 1.5 percentage points over 10 years. In every sector, executives are asking: What can AI do for us? And how can we leverage it to stay ahead of or catch up to the competition? The answer lies in the data.

Harnessing the power of AI is reliant upon quality data at the ready. As much as AI-driven solutions like large language models (LLMs) appear to mimic human intelligence, they are only as good as the data on which they’re trained and the people who make decisions based on the tool’s output.

Recent headlines may lead you to believe AI is upending everything and going to cost jobs. However, the combination of AI-powered efficiency and the ever-important human touch creates a powerful synergy to build better business relationships. That’s why it’s essential to combine human judgment with a pipeline of reliable, unified data to make the most of AI and ML. Unfortunately, more than three-quarters (77%) of respondents to the Workday survey said they were concerned about the timeliness or reliability of their data.

Empowering Human Judgment

Much of the media coverage around AI has been focused on predictions that millions of people could lose their jobs to the technology. Fortunately, cooler heads have prevailed and organizations like the Society for Human Resource Management (SHRM) have begun talking about the more likely scenario in which AI aids employees instead of replacing them, The world’s largest HR association, boasting nearly 325,000 members, SHRM points to a Microsoft report envisioning “a future where AI saves time, enables smarter work, stops information overload, improves data searches, eliminates busywork and unleashes creativity.”

I view AI and ML as talented new co-workers, not replacements for people. These advancing technologies can help provide sharper insights, free up the capacity to partner with business teams in new and exciting ways and enable faster business decisions, 20% to 30% faster, according to recent Workday research. By automating certain processes and transforming the way we can look at data, AI and ML empower humans to make more precise decisions and drive more effective behaviors.

Understandably, AI and ML are causing anxiety in the workplace. As they are deployed, teams will continue to have to navigate learning curves as they become comfortable working with new technologies. AI and ML will be deployed in process-specific ways that will reshape people’s jobs, but not eliminate them. For example, finance teams will not simply hand over decisions to AI. Instead, AI and ML will help remove some of the risk from predictive modeling.

Such advancements are poised to revolutionize businesses, yet organizations may find their expectations dashed if they do not set the stage to seize that potential. Companies saddled with data silos will find their AI and ML journey slow-going.

AI automation requires careful testing and deployment, and that process is far more time-consuming if teams must pull internal and external data from multiple systems. Data is fueling the emerging AI age — build the right data infrastructure and you will find yourself moving forward much faster.

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Katie Rooney is CFO and chief operating officer of Alight, which provides cloud-based benefits and human capital solutions.

CFOs and the C-suite continue to observe the evolution of artificial intelligence (AI) technology and the opportunities — and risks — it can create. Some organizations have chosen to adapt and integrate only when it directly addresses a specific problem they need to solve, while others have done so on a more preparatory, individual workflow basis. And finance leaders who are hesitant about AI are cautious about implementation on a large scale at the moment.

According to new data from tech.co's Impact of Technology on the Workplace report, caution abounds. Over two-thirds (67%) of the more than 1,000 business leaders surveyed said AI integration either remains limited or is non-existent. 

AI Can Be Helpful; Cybersecurity is the Priority

From using AI to monitor camera footage of employees for inventory management to preparing for meetings with stakeholders, CFOs and finance teams have shared use cases of AI in their workflows. But despite nearly six in 10 (59%) respondents saying they find AI tools helpful, over a third (34%) said AI tools aren't used at all at an organizational level. Slightly fewer (33%) said these tools are used at an organizational level but to a limited extent.

Data also suggests cybersecurity integrity is the primary factor influencing AI integration. Among all respondents, 40% said cybersecurity threats will greatly impact their performance in 2024, and nearly a fifth (19%) said their company is using AI-powered tools in their cybersecurity efforts. 

What is AI’s Best Use Case?

While there is general agreement that AI can bring value to a company, the specific areas where it is most valuable are debatable. Data analysis was the most prominent, earning just under a third (32%) of the affirmative responses. Writing tasks (26%), scheduling and calendar management (21%), and automated data entry (20%) were also popular responses. 

As for which AI providers are best, OpenAI's ChatGPT, the company that pioneered the movement, remains the top choice. When asked what tools are most useful to business leaders at an individual level, nearly two-thirds (65%) chose ChatGPT. Google's BardAI (recently renamed Gemini) was also popular, earning nearly half the responses (48%) among survey participants. 

AI's Impact on Workplace Outlook

Surveyors found a positive correlation between AI use and reduced weekly work hours. Among the just 4% of organizations in which AI was fully integrated, 93% said they would consider implementing a four-day workweek.

While the sample size is still small for the number of AI-integrated companies, the greater likelihood for a flexible policy such as a four-day workweek is promising for talent-strapped finance teams. And out of the group with no AI adoption, views were split. Just over four in 10 (41%) said they would consider a flex policy, with nearly an equal amount (38%) rejecting the idea entirely.

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tech.co surveyed 1,047 senior leadership professionals from businesses based in the United States. Participants had job titles ranging from manager to director. tech.co is a subsidiary of MVF, a lead generation platform.

AI-powered chatbots are the trendiest thing in technology now. With these tools getting people out of parking tickets, writing essays for students, and even eerily hiring human beings to bypass captcha tests, this technology is unprecedented and disruptive.

Industry leaders requested formal bans on the further advancement of this technology last week, while some countries have banned the software’s use altogether. 

Whether generative AI will work alongside or replace humans is up for debate. Regardless of industry, companies using people-centric decision-making processes are now dealing with a technological catalyst that many executives, especially CFOs, will respond to by reassessing how they approach some decisions. 

As products like ChatGPT continue to advance, here are five ways finance leadership can expect this type of technology to impact daily operations sooner than later. 

1. Consulting

Despite leadership saying consulting is safe for now, expect those whose business model is making the most informed, top-notch decisions and business guidance models to shift toward using AI to do their jobs. With the trillions of data points the latest version of ChatGPT operates on, consulting agencies that choose to dismiss the software’s use in their services may be doing themselves a considerable disservice. Major players in the consulting space are already making deals to build the most technologically inspired offerings. Just last month, Bain & Company announced a partnership with OpenAI, the parent company of ChatGPT.

2. Talent Evaluation

Employers may have to reevaluate the hiring process. Not only is ChatGPT capable of enhancing the workforce's learning processes, it can also be leveraged to create personal cover letters, resumes, interview tips, and even verbatim dialogue for candidates who wish to sound the best in the interview. In response to this, executives should put an onus on the interpersonal aspects of a job interview, and judge candidates not solely by the work they have done but by their demeanor, work ethic, energy, and interpersonal communication skills; you know, the stuff that makes them human. 

3. Supply Chains

Constructing pathways to move people and goods can leverage outside-the-box knowledge to drive efficiency. Those who find constant variables popping up in the way they get the materials or products they need, especially at large scale, may benefit from the constantly updating information database in AI-powered chatbots. Companies like clothing retailer Gap have already inked agreements with tech companies in hopes of using the technology to “nudge” shoppers into exchanges and not returns.

Generative AI will also help the retailer optimize where to move, sell, and store merchandise. Ninety-five percent of eligible new inventory managed through Optoro, the technology provider and AI-powered tech company working with Gap, is listed and ready to be resold the same day it is received while “significantly” reducing out-of-stocks for customers. 

4. Remote Work and Freelancing

Regardless of what an employer thinks about employees using ChatGPT to assist or complete their work, those who employ remote workers or contractors may have little control over how much they use the software. With remote workers finding new ways to juggle multiple positions unbeknownst to their employers, AI generative technology can create more opportunities for workers to do just that.

With freelancing and the gig economy becoming a dominant factor in the way businesses and people alike work, ChatGPT may eliminate the need for companies to outsource small projects that would be too time-consuming for full-time employees but not a heavy-enough lift to justify a dedicated internal position. These types of in-between duties are precisely what ChatGPT may excel in executing first, even in its public infancy. 

5. Workplace Communication

Much like job candidates using ChatGPT to make themselves appear the perfect candidate, the technology can also enable employees to replace their own words with AI in inter-organizational communications. It can make them appear more informed and on their game than they actually are. Many employees, especially Gen-Z, have a distaste for meetings and would rather receive communication via email or text messaging. ChatGPT's technology may serve them well in attempts to sound more intelligent to their managers.

With AI’s ability to construct professional and grammatically clean copy on a whim, managers and executives who adhere to their employee’s wishes of communicating through digital channels may not even be speaking to the person they think they are, but rather with a buttoned-up version of the employee facilitated by a chatbot. That may cause some confusion and frustration when those virtual conversations turn to in-person ones, and those same employees are asked to explain their ideas. 

Finance has in many ways spearheaded business automation in the past few years — but will that necessarily happen with artificial intelligence, particularly generative AI? 

Vendors are pushing that theme, but uncertainty around AI governance, controls, output accuracy, and security means history may not repeat itself.

In a session, “AI in the Audit — What Finance Executives Need to Know,” at the FEI’s virtual Corporate Financial Reporting Insights conference earlier this week, panelists revealed some of the early adoption strategies in finance and auditing.

Ed Moran, managing director of innovation at KPMG and panel moderator, said that the Big Four firm’s generative AI was filling “real capacity gaps,” including drafting policies and procedures and performing simple audit tests. “I think this is going to be as ubiquitous as spreadsheets,” he commented during a separate session on auditing regulation.

But finance teams, especially their controllers, will be weighing finance’s need for completeness and accuracy against the potential benefits of quick implementations of generative AI.

Controls and Governance

Payments company PayPal Holdings has been using generative AI and exploring use cases in functions like compliance, customer service, fraud, human resources, and credit risk decision-making. But finance lags. “[Finance’s] beginning point is just to be skeptical and [ask] what are the controls necessary to realize it within our function, which, of course, [handles] lots of material nonpublic information,” said Hasitha Verma, PayPal’s chief accounting officer.

Companywide, PayPal is taking a centralized approach to governance. The payments company has set up an AI center of excellence to guide and train employees and develop and approve use cases. It also drafted a responsible AI framework. But in these early days, the most crucial piece might be the written guidelines for individual employee use of generative AI tools.

“One of the distinctions we're making is using free public tools [like ChatGPT] versus subscription-based ones or vendor-provided services,” said Verma. When using open-source AI tools, the guidelines tell employees never to upload sensitive, confidential corporate information. For a payments company, that includes its financials and “high-stakes” data on consumers, merchants, bank instruments, and credit and debit cards. 

“Generative AI works by taking all the submitted data points and using them to generate some output. If you’re submitting confidential information, the AI tool may produce an output that substantially resembles one of those proprietary inputs,” Verma said.

Early Auditing Uses

The other panelist, audit partner Mike Sawyer of KPMG, said the Big Four firm is in the vanguard of companies using generative AI but is cautious about using it responsibly.

KPMG has been trying algorithmic approaches in auditing for years, Sawyer said, but it required substantial upfront investment and programming. This past Summer, KPMG developed its version of a generative AI tool that reduces administrative burdens and assists with audit quality, said Sawyer. Alongside the tool, KPMG released a “prompt library.” The first tier of the library explains large language models and describes how to best interact with KPMG’s tool.

A higher-level tier of the library helps enhance audit quality. A chatbot prompts engagement team members about documentation and other aspects of an engagement and compares the inputs to the relevant audit standards. The output is an observation like, “Hey, your documentation is great, but maybe think about these two or three things,” said Sawyer. 

Generative AI “can get things wrong,” said Sawyer, “but just interacting with it, reading prompts that have standards listed, those are huge triggers to think about everything that needs to go into the audit file.”

Somewhat ironically, a key barrier to the use of generative AI in finance could be auditability. “One of the challenges of AI tools is there’s a bit of opaqueness in the output, in the processes that generate the output,” said Verma. “How would our auditors look at it?”

Sawyer agreed. “We’re thinking about how do we apply the same kind of lens to completeness and accuracy of data and how management is controlling the data,” Sawyer said. “We’re seeing a lot of use of generative AI in low-risk areas,” he said, helping KPMG develop a model of how to audit it.

Wait or Move Forward?

With the shortened development lifecycles in generative AI — OpenAI announced a customizable version of ChatGPT last week — it may seem like finance will be at a disadvantage. But because of the speed of development, companies that quickly adopt the most recent innovations in generative AI and roll them into the business may not gain a sustainable competitive advantage.

Organizations have to assess what’s going to be available two, three, or four months down the road, said Sawyer. “All of a sudden there will be an off-the-shelf solution.” The cost-benefit analysis, said Sawyer, will be, “How much time do we invest now” in a custom tool “versus waiting a couple of months” for an enterprise solution?

On the other hand, standing still won’t be a realistic option when service providers adopt AI. Microsoft, for example, is embedding AI into its software and platforms.

“When onboarding a vendor, there’s [now] another question you must ask,” said Verma. “Do they use AI in any of the services they provide you? And have they covered all the bases you would cover if you were the direct user?”

Employees’ spend and expense filing, two areas that have given headaches to even the largest organizations lately, may be able to leverage AI technology to help improve finance operations. In particular, generative AI’s ability to monitor and predict employee spending may be a way for companies to have greater visibility and clear governance over employee spend when on the road.

AI is helping to eliminate business travel issues popping up since travel has bounced back post-pandemic, with 70% of companies saying they use AI and other technologies to manage certain aspects of business travel, according to new data from American Express’ Trendex Business Travel Edition. 

As nearly three-quarters (73%) of all respondents said they currently use some type of expense management software, the incorporation of AI into the expensing process may turn a classically-dreaded process for business travelers and CFOs alike into a simplified one. And according to surveyors, American Express and Microsoft are developing new technology that addresses the issues faced by expense managers and travelers alike. AI and machine learning capabilities offer solutions to make expense reporting and approvals simpler, as well as streamlining reconciliation and reimbursements for accounting teams.

To ensure employees stay within per diem allowances and aren’t held responsible for changes to accommodations outside of their control, AI tools may be a way to safeguard against unexpected expenses. Forty percent of survey respondents said they are planning to use AI products that handle these types of things.

By automating the creation of expense reports and the approval process, travelers hope these types of technologies can help create a smoother and more cost-efficient travel experience. However, its incorporation has many leaders skeptical about the practicality of an immediate implementation. Safety and security (67%), functionality (56%), training, (53%), and the cost of the AI itself (53%) were all top concerns among leadership.

A Shift to Virtual Cards

Whether employees are fronting the money themselves or using a corporate card to reconcile travel expenses, cybersecurity, cost, and ease-of-use are all shifting the future of employee spend towards a virtual card system, data shows. Virtual cards can be trip-specific and use data to automatically configure policy controls and spending parameters to stay within company policies.

Of business travelers already using virtual cards, 73% say they will increase their use for employee business travel over the next year. For those who aren’t using virtual cards yet, over eight in 10 (82%) say they are likely to consider using them in 2024.

“Virtual cards provide enhanced security, customization, and cash flow management while providing better visibility into an organization’s spending,” said Aliya Bokhari, senior vice president of American Express. “Automation continues to make virtual cards useful with controls for spending limits, expiration dates, and allowed merchant categories.”

The American Express Business Travel Trendex polled 1007 business travelers between July 10 and 17, 2023. 

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