Technology changes every business, often radically, and the pace of change is getting faster. Now, generative AI (gen AI) is beginning to show its disruptive potential (see sidebar “Gen AI: A primer”). The technology won’t affect all businesses equally, and certainly not at the same time. Yet across industries and geographies, gen AI could present substantial opportunities for significant value creation.
But value doesn’t create itself. Instead, it’s the CFO’s role to allocate resources at the enterprise level—rapidly, boldly, and disproportionately—to the projects that create the most value, regardless of whether they are driven by gen AI. Similarly, in leading the finance function, the CFO can’t implement gen AI for everyone, everywhere, all at once. CFOs should select a very small number of use cases that could have the most meaningful impact for the function. In this article, we’ll discuss how CFOs can most effectively approach gen AI company-wide, prioritize specific use cases within the finance function, and rapidly climb the gen AI learning curve.
Gen AI and enterprise-level value creation
The most important action that CFOs should take is to identify the largest opportunities for value creation—and then make sure that they receive the money and other resources that they need. Gen AI holds the potential to be a revolutionary technology, but it doesn’t change foundational principles of finance and economics: a company must generate a return above its cost of capital.
Moreover, company capital (or access to more capital) is finite, and projects compete with one another. For CFOs to maximize value creation, they must rank the company’s 20 to 30 most value-accretive projects regardless of whether they are AI-related. The Pareto principle always applies; usually a very small number of opportunities will deliver most of the company’s cash flows over the next decade. The CFO cannot let the highest-value initiatives wither on the vine merely because a competing project has “gen AI” attached to it. Sooner or later, shareholders have to pay for everything, and none of them should be on the hook for a gen AI premium.
But to that same point of maximizing shareholder value, a CFO must recognize existential threats to a company’s businesses and be clear about the most important levers for generating and sustaining higher cash flows. When an opportunity squarely addresses or significantly relies on gen AI, CFOs should not shunt it aside because they don’t understand the technology or lack imagination to recognize the value it could create.
Often, a choice about capital allocation won’t be either/or: an important business or value lever can have an even greater impact by incorporating gen AI. That applies whether the most important drivers are revenue generators (such as creating an interface that will attract more customers or encourage more cross-selling), margin expanders (for example, reducing manufacturing, procurement, or distribution costs), or a factor that spans revenues and costs (such as helping to attract, retain, and motivate employees by freeing them for more creative work).
Microsoft, for example, has been far ahead of the curve in investing in gen AI to build competitive advantage in key core businesses, such as by creating the Microsoft 365 tool Copilot, which provides real-time suggestions to improve documents, presentations, and spreadsheets. While demonstrated commercial success has largely come from digital natives, some traditional, nontechnology companies are moving aggressively as well. Morgan Stanley’s Wealth Management division, for one, has shown remarkable progress in developing an internal-facing service that uses OpenAI technology and Morgan Stanley’s proprietary data to provide its financial advisers with relevant content and insights in seconds.
A world-class CFO ensures that these and other gen AI initiatives aren’t starved of capital. Indeed, one of the biggest misconceptions we find is the belief that it’s the job of the CFO to wait and see—or, worse, be the organization’s naysayer. Capital shouldn’t sit; it should be aggressively moved to fund profitable growth. The best CFOs are at the vanguard of innovation, constantly learning more about new technologies and ensuring that businesses are prepared as applications rapidly evolve. Of course, that doesn’t mean CFOs should throw caution to the wind. Instead, they should relentlessly seek information about opportunities and threats, and as they allocate resources, they should continually work with senior colleagues to clarify the risk appetite across the organization and establish clear risk guardrails for using gen AI well ahead of the test-and-learn stage of a project (see sidebar “New technology, new risks”).
The best CFOs are at the vanguard of innovation, constantly learning more about new technologies and ensuring that businesses are prepared as applications rapidly evolve.
For some CFOs, it may feel orthogonal as a “numbers person” to champion visionary innovation. But they’ve got to do it: market-beating growth won’t come from incremental change. Behind the scenes, CFOs can take advantage of their relationships with functional and business unit leaders to prod them about exploring gen AI opportunities, and repeatedly follow up in subsequent interactions. They should upskill and empower their own team members to build important relationships across the organization and better understand the assumptions underpinning innovation projects. And they should be “always on” when it comes to innovation—not just in periodic reviews or when closer scrutiny is needed for struggling projects.
Gen AI and the finance function
For many finance functions, gen AI will be table stakes—one among several of the essential tools that every effective, forward-looking finance function will use. The technology has the potential to save meaningful amounts of time and resources. That in itself is a reason to move forward—and why most, if not all, finance functions in large enterprises will likely be using gen AI in significant ways within the next three to five years. In fact, one way to conceptualize gen AI is to consider it as digital’s “third wave” (Exhibit 1). The first wave is to establish a digital foundation; in our biennial survey of global CFOs completed in late 2023, about two-thirds of respondents reported that their functions were digitally connected and using data for the basics such as visualization in dashboards.1
The second wave, clearly under way, is analytics empowerment; about half of the CFOs reported that their functions were already using advanced analytics for discrete use cases such as cost analysis, budgeting, and predictive modeling. The third wave will make extensive use of robotics and AI. Very few companies are at the third wave yet. But bold CFOs put their finance team in the best position to learn to work with these tools as the technology gains momentum.
Getting started in the finance function
CFOs typically aren’t software engineers, let alone practiced experts in predictive language models. But they don’t have to be. Their first step should be to try out the technology to get a feel for what it can do—and where its limits are at the moment. Solutions such as OpenAI’s ChatGPT are available online, and other applications (including McKinsey’s Lilli) are already in use.
CFOs’ first step should be to try out the technology to get a feel for what it can do—and where its limits are at the moment.
Try experimenting by uploading publicly available earnings calls transcripts from your competitors and asking the AI tool to produce the five most-asked questions—and to suggest answers. Or upload your company’s and its competitors’ financials, and ask the gen AI solution to take the perspective of an activist investor: What elements of your company’s performance would an activist home in on? Depending upon the sophistication of the gen AI solution, CFOs can also upload invoice and payments data and ask it to create charts that visualize the information—including a request for the one, most important chart. We find that when CFOs experience the technology firsthand, they not only better understand what gen AI is but also more rapidly grasp near- and immediate-term opportunities.
We advise CFOs to budget a nominal amount at the learning stage, not for purposes of deploying AI at scale but rather to improve the learning experience for themselves and their team members. Again, though, the goal is not to let a thousand flowers bloom. Instead, CFOs should select a handful of use cases—ideally two to three—that could have the greatest impact on their function, focus more on effectiveness than efficiency alone, and get going.
One point that quickly becomes apparent when moving forward is that gen AI is not plug and play; companies can’t simply set the models on existing sources of information and let them have at it. Gen AI doesn’t create like a human does or have a eureka moment. It doesn’t even do math (that’s the remit of traditional, or analytical, AI). Gen AI is a predictive language model—a translator that sits above existing unstructured data and seeks to generate content that a human would find pleasing. The data sets themselves first need to be rigorously processed and curated, just as data scientists prepare data lakes for advanced analytics and analytical AI.
Identifying use cases
We believe that gen AI can have an impact on finance functions in three major ways. First, through automation—performing tedious tasks (such as creating first drafts of presentations). Second, by augmentation—enhancing human productivity to do work more efficiently (such as by gathering and synthesizing multiple pieces of information into a coherent narrative). Third, through acceleration—extracting and indexing knowledge to shorten financial reporting cycles, and speeding up innovation. Gen AI can greatly enhance CFOs’ ability to manage performance proactively and support business decisions. A high-performing finance function understands the use cases that could most significantly and feasibly improve their function (Exhibit 2).
For example—and by no means as an exhaustive list—a few multinational enterprises have already begun to implement the following:
- Synthesis of information, which can create customizable interactive charts through natural-language queries. For example, solutions exist that provide a general Q&A chatbot, a chart creation tool that generates charts seconds after receiving a prompt or description of code, and a visualization tool that customizes charts by using existing code and validating the accuracy of the code.
- Digital performance management, which answers performance-related questions, synthesizes status and scenarios, identifies drivers and root causes of budget variances, and suggests resolutions. This solution is typically self-serve, business user–friendly (as opposed to finance user–friendly), and can lead to more effective performance management dialogues.
- First drafts of external reporting, which not only can save weeks of team time in preparing advanced first drafts of securities filings and stakeholder reports (such as sustainability reports) but also runs queries on the current regulations and standards to help ensure that the reports meet current standards.
- Working capital management with features such as an always-on support bot to help facilitate collections and payments, and an always-updated customer payment history risk assessment, including the capability to limit customer credit based on real-time information about customer-specific activity and market events.
The array of gen AI use cases is wide, varied—and no longer merely theoretical. And while it’s still early days, the rate of adoption is speeding up. Those realities make it even more important for CFOs to get started in a considered and proactive way.
Gen AI can be an important tool for value creation. CFOs should strive to be gen AI enablers, not gatekeepers, and make sure that strategically critical initiatives rapidly and continually receive necessary resources. They should also ensure that they and their own function quickly climb the gen AI learning curve. The future is already starting.