Workday

This is a blog post from our partner, Workday, highlighting findings on Generative AI from a Deloitte CFO Signals quarterly survey.

In the world of finance, Generative AI has entered the chat.

In a recent CFO Signals, Deloitte focused part of its survey on Gen AI and found that “a sizeable proportion of CFOs’ organizations (42%) are experimenting with it, while 15% are incorporating it into their business strategy.”

Nearly one-quarter (24%) of respondents said they’re “reading and talking about it,” while another 17% opined “it’s too soon to tell.”

Generative AI—the technology that can take vast amounts of data and use it to create new content—has captured the attention of a wide variety of industries. 

And it’s no surprise, considering the potential impact of the technology. Last year, McKinsey & Company estimated the global economic benefits of Generative AI could add the equivalent of $2.6 to $4.4 trillion a year across 63 use cases it analyzed. As a point of comparison, the report noted that the United Kingdom’s 2021 GDP was $3.1 trillion. 

A Cautious Approach Toward Gen AI

Reflecting a cautious, analytical approach as a whole, the survey’s 115 respondents expressed a few common concerns around Gen AI and the potential benefits of AI in finance.

Finance leaders’ top three concerns were around the technology: “impact to risk and internal controls” (57%), “data infrastructure and technology needs” (52%), and “investment needs (technology and capabilities)” at 51%.

The findings reflect the need for responsible AI on a wide scale to establish trust, minimize risk, and drive greater business performance.

What AI Can Do for the Finance Function

While a significant proportion of CFOs are considering the risks of an increasingly technology-enhanced future, the potential benefits of Generative AI are also dawning on them.

Almost half of finance leaders (49%) named planning, forecasting, and analysis as the top potential uses of Generative AI, far outpacing the automation of routine or transactional processes (26%) and increased efficiency (20%).

Meanwhile, CFOs identified the top three benefits they hope to achieve if their organization were to adopt Gen AI:

• Reduce costs (52%)
• Improve customer/client experience (50%)
• Increase margins, efficiencies, and/or productivity (45%)

Other potential benefits include developing new capabilities, products, or services; creating scale and/or capacity; and improving the accuracy of forecasting, modeling, and scenario planning.

What’s Needed to Incorporate Gen AI in Finance

Lending weight to the idea that intelligent finance demands a human-centered approach to AI, a majority of CFOs (63%) said talent resources and capabilities present the biggest barrier to adopting and deploying Gen AI within their organizations.

Finance leaders further said that the single most important factor in helping them make decisions related to using Gen AI are use cases (39%). CFOs also mentioned wanting to understand the ROI of gen AI, as well understanding of risks and limitations along with best practices from peers within their industry.

While widespread adoption of AI in finance hasn’t yet happened, it’s clear that it’s on the radar of many CFOs in the forefront.

“Do they have the necessary data, technology, and talent to implement GenAI?” Deloitte wrote in its report. “The survey results suggest no―not yet anyway. But for now, CFOs appear to be curious about the upsides and downsides of GenAI, and what opportunities, as well as challenges, the technology might bring.” 

Read the full CFO Signals 3Q 2023 report at Deloitte.

This blog post was originally published on the Workday blog.

More from our FP&A Done Right Series:

CFOs on AI, Partnerships and Skills

Workday’s Global CFO AI Indicator Report

The Mandate for Business Agility

This is an excerpt from a blog post from our partner Workday. It provides insights on how Generative AI will impact FP&A activities.

Digital transformation and data go hand in hand. Most executives get this: today, the vast majority view data as a critical asset to be tapped, and new AI capabilities have made this data more beneficial than ever, says Michael Schrage, a research fellow at the MIT Initiative on the Digital Economy.

But here’s the catch, Schrage asserts: Most executives can’t name the five most valuable data assets within their organization, or their firm’s return on data (ROD). That’s a problem, he says, because if a business isn’t paying close attention to the strategic value of its data, making the right AI investments will be much harder. And as the age of generative AI and machine learning (ML) takes off, the potential ROI on those investments for CFOs and financial planning and analysis (FP&A) teams is massive. Achieving those returns requires merging and marrying your most valuable data with AI.

The ways AI can power a paradigm shift in forecasting and metrics are coming into focus, Schrage says. Trained on customized data sets, large language models (LLMs) can turn KPIs into intelligent mechanisms that help create new value, instead of just tracking and protecting value. And with ML supporting FP&A activities, forecasting can become a source of dynamic insights to support operations and workforce planning—far more than just an accurate financial picture of the future. KPIs become tools for better key performance insights and key performance investments.

In other words, “The generative revolution changes everything,” Schrage says.

Future-Ready Metrics

With data now a lifeblood connecting and defining the digitally transformed organization, metrics have become increasingly important for value protection and value creation. Schrage’s most recent research focuses on combining generative AI, ML, and KPIs, asking whether KPIs can evolve beyond being simple measures and instead become software agents capable of learning.

This idea is no longer a hypothetical, given dramatic developments in generative AI over the last few years. Aided by AI, metrics can become forward-looking tools to support forecasting and scenario planning activities, helping finance leaders be more proactive and strategic. 

Using ChatGPT, you can actually ask the KPI questions that matter most, Schrage says. “What could we do to improve you? Is the data helpful to you? What new data would improve your forecast? What would you say under this circumstance? Do you think this scenario would be good for you or bad for you?” 

The bottom line: AI can help the finance function drive value with more predictive and future-oriented KPIs, whether it’s classic metrics such as revenue, profit, and sales, or increasingly important hybrid financial custom-facing metrics such as customer lifetime value or customer churn rate.

“What if your customer lifetime value KPI could talk to your customer churn KPI? The math of that kind of thing is interesting,” Schrage says.

Data + AI = Intelligent Forecasting

The potential value of predictive insights unlocked by AI is huge. So long as LLMs are trained on quality data—bad data is a major stumbling block for organizations in adopting AI, a global Workday survey found—finance leaders have the opportunity to rethink what forecasting can and should be. 

“Is a forecast about being correct and precise, or is it about being a source of insight and conversation for how the business should prepare and prioritize, go to market, or respond to customer needs?” Schrage asks. With custom LLMs or ML in the mix, forecasting becomes a richer, more cost-effective way of gaining actionable insights for the organization.

When it comes to scenario planning, Schrage sees multiple potential forecasting use cases emerging via generative AI apps built atop customized LLMs. Expect to see emerging AI/LLM ecosystems (such as Hugging Face, LangChain, and OpenAI) allow businesses to connect data streams from platforms such as Workday with generative AI systems to populate scenarios with real data and defined parameters, he says. 

“The ability to interconnect, to create interoperability between Workday and specific scenarios—it could become really meaningful, something that could be part of a compliance stress test for one’s FP&A initiatives,” Schrage adds, observing that the costs for these kinds of analytics are coming down.

Starting Points

The scope and purpose of a custom forecasting model or scenario will vary business to business, of course. But all organizations should start their AI and ML planning journey from the same place, Castonguay says. 

“You have to start with your business needs,” he adds. “What problem are you solving for? Figure that out and go from there.”

Data can be another entry point, Schrage adds. “If data is an asset, then what are your most valuable assets—and how can you get greater value from them?”

Answering that question is made harder by data silos, which remain common. Nearly two-thirds (59%) of organizations report their data is somewhat or completely siloed—and just 4% say their data is fully accessible, according to the latest Workday global survey. Those numbers need to change.

To boost accessibility and value extraction in the age of AI, Schrage suggests the finance function needs to step up and take charge of data.

“For the future of capital allocation, CFOs should be first among equals,” Schrage says. “They should be the drivers of change.”

That makes sense considering that finance—once slower to adopt AI than other functions—is gearing up its AI activity. Gartner® predicts that by 2026, 80% of large finance teams will rely on internally managed and owned generative AI platforms that have been trained on their own proprietary business data.

This blog post was originally published on the Workday blog.

More from our FP&A Done Right Series:

CFOs on AI, Partnerships and Skills

Workday’s Global CFO AI Indicator Report

The Mandate for Business Agility