Joseph Fuller, a professor of management practice at Harvard Business School, says business leaders have to embrace decision intelligence—and rethink company processes in the age of AI—if they want to effectively manage risk and outperform peers.
These days, the biggest risk for CFOs might be a lack of visibility into their organization’s operational data.
“Building that bridge into the operating data for the CFO organization is the surest way to be able to detect risk while actions can still be taken to account,” said Joseph Fuller, a professor of management practice at Harvard Business School, at a recent Fortune “Emerging CFO” webinar. “It’s when the risk has already been baked into the numbers that you’re at a loss of what to do, except not make the same mistake again in the future.”
CFOs are uniquely poised to harness AI technology as part of their journey toward becoming value creators and finance futurists. And that means their new mandate includes understanding the ROI of data that will drive business insight.
While CFOs must tackle underlying issues as varied as supply-chain disruptions, errant financial forecasts, or declining market share, it’s incumbent upon them to help navigate their organizations around such risks.
Decision intelligence—the combination of AI and machine learning (ML) technologies to accelerate decision-making—can play an important role, Fuller said.
“Decision intelligence allows us to significantly reduce exposure to operating risk, but we do have to be cognizant now of risks that are emerging as we deploy these technologies that are so powerful and so likely to increase companies’ productivity,” he said.
To outperform peers, companies have to understand that “winning with AI basically relies on two variables: how much data you’ve got and how fast you get learnings.”
Yet moving too cautiously can carry unwanted costs, Fuller added.
“A big risk companies face today is that they’re approaching the transformation into AI-driven processes cautiously,” he said. “You are incurring a big risk if you’re moving slowly and your archrival is moving fast—particularly if they’re large, because if they’ve got a magnitude of data advantage already and then they get a speed advantage, you’re never going to catch up.”
Finding that sweet spot between innovation and caution is critical, Fuller said.
“You have to be deliberate about balancing risk management in deploying AI with the risk that being lackadaisical about it leads to a shift in competitive advantage will be very, very hard for laggards to overcome,” he said.
“Companies have to design processes to maximize its exploitation and organize people around that, as opposed to appending AI to existing processes.”
Joseph Fuller
Professor of Management Practice
Harvard Business School
When it comes to understanding AI, Fuller said that many organizations see it as “another wave of technology like it’s some kind of super-sexy SaaS.” That leads to a misunderstanding as to how transformative the technology can be, which he likens to the advent of electricity in the Industrial Age.
“Generative AI is the most important technology since alternating current,” he said. “It is absolutely fundamental.”
Noting that it took a couple of decades for companies to understand how to reconfigure their processes, Fuller said the innovation eventually allowed them to maximize what it could do to boost productivity.
“Companies have to design processes to maximize its exploitation and organize people around that, as opposed to appending AI to existing processes,” he said. “What I would do, depending on my industry, is to start zeroing in on those processes that are absolutely critical in my competitive advantage.”
Fuller said companies must “resist the temptation” to adopt AI too slowly.
“Start the skunkworks for figuring out how you want to cut over to an AI-centric process in parallel to running your current process—but don’t dawdle.”
“What AI can allow you to do is get a full picture of your skills inventory, just like you can have a full picture of your working capital or your finished bids inventory, and make recommendations about where to deploy it.”
Historically, organizations have spent decades and hundreds of billions of dollars trying to give humans better data to make better decisions—an approach no longer viable, Fuller argued.
“It’s not fit for purpose because it doesn’t integrate across data sources, and it keeps putting people in the path of every choice,” he said.
Decision intelligence tools are no longer about technology supporting a human being, Fuller said. Instead, he added, “You build AI-based tools that are cutting across multiple data sources and presenting recommendations to individuals based on your authorities to approve the decision being made by the technology.”
“It inverts the relationship between the human and technology, which is fully appropriate now that we’ve got AI-type tools and machine-learning-type tools that allow lots of routine transactions to be made with 100% fidelity, with no human intervention,” he added.
“Start the skunkworks for figuring out how you want to cut over to an AI-centric process in parallel to running your current process—but don’t dawdle.”
According to Fuller, technology is doing two critical things: generating multiple scenarios based on various courses of action and presenting recommendations with associated risk analyses with “the full weight of AI’s ability to do massive amounts of computation and forecasting—and then letting the human being decide.”
For finance leaders, AI offers a critical ability beyond multiple scenario forecasts.
“If you can get operating data—about accounts, inventory, working process, current pricing—and contrast that using decision intelligence with forecasts and budgets, you’re going to have the opportunity to detect problems much earlier and therefore manage your risk much more effectively,” Fuller said. “All of those things are much more visible to the CFO organization so they’re not limited to retroactive across-the-board, late inventions that correct inadequacies in performance.”
Human capital management is another area ripe for a rethink.
While managers decide where to deploy their employees’ talent, Fuller notes, what if those people have skills highly relevant to a more importante project elsewhere in the company?
“What AI allows you to do is to get a full picture of your skills inventory—just like you can have a full picture of your working capital or your finished bids inventory, and make recommendations about where to deploy it,” Fuller said.
Fuller recognized that moving talent across the organization might create issues around departmental budgets and performance reviews. Yet he also noted that critical talent is in short supply and suggests that the benefits outweigh potential drawbacks.
“Keeping that talent excited and motivated working for your company by putting them on key projects, by allowing them to grow their networks within your company, by challenging them to develop new skills—you’re increasing the operating tempo, making your place of work more engaging for that worker,” Fuller said. “But most importantly, you’re putting the right talent on the right project at the right time and not late at letting organizational hierarchy decide who does what, when.”
Building trust around AI for a finance team requires “transparency, auditability, and openness as it relates to data,” Workday CFO Zane Rowe said in a recent podcast.
Evan Cabat, chief accounting officer at SiriusPoint, led a finance digital transformation that used technology to drive greater insights, increase organizational efficiency, and fuel future growth for the insurance company.
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