With the potential to supercharge enterprise functions in ways yet to be imagined, artificial intelligence (AI) and machine learning (ML) have understandably drawn plenty of attention from those looking to prepare for a future that’s quickly approaching.
In the meantime, there’s plenty of opportunity for organizations to use AI and ML as they embark on their digital transformation journeys—particularly within their finance functions.
“Finance is in danger of becoming real laggards in the area of AI, automation, and even traditional analytics,” Tom Davenport, author of All in on AI: How Smart Companies Win Big With Artificial Intelligence; a president’s distinguished professor at Babson College; fellow at MIT Initiative for the Digital Economy; and visiting professor at Saïd Business School, University of Oxford
In a recent webcast hosted by Fortune and sponsored by Workday, Davenport, along with Vanessa Kanu, CFO at TELUS International; Katie Rooney, CFO at Alight Solutions; and Philippa Lawrence, vice president and chief accounting officer at Workday, discussed how organizations were implementing advanced technologies to address the talent gap in finance.
Davenport said a survey he conducted a few years ago suggested human resources (HR) departments were “well ahead of finance in terms of using predictive analytics and machine learning.”
However, for all the ways AI and ML could help transform the enterprise, Davenport emphasized that existing technologies can free up people to perform higher-level functions. Technology, as he sees it, won’t simply replace headcount.
“AI is typically a task-oriented tool. It doesn’t replace entire jobs and certainly not entire business processes,” he said. “In order to have much of an impact, you have to do a variety of small use cases and sort of pile them on top of each other.”
Finance organizations have also started to look at AI and ML use cases to evaluate how such areas as customer service and employee-learning activities drive financial performance and quantify the value they provide to the business, Davenport noted.
Davenport added that audit organizations are already using automation to read through contracts to determine liabilities and measure performance, adding that CFOs and auditors will still need to review the end product and sign off on final results. “We’re never going to ask an AI system to do that—because we can’t,” he added.
Davenport noted that AI and ML remain probabilistic functions. “All machine learning is based on statistics and statistical predictions,” he said. “If there’s an area where you absolutely have to have the right answer, that’s still going to be one that a human will have to do.”
While ChatGPT has garnered a wealth of news headlines in recent months, the biggest technology-enabled gains for business in the near future will likely come from automating repetitive tasks. “There are lots of opportunities from robotic process automation (RPA) for relatively structured predictable finance jobs—jobs involving pulling information from one system and putting it into another,” Davenport said. “It’s really quite useful for those settings.”
Establishing a Solid, AI-Ready Data Foundation
“AI is only as good, and the insights are only as good, as the underlying data,” Rooney said, adding that maintaining a solid data foundation is a priority for Alight Solutions, an Illinois-based human capital technology and services provider to 70% of the Fortune 100 companies. “Our first focus has really been on streamlining the data infrastructure,” she said. “We have all of our systems—finance, HR, every country—actually on Workday, which has helped level-set the teams.”
That data foundation, however, is critical to enable clear, data-driven decision-making. “Our strategy is really around making sure we have that one unified data source—and it’s even broader than finance,” Rooney said. “It has to get as much as we can across our organization, as well.”