The recent challenges posed by economic headwinds have compelled CFOs and finance departments across diverse company sizes to make difficult decisions regarding cost reductions, while operating within more stringent budget constraints and balancing innovative approaches to office utilization and workforce optimization. These pressures are particularly heightened for middle-market companies, which often have fewer resources compared to their larger counterparts. Additionally, many industries are facing slowing revenue growth with inflation pressures still squeezing profitability.
“Every company is focused on cutting costs and getting lean. They’re worried about a recession,” says Vikas Agarwal, leader of the Financial Services Risk & Regulatory group at PwC. Against that background, chief financial officers are employing technology and data mining tools to accomplish their goals more efficiently.
CFOs have a range of choices at their disposal, whether they opt for traditional tools like Microsoft Excel or enterprise resource planning (ERP) systems, embrace newer data analysis and visualization tools, or leverage cutting-edge artificial intelligence (AI).
“Starting with COVID, rising interest rates and all the economic challenges of late, the asks have never been greater for what needs to come out of a CFO’s office, so it’s about how you can do more with the same or potentially do more with less,” says Josh Schauer, vice president of finance at insightsoftware.
CFOs working at middle-market companies are grappling with a set of unique challenges. “I see three primary issues that CFOs are facing today: resources, prioritization, and data management, says Richard Jenkins, a managing director at Alvarez & Marsal. “Concurrent with budget constraints, middle-market companies are particularly hamstrung in terms of resources. Increasingly, middle-market company CFOs are facing issues that are as complex as some of their Fortune 500 brethren, and yet they don’t have the ability to resource them in the same way”.
Prioritization comes into play in terms of deciding which strategies and customers to pursue at a time of limited resources. How companies manage and analyze data is also important. Often, companies are juggling multiple ERP systems while tacking on add-on acquisitions.
Private equity operating partners, advisors and consultants are looking at ways CFOs can use ERPs to their advantage, tap into data mining and analysis tools, and get comfortable with AI applications.
Finance professionals have historically relied on Microsoft Excel, however newer software that can harness data more effectively is becoming increasingly popular. Sources pointed to companies like Alteryx, a data science and analytics company; Workday Adaptive Planning, which provides EPM software; and Tableau, an interactive data visualization software provider, as examples of new data software that CFOs are using.
Schauer says his company works with businesses on harnessing data to find areas to trim costs. “All cost-cutting decisions should be made with a great deal of thought, and they should be made only after you’ve established the facts of your business,” he says. “Everything goes back to understanding data. If you don’t understand your data, you could be making a shortsighted decision to remove spend in an area that is detrimental to the long-term success of your business.”
Experts are also looking at how AI can assist finance departments. In a report, PwC said the CFO function offers the most fertile ground for adoption of AI and automation. CFOs can tap into AI to set targets for outcomes and benefits, involve frontline employees to show them how automation can help them in their jobs rather than replace them, select automation opportunities, and implement a governance structure by formalizing an AI strategy and training, testing and monitoring data inputs. CFOs can also prepare the rest of the workforce by engaging them in data and analytics tools across sales, marketing and other departments.
PwC’s Agarwal says various large institutions have started to work with AI in recent months. Some are setting up their own engines in-house, while others are tapping into an external application programming interface (API). A third option is open-source software that can be added to computers and allow businesses to get some of “the same functionality without as much computing power,” Agarwal adds.
Most companies tapping into AI are still learning how it works rather than using it to make decisions. Agarwal notes that whatever path AI takes, humans still need to be in the loop on decision-making. “A machine can make a recommendation, but it’s very important to have effective challenges, and there are still a lot of problems with algorithms and how they work,” he says.
Going forward, companies will need to add new regulatory and compliance procedures. “You need to develop your framework for responsible AI. There are a lot of layers of risk that you need to look at with AI, including privacy risks, cybersecurity risks and regulatory risks,” says Agarwal. “All of these things are important as you try to figure out how your AI model is operating.”
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