Artificial intelligence in all its forms is captivating finance departments. But new evidence shows that most finance groups are neglecting huge potential value right in their wheelhouse, through technologies that have been available for years.
RPA has gained the most momentum to this point, according to the study of 501 finance executives and professionals in the United States, U.K., and Germany, which Bain conducted in collaboration with Research Now.
For example, FedEx uses RPA to automate tax, payroll, credit card reconciliations, treasury, and other finance processes in its global shared services organization. Fossil Group uses RPA to automate the monthly financial close process. And Allianz automatically reconciles cash pool accounts across three systems through robotics.
The rapid adoption of automation tools continues a decades-long trend of technology transforming finance, from optical character recognition to spreadsheets to electronic invoicing.
But here’s the rub: The accumulation of technologies has created substantial complexity, leaving few finance departments able to wring the full potential value from the technology they own. While they are rapidly investing in an array of new technologies, many have not yet fully adopted more mature technologies:
The upshot: Too many finance departments may be rushing into new technologies without taking parallel steps to capture value from their existing store of technologies. Absent proper integration, companies that adopt more tools risk adding further complexity and unwittingly creating a new set of problems.
Fortunately, the survey suggests how finance departments can use the full toolkit more effectively.
Don’t neglect the tried and true. The extent to which many finance departments have overlooked established technologies is startling. Among large companies surveyed, only 53% have a tool to automate journal-entry creation and validation for accounting. Also, only 31% have a tool to automate revenue recognition.
While RPA or machine learning might help here, other root causes, such as poor data governance or a poor user experience, typically come into play.
Look beyond cost. Respondents cite cost savings as the top reason overall for adopting digital tools. But speed, freeing up staff time, financial controls, and other reasons follow close behind. In some industries, such as telecommunications, media and technology, and business services, at least one of those reasons is more important than cost savings.
Microsoft’s Enterprise Partner Group, for instance, uses machine learning in forecasting. With worldwide accuracy greater than 98% on average, this method has been more accurate than the internally developed manual forecast for five consecutive financial quarters.
Define the destination, then test and learn. More often than not, finance departments are overconfident in their digital maturity and ability to carry out their digital goals. CFOs, for instance, express more optimism than do frontline finance professionals about their department’s position in the industry, the quality of technology they use, and the sufficiency of funding to execute digital goals.
In light of fairly widespread overconfidence, senior finance leaders should objectively assess their digital starting point, with input from internal customers and frontline users. That will allow them to create a clear and compelling digital vision and multiyear blueprint, with a portfolio of digital bets.
Once a company starts to deploy unproven technologies, it should be willing to test and learn, rather than go all-in immediately. A test-and-learn approach mitigates the risk of overreliance on one technology that eventually falls short of expectations.
Fix and simplify processes. Rather than having an RPA bot pull data from a large number of different customer invoice types, for example, a company at some point will want to move to common invoice formats and away from paper invoices.
Similarly, using machine learning, a company can review large volumes of inbound requests across a range of email inboxes and route requests to the right teams. In billing and collections, machine learning can help companies identify deductions with a high probability of being valid and link backup documents with deductions.
However, a machine-learning algorithm works only as well as the quality of the data. Without good data governance and quality, algorithms have limited value.
Digital tools are essential for finance to raise its value to the business. They have become powerful enough to help the finance function identify value opportunities, not just monitor trends in the numbers, and to allow finance to proactively manage risks, not just maintain controls.
Making that transition to a true business partner will hinge on using existing technologies more effectively, while testing the latest tools to identify the handful that will make a difference, then integrating them into the larger family.
Michael Heric is a partner with Bain & Company’s performance improvement practice. He also leads the firm’s support function work globally. He is based in New York.