In a survey of 300 CFOs and finance leaders in May 2020, 90 percent of respondents said they anticipate investing more, less, or the same amount in AI since COVID-19. To get the full benefits, and thus competitive advantage, from such investments, CFOs must look beyond projects that only aim to modernize the function, however.
The top priorities should be to improve the organization’s data architecture to support future AI goals; to invest in citizen data scientists so that AI production can be rapidly scaled where successful; and to redesign the organization’s reporting suite so that is best aligned with internal customer needs rather than with traditional finance tasks.
For example, a common use case of AI is to use machine learning to predict customers prone to late payments and issue earlier payment reminders to such customers or chase late payers automatically. Reducing late payments has a clear ROI in that it will improve a company’s cash flow. However, this is not using AI to do anything new. Finance would have chased those payments anyway, albeit less efficiently. On the other hand, something truly transformative could be using AI to identify likely late payers at the sales stage, so that sales prospects are prioritized according to which is likely to pay most promptly. This has the potential to transform a business’ approach to mitigating late payments and improve cash flow even further while reducing the need to chase for payments in the future, freeing up finance function time for higher-value work.
To avoid the inherent bias of a use-case-focused approach to AI projects, Gartner experts advise CFOs to start with a problem that needs solving, not a process that needs modernizing.