Three ways autonomous technologies will impact the FP&A

Understanding these three trends will help leaders

Financial planning and analysis (FP&A) and controllership leaders need to have a plan to harness the ways digital acceleration is disrupting legacy processes in their functions, according to Gartner, Inc.

“We’re seeing widespread acceptance among finance leaders that technology is driving finance processes towards an autonomous state of operation,” said Matthew Mowrey, senior director analyst, research in the Gartner Finance practice. “80 per cent of CFOs we surveyed in 2022 expected to spend more in AI in the coming two years, for example. Around two thirds of finance leaders we surveyed think their function will reach an autonomous state within six years”.

To make autonomous finance a reality, in broad terms organizations need to move beyond investment priorities and rethink three aspects of their operations. They must consider how functions can strengthen semantic models to improve data quality and transparency; how can technology expand the number of teams performing judgment-based activities versus manual activities; and how autonomous finance can improve business performance by minimizing the burden of data analysis and decision making.”

To help FP&A and controllership leaders plan out this future, Gartner experts have made three predictions for the impact of autonomous technologies through 2028.

By 2025, 70 per cent of organizations will use data-lineage-enabling technologies such as graph analytics, machine learning (ML), artificial intelligence (AI) and blockchain as critical components of their semantic modelling.

FP&A teams build reports and analysis using data from multiple – and often disconnected – systems. End users don’t always have clear visibility into these transformations and can end up not trusting or misusing finance data while making decisions.

“When poorly understood data is used, and FP&A can’t explain its treatment, decision makers often revert to instinct or gut feel,” said Mowrey. “Data lineage solutions promise to better explain data’s treatment and improve its transparency for decision makers.”

An increasingly regulated data environment alongside a growing volume of data and decision support demands is pushing organizations to pursue more ambitious solutions to this problem. FP&A teams have tended to perceive this as an IT initiative because it is linked to enterprise data and analytics architecture. However, FP&A teams have the right skills and capabilities to drive it within the organization.

By 2027, 90 per cent of descriptive (“what happened”) and diagnostic (“how or why it happened”) analytics in finance will be fully automated.

“There is a recent trend of analytics and business intelligence (A&BI) tool vendors acquiring data science and machine learning providers which indicates a desire to leverage these capabilities to automate descriptive and diagnostic insight generation,” said Mowrey. “Today’s A&BI platforms are shifting emphasis from the analyst as a consumer to the decision maker as a consumer.”

Although automated or augmented A&BI descriptive and diagnostic insights may minimize the analytical skills barrier, decision makers must still understand and act upon them appropriately. FP&A leaders must help establish continuous and evolving literacy programs for all employees — including senior executives — to remain relevant and competitive.

By 2028, 50 per cent of organizations will have replaced time-consuming bottom-up forecasting approaches with AI, resulting in autonomous operational, demand and other types of planning.

“AI-supported decision making is just emerging as a practical, off-the-shelf innovation, and is expected to mature within the next five years, said Mowrey. “Although it is available in many financial planning applications, it just isn’t used that widely, but we expect that to change significantly in the next few years.”

Organizations should pilot solutions in pockets where current decision management approaches leave decision makers wanting, so users will become more comfortable with AI in the decision-making process. Greater comfort with AI episodes will lead to more serial acceptance and an organization momentum to drive further adoption.



Leave a Comment

Related posts