By 2026, the vast majority (90%) of finance functions will deploy at least one AI-based technology solution, but less than 10% of functions will see headcount reductions, according to Gartner, Inc.
As chief financial officers (CFOs) are already making changes to fully leverage AI in financeA sense of uncertainty, unrealistic expectations and employee disengagement often hamper success rates in using AI.
CFOs who combine the strengths of people and machines increase their chances of success. AI Success requires a satisfied and engaged workforce, Gartner analysts say.
“Despite AI’s ability to mimic human performance, algorithms cannot match the unique capabilities of people in areas that require creativity and complex problem solving,” said Ash Mehta, principal director analyst in Gartner’s Finance practice.
“By recognizing the respective strengths of people and machines, finance leaders can create processes that enhance the capabilities of people and machines, while mitigating their weaknesses. This requires a new type of collaboration between people and machines that will improve business performance and employee satisfaction.”
For example, while AI-driven machines are very adept at automating simple decisions and processes by quickly analyzing large amounts of data, they cannot work independently and may fail to draw good conclusions when faced with unusual circumstances.
On the other hand, people draw on their creativity and innate understanding of human behavior to quickly draw conclusions when faced with new and unfamiliar problems, but they cannot hope to outperform a machine when crunching numbers.
“To maximize the capabilities of AI and people, they must learn to collaborate in ways that leverage each other’s strengths,” Mehta said.
Recommended Readings
Gartner experts call this collaboration the human-machine learning loop, which drives continuous process improvements that encourage finance staff and AI-driven machines to collaborate on processes while distributing work based on each other’s strengths. While relying on each other for improvements, both parties can iteratively add greater value.
The human-machine learning loop begins with creating an algorithm, automated process, machine-driven task, or autonomous workflow, with a focus on tasks that machines perform as well as or better than humans. Machines then perform these tasks, such as generating a revenue forecast, approving an expense report, or determining the optimal payment terms for a specific customer.
Machines can also inform and advise. This is the case when a forecasting algorithm suggests that a recent policy change will alter sales prospects, or when a machine-driven invoicing process suggests sending invoices on certain days to increase collections.
The work that machines do in this way then allows humans to gather information, advice, and recommendations from the algorithms, using their creative and strategic strengths to solve complex problems by designing process improvements. Once the new processes are in place, people trigger the next iteration of the loop by building new machines that run the new processes and analyze the corresponding data.