Artificial intelligence (AI) is capturing more attention among CFOs, but allocating funds is only the first step in successfully adopting the technology. To reap the highest benefits from AI, finance heads must tailor their strategies around four key behaviors, a Wednesday Gartner release advised.

AI remains in its infancy inside financial departments. Gartner, in surveying 103 finance leaders across multiple industries, found that of those who have deployed AI in finance, 75% report having done so within the past two years. Finding AI-specific talent, incorporating software with embedded AI capabilities, experimenting broadly with pilots and designating an analytical AI implementation leader are all critical actions CFOs can take in order to support and foster AI deployment, according to the report.

The most challenging of these actions appears to be finding AI-specific talent outside of one’s organization that can bring in key skills and experience, said Jacob Joseph-David, director, research for the Gartner Finance practice.

It can be confusing for finance heads determining where to find this talent, he said, with some looking to their IT departments or others seeking to train some of their existing analytics staff to fill these key roles. Looking outside of one’s organization for fresh blood may increase the likelihood of finding the talent needed to enhance AI deployment, however, Joseph-David said.

“What we saw was that when you went outside of your finance organization to hire, there's a higher propensity of being a leader in AI,” he said.  

Available budgets and time constraints are critical factors when it comes to where organizations may be able to hunt for the AI-specific talent they need, however, he said.

“Not every organization has the funds to say, ‘Hey, let’s go out and find new data engineers, new data scientists, and put a new leader of AI in charge of this,” he pointed out. “That seems like one of the hardest things to overcome.”

Setting realistic expectations for stakeholders

AI deployment is increasing, notably, with more finance leaders able to overcome these challenges. McKinsey’s 2021 State of AI survey found 56% of respondents noted AI adoption was up by at least one function, compared to the 50% who said the same in 2020.

Accounting processes, financial analysis and back-office processing tend to be the three “heavy hitters” of current AI implementation in finance departments, or those that tend to be the most popular applications of the technology at present. Gartner sees an average of four AI use cases implemented by organizations within the first year, Joseph-David said.

Successfully moving AI into new uses cases also requires finance leaders to set realistic expectations and targets with their stakeholders, he noted.

“One of the big things that we’re seeing is level setting expectations with the benefits of AI,” Joseph-David said. “One of the barriers we’re seeing a lot is that stakeholder expectations are often too lofty.”

Stakeholders may expect to be able to monetize that data immediately, Joseph-David explained, which is unlikely given the nascency of AI within finance. Setting clear guidelines for implementing AI within one’s organizations and positing a realistic timeline of when to expect process automation to become really valuable is therefore key.