The demand for “soft skills” among key leaders like controllers is rising as more companies bring emerging technologies such as AI into their processes and offerings, said Rebecca Baker, director of product management for the Institute of Management Accountants.   

As AI becomes more entrenched inside financial processes, more companies are determining that “what I need more of is exactly those things that humans are excellent at,” Baker said in an interview.

Roughly one-third (34%) of controllership teams said critical thinking was the most important skill to hone over the next three to five years, according to a recent report by IMA and Big Four accounting firm Deloitte focusing on the use of AI in controllership. Self-sufficiency came in second, with 23% citing it as a key skill, with technology in third place at 17% of respondents, the survey of more than 900 finance and accounting leaders found.

Breaking down the language barrier  

Business leaders have continued to shine a spotlight on generative AI, funneling millions into the technology to experiment with a growing list of potential use cases. Sixteen percent of financial leaders are either currently using or plan to use generative AI technology, while 44% plan to adopt the technology in the next five years, the survey found.

Such experimentation is putting pressure on the finance function, with its professionals tasked not only with balancing investments and risks but with becoming “more involved in the tech implementations,” Baker said.

Baker joined IMA in May in her current role and has previously held positions including VP of product design, research at Southern New Hampshire University and as director, UX design for CA Technologies, according to her LinkedIn profile.

For controllers — who have steadily become the CFO’s right hand, responsible for producing data and reporting to the street and for running key areas such as payroll and collections — brushing up on critical thinking or other, softer skills is essential as they become more involved in such implementations.

Understanding the technology is important, but finance leaders also need to be able to work closely with their peers on the IT or tech team, something that can be challenging as the two groups are often speaking “very different languages,” Baker said.

“It's not just the tech, it's the understanding of the requirements and the needs being very different on both sides,” she said of the relationship between finance and technology. “You get a developer on one side and a finance person on the other side, and they have very different focuses when we talk about integration.”

That doesn’t mean CFOs or controllers need to become data scientists, but “they need to be able to leverage data scientists, and to be able to leverage the technology, and that requires a basic understanding of those different professions, and what they can bring to the table,” she said.

Data, governance remains top of mind

A strong relationship between tech and finance is essential to ensuring the business can accurately assess and hopefully circumvent some of the hiccups that could come from increased use of generative AI.

One concern looming large in the minds of controllers is data cleanliness, or the ability to ensure all of the data going into their generative AI tools is transparent and predictable — a notable challenge as many companies still face issues of untangling their unstructured data, as well as incompatibility between their existing systems and generative AI.

“There's a lot of hardware and infrastructure considerations that most companies may not be prepared for,” Baker said. “They may not have the capacity to be able to take advantage of these things at speed.”

One of the major issues when it comes to generative AI implementation is weak data governance. “While specific implementation challenges may vary, one common barrier is the alignment of system architecture. This relates to the noted challenges around data inconsistencies across applications,” the IMA report reads. “Inconsistent data governance across the organization leads to challenges in implementing integrated solutions.”

Integrating AI into one’s existing systems can also be challenging especially when it comes to bringing it into financial systems, “because the progress in financial systems does not necessarily keep pace with the progress in other systems,” Baker said. “And so just from a tech standpoint, there's always going to be a bit of a mismatch.”

Being able to speak tech’s language is even more critical for finance leaders here. As the executives tasked with ensuring they are creating both short- and long-term gains, that communication is key for them to get a clear sense of costs.

Finance leaders need to understand the expenses not just of the initial set up of the technology, but “time over time, month over month, what am I looking at,” Baker said. “What's that going to be: increased energy uses, hardware usage? What am I having to do with the cloud here? And what am I going to have to maintain from a licensing standpoint? Those things all have to come into play when they're making their calculations so that then you can do those ROIs.”