Gartner warns CFOs on AI spend split over priorities

Many finance leaders are directing AI spending towards productivity rather than decision quality, according to Gartner. A survey of 204 finance leaders found a gap between those investment patterns and board expectations.

The research found that 45% of AI investments in finance are aimed at productivity, compared with 20% focused on decision quality. Gartner warned that this mismatch can raise doubts about the value of finance AI programmes, even when projects are delivered as intended.

The findings point to a divide between the outcomes Chief Financial Officers are pursuing and those boards want to see. Finance teams have often concentrated AI spending on tools that improve individual efficiency or streamline transactional work, the survey and Gartner's analysis found.

That approach can produce measurable gains within the finance function, but those gains do not always translate into broader business results. Once a process becomes faster or requires less manual work, the benefit can level off unless it changes a broader business decision or materially alters how finance operates.

Shankar Keshav, Principal Analyst at Gartner, said the issue is not a lack of activity but the type of projects being funded.

"Many CFOs are prioritising AI use cases focused on productivity and efficiency," Keshav said. "However, boards place greater emphasis on investments that drive growth, improve decision-making and deliver competitive advantage."

This difference in priorities can create what Gartner described as a perception gap. Finance leaders may point to progress in AI adoption, while boards remain unconvinced that the work is delivering strategic results for the wider organisation.

"This imbalance can lead to a perception gap, where finance leaders report progress on AI adoption, but boards see limited strategic impact," Keshav said. "As a result, even well-executed AI initiatives may fall short of expectations when they fail to address the outcomes most valued at the enterprise level."

Value split

The survey also highlighted differences in realised value depending on the type of AI initiative. Finance functions that invested in what Gartner called "Upend" AI initiatives were more than twice as likely to report high realised value from AI.

These projects were described as efforts to create new value propositions, products or markets, rather than simply making existing tasks more efficient. The finding suggests boards may look more favourably on AI spending tied to new revenue opportunities or stronger strategic choices than on projects focused only on internal productivity.

For Chief Financial Officers, that raises a broader governance question about how AI portfolios are constructed. Rather than backing a long list of small efficiency projects, finance leaders should balance investments across different types of outcomes, including initiatives tied to growth, better decisions and reusable internal assets, Gartner said.

Portfolio shift

That means taking a portfolio approach to AI investment and judging projects against enterprise outcomes rather than activity measures within finance. The mix should include work that improves scenario analysis, identifies growth opportunities and develops reusable data, models and knowledge, Gartner said.

"CFOs need to shift more investment toward AI use cases that improve decision-making, enable scenario analysis, identify growth opportunities and build reusable assets such as data, models and knowledge," Keshav said.

Measuring success by the number of pilots launched or the hours saved may no longer be enough. Boards are likely to want evidence that finance AI spending contributes to corporate objectives and creates value beyond the department deploying it.

That will require finance leaders to define more clearly what success looks like before projects are approved and during implementation. The focus should shift to metrics linked to enterprise objectives, while recognising that both short-term and longer-term returns matter, Gartner said.

"CFOs must spell out and communicate what finance AI success looks like with clear metrics that are tied to enterprise objectives, including both near-term benefits and long-term strategic value," Keshav said. "And this will be a moving target, requiring reassessment and rebalancing as business needs and expectations evolve."