CFOs urged to take disciplined approach to AI spend

Chief financial officers must take a more disciplined approach to AI investment in finance, according to Gartner, which based its view on a survey of 160 senior finance leaders.

The findings suggest finance leaders are moving beyond experimentation and are being pushed to set more realistic expectations about when AI projects will begin to generate returns. Simpler finance use cases tend to deliver results faster than more complex applications tied to planning, analysis and decision-making.

The survey, conducted from January through April, found that data extraction, accounts payable and receivable automation, and report creation generally deliver returns within nine to 10 months. More involved tasks such as data management, insight generation and forecasting usually take longer to realise value.

That timing gap is shaping how finance departments allocate spending. Many AI investments are concentrated in areas with a shorter path to returns, particularly work linked to productivity gains.

These include report creation and process automation, which remain among the most commonly adopted uses of AI in finance. But a narrow focus on quick wins risks sidelining projects that could have a broader effect on decision-making, risk management and revenue growth.

Marco Steecker, Senior Director Analyst in the Gartner finance practice, said finance leaders need to treat AI investment as a portfolio rather than a collection of isolated experiments.

"AI adoption has reached a point where CFOs must adopt more deliberate portfolio management," Steecker said. "The goal is not to stifle experimentation, but to know where to invest, when to cut underperforming initiatives, and which foundational capabilities to accelerate, especially as AI technology becomes more user-friendly and barriers to experimentation diminish."

Return horizon

The findings point to a tension facing finance chiefs. On one side are projects that can reduce manual work and show measurable impact in under a year. On the other are more demanding initiatives that may take longer to implement but promise broader business benefits.

Steecker said finance leaders should resist letting the appeal of rapid returns dominate spending decisions.

"Finance AI investments tend to be focused on lower time-to-value use cases that boost productivity, like report creation and process automation," he said. "However, CFOs should not let the appeal of quick returns crowd out more complex use cases that take longer to mature but can improve decision-making, manage risk and support revenue growth."

Priorities are also shifting as another obstacle moves to the foreground. While securing suitable talent has long been seen as a leading barrier to AI efforts in finance, the spread of agentic coding tools has changed the picture.

Gartner's research found that AI literacy has now overtaken hiring as the main challenge for finance leaders. The issue is no longer only whether departments can recruit specialists, but whether existing teams understand how to use AI tools effectively and appropriately in day-to-day work.

Skills gap

For CFOs, that creates a management task that goes beyond budgeting for software or identifying pilot projects. They also need to decide how staff will gain practical experience and how to build confidence in tools many employees may still see as unfamiliar or opaque.

Steecker said finance teams need regular, practical exposure rather than abstract training alone.

"Low AI literacy is now the most significant barrier finance leaders must address," he said. "CFOs should give employees practical opportunities to use AI through project assignments, sandbox experimentation and short, on-the-job activities. This will help finance teams build the skills and confidence needed to get more value from AI."

The findings underline a broader change in how finance departments are approaching AI. Early trials often focused on testing what the technology could do. The emphasis is now shifting to where to invest, how long to wait for results, and when to stop projects that fail to deliver.

For finance chiefs, the message is that success will depend less on the number of AI initiatives launched and more on the discipline used to judge returns, sequence investments and close the literacy gap within their teams.