Gartner says CFOs struggle to turn AI into business value

Chief financial officers are failing to turn finance AI deployment into business value, according to Gartner. The finding is based on a survey of 204 finance leaders.

Research from Gartner found that finance teams have moved AI from experimentation to production, but the benefits so far have fallen short of many finance chiefs' expectations. The data suggests the main gains have come from efficiency rather than better business decisions or broader influence across the organisation.

According to Gartner, 66% of finance organisations that have adopted AI cited greater efficiency and productivity as a top benefit. Yet 63% said implementation was slower than expected in 2025, highlighting a gap between rollout efforts and the results finance leaders hoped to achieve.

Marco Steecker, Director Analyst, Research, Gartner, said finance leaders should stop treating the spread of AI tools as proof of success.

"Finance teams have certainly made AI use more common, but CFOs now need to prove that AI is improving decisions, accelerating execution and helping finance shape enterprise outcomes," Steecker said.

He added that simple deployment metrics were no longer enough. "They must not mistake activity for impact. Counts of pilots, tools rolled out or use cases in production show that finance is moving, but they do not prove that AI is delivering the value boards now expect."

Value gap

The results reflect a broader shift in expectations around AI in finance. Early efforts often focused on testing tools and automating routine processes, but boards and senior executives now want clearer evidence that AI spending is generating material returns.

Some of the weakest outcomes were in implementation speed and analytics-related use cases, Gartner said. Financial forecasting and insight generation were among the lowest-rated areas, suggesting that more complex applications are proving harder to turn into measurable gains.

That matters because forecasting and analysis are central to finance's role in planning, capital allocation and business oversight. If AI remains concentrated in narrow productivity tasks, finance teams may struggle to show that the technology is changing how the function contributes to broader decision-making.

Steecker said many projects were still aimed at modest operational improvements rather than bigger business issues. "Finance leaders see the potential of AI analytics, but too many initiatives are still aimed at incremental improvements rather than material business problems."

He said the strongest opportunities may lie in more difficult areas. "The best opportunities are in areas that matter to the business and are difficult to diagnose using traditional methods."

Shift in focus

Gartner said chief financial officers should judge their AI portfolios by realised value rather than deployment volume. It also advised finance leaders to shift investment away from a narrow focus on productivity-led use cases and address common obstacles, including cost overruns and rigid team mindsets.

Gartner also pointed to the need for stronger foundations in data, talent, process and governance as finance teams prepare for wider use of embedded AI assistants and AI-enabled simulation. The recommendations suggest many organisations still face basic readiness issues even as adoption increases.

For finance leaders, the findings raise questions about how AI projects are selected, measured and governed. Counting pilots and systems in production may be simple, but those measures do not show whether finance is producing better forecasts, identifying risks earlier or helping management make stronger decisions.

The survey results also suggest the finance function may be seeing the same pattern found elsewhere in business technology: deployment has advanced faster than proof of return. In that environment, scrutiny from boards is likely to intensify as organisations seek evidence that AI spending is linked to tangible outcomes.

Steecker framed the issue as a test of finance's role rather than its willingness to experiment with new tools.

"Finance does not need to prove that it can use AI anymore. It needs to prove that AI can change how finance supports better business decisions."