Dive Brief:
- Finance teams must set realistic time-to-value expectations for their artificial intelligence investments as pressure mounts to deploy the technology more strategically, according to Gartner analysts.
- The research firm’s latest study of AI in finance unveiled Thursday found that 55% of CFOs reported positive overall returns from their 2025 AI initiatives. But when asked about individual AI use cases, 57% said returns were unclear, suggesting that positive overall returns may be masking uneven performance across investments.
- “There’s some projects that are delivering a significant amount of value,” Marco Steecker, a senior director analyst in Gartner’s finance practice, said in an interview. “But a large amount of projects are giving maybe a little bit of productivity but not a big bang in terms of transformation.”
Dive Insight:
Gartner says AI adoption in finance has reached a point where experimentation alone is no longer enough. As finance teams move from pilots toward broader deployment, CFOs need to manage AI initiatives as a portfolio — setting clear expectations for how quickly different projects should deliver value, cutting losses when they fall short and shifting resources toward applications that can have a broader impact on finance and business outcomes.
“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,” the report said.
For relatively straightforward finance use cases, including data extraction, accounts payable and receivable automation and report creation, Gartner found that organizations typically achieve expected value within nine to 10 months.
More complex applications, such as data management, insight generation, forecasting and scenario planning, generally require longer development periods.
Those benchmarks can help CFOs distinguish between an initiative that needs more time and one that is consuming resources without a clear path to value, according to Steecker. But he said CFOs shouldn't wait nine to 10 months to start evaluating an initiative.
“When we look at this timeline of nine to 10 months — and longer for the more complex initiatives — what we're saying here is, if you haven't gotten value by this point in time, and you've done that work of trying to figure it out and diagnose the root cause issues, it's likely that you're probably just putting money into something that isn't going to be delivering value for your organization,” he said.
The report does not, however, suggest that finance leaders should focus only on AI projects with the potential for quick returns. While early AI use in finance is driving greater productivity, Gartner’s research suggests finance teams may be concentrating too heavily on efficiency gains at the expense of other potential business outcomes.
Seventy-three percent of finance leaders identified productivity as an AI objective in 2025, while 59% cited cost reduction. Other objectives, including evolving the enterprise, managing risk, fostering resilience and increasing revenue, were cited by substantially fewer respondents, generally in the 20% to 30% range.
Steecker said finance leaders may need to be open to a broader set of AI use cases — even where the payoff takes time.
“You want to be a little bit more aggressive with the timelines for use cases that have proven to deliver faster results and give a little bit more leeway for those that take longer to develop,” he said.
