TLDR: Despite growing AI adoption in finance, only 7% of CFOs report high ROI, with many facing significant cost overruns and potential project abandonment. Gartner outlines five critical steps for CFOs to bridge this value gap, focusing on strategic use case identification, team skill development, a balanced build-vs-buy approach, proactive cost management, and robust AI governance to ensure impactful and sustainable AI initiatives.
In an era of increasing Artificial Intelligence (AI) integration, Chief Financial Officers (CFOs) are confronting a stark reality: a significant disconnect between the promise of AI and its actual return on investment (ROI) within finance functions. A recent Gartner report, authored by Matthew Kiel and published on October 9, 2025, highlights that while nearly three in five finance teams are piloting or fully implementing AI use cases, and two-thirds of CFOs express optimism about AI’s value, a mere 7% are currently seeing a high impact from these initiatives. This ‘value gap,’ coupled with initial AI project cost estimates being off by as much as 500% to 1,000%, threatens to lead to the abandonment of over half of AI pilots in the coming years.
To navigate these challenges and unlock AI’s full potential, Gartner proposes five essential steps for CFOs:
1. Pinpoint High-Value Finance AI Use Cases: Successful AI implementation begins not with technology, but with a clearly defined business problem or objective. CFOs must resist the urge to adopt solutions merely because they’ve worked elsewhere or are the latest trend. Instead, they should align AI use cases with organizational priorities, evaluating them against expected value and feasibility within their company’s unique context, maturity, resource capabilities, and goals. This tailored approach is crucial for achieving positive, impactful results.
2. Prepare the Finance Team for AI Implementation and Adoption: A significant barrier to AI adoption is the low data literacy and technical skills within finance teams. To overcome this, CFOs need to invest in developing foundational data science and generative AI (GenAI) skills, such as basic Python, data wrangling and modeling, and prompt engineering. Furthermore, fostering ‘citizen data scientists’ – individuals who combine technical proficiency with strong business acumen – can address up to 90% of finance’s AI opportunities, reducing reliance on external data scientists who may lack functional context. Gartner predicts that by 2029, one-third of finance staff will be in ‘shared jobs,’ collaborating directly with AI.
3. Plan and Define a Clear AI Build vs. Buy Strategy: The quickest path to AI value often lies in leveraging embedded functionalities within existing software. While 99% of finance teams plan to ‘buy’ AI capabilities, 60% have experienced post-purchase regret. To avoid this, CFOs must ensure vendors deeply understand their organizational processes and functional capabilities. A hybrid ‘build-and-buy’ approach is recommended, balancing the quick efficiency gains from prebuilt solutions with the long-term competitive advantage offered by customized capabilities. By 2028, 80% of finance organizations are expected to adopt this hybrid model for GenAI.
4. Proactively Manage AI Cost and Value: The volatile nature of AI costs, often driven by usage, varied vendor pricing, and data management demands, necessitates proactive management. CFOs must scrutinize these cost drivers and push for greater transparency in vendor pricing. Developing consistent methods for measuring and discussing AI’s value is equally critical for evaluating use cases across the enterprise and accelerating funding for the most promising pilots. Enterprises that adopt a portfolio approach to AI investments are more than twice as likely to achieve mature levels of AI implementation.
5. Prioritize AI Governance and Risk Mitigation without Compromising Progress: AI adoption introduces new risks related to data privacy, security, intellectual property, and model performance (e.g., hallucinations, bias). CFOs are uniquely positioned to champion an AI governance framework for both finance and the broader enterprise, emphasizing data integrity, continuous model testing, and human oversight. A phased approach, aligning with the organization’s current AI maturity while supporting an accelerated value strategy, is crucial.
Also Read:
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CFOs are advised to focus on understanding the unique aspects of AI solutions, comparing use cases against business needs, and learning from early adopters before making substantial AI investments. Challenges such as talent development and data quality, while significant, should not stall progress; instead, leading organizations are using AI to improve data quality, making faster gains even with imperfect data.
