Most CFOs expect AI to transform finance, but only 9% have scaled it
A wide gap exists between what CFOs want from AI and what they've actually achieved. While 75% expect artificial intelligence to have high or very high impact by 2030, only 9% report successfully scaling AI across their finance operations, according to a BearingPoint study of over 200 finance leaders.
The problem isn't technology. It's structural. CFOs cite data quality, governance, and process design as the real barriers. Without these foundations in place, AI pilots stay isolated experiments rather than becoming enterprise-wide capabilities.
The maturity gap is real
Most organizations remain at early stages. Seventy-three percent of CFOs describe their current AI adoption as minimal or basic. More than 80% anticipate significant or moderate changes to finance roles within five years, signaling that the shift is coming whether they're ready or not.
AI shows clear potential in forecasting, automation, and decision support. But potential doesn't equal execution. Organizations struggle to move beyond isolated pilots because they lack the right data foundations and governance models.
Data quality is the biggest roadblock
Seventy-four percent of CFOs cite data quality as a major obstacle to AI adoption. Fragmented system landscapes and unclear governance structures compound the problem. Many organizations find themselves stuck in what BearingPoint calls a "pilot trap"-individual initiatives deliver value, but they remain isolated and fail to scale.
This leaves finance functions unable to translate AI potential into sustained, enterprise-wide impact.
Operating model redesign is the answer
Leading organizations are moving beyond experimentation. They're aligning AI initiatives with broader transformation programs, standardizing processes before automating them, and building the data and governance structures required for scale.
This shift changes what finance professionals do. As AI augments core activities, finance teams evolve into analysts who interpret and validate AI-generated outputs. Organizations investing in hybrid finance and data capabilities, combined with clear governance and trust frameworks, are better positioned to scale adoption.
Rather than treating AI as a series of projects, these organizations embed it into systems, processes, and decision-making, enabling continuous performance steering and more predictive, insight-driven finance.
What CFOs need to do
Closing the gap between ambition and execution requires deliberate action across four areas:
- Operating model design
- Data and system transformation
- Governance frameworks
- Workforce development
CFOs who treat data quality, governance, and process design as prerequisites rather than afterthoughts are the ones pulling ahead. Those who redesign how finance operates-not just which tools it uses-will define how finance creates value in the age of AI.
Learn more about AI for CFOs or explore AI for Finance to understand how to close this gap in your organization.
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