New data shows a 32-percentage-point divide between CFOs and their Financial Controllers on AI adoption. If you think your digital transformation is complete, you might be confusing automated dashboards for true operational efficiency.
The macroeconomic landscape demands agility, which for the modern CFO, means decisively embracing digital transformation. The prevailing narrative suggests the finance function is rapidly adopting Artificial Intelligence. Yet, a costly perception gap is opening between the C-suite and the operational frontline, a divide that could undermine the very value AI is meant to unlock.
New data from iplicit reveals a stark difference in how senior finance leaders and their Financial Controllers (FCs) view the state of AI adoption. An astonishing 51% of midmarket CFOs believe they have fully adopted AI within finance; however, only 19% of their FCs agree, according to the survey data. This 32-percentage-point chasm represents a critical red flag for any CFO relying on AI to deliver sustainable operational efficiency.
From the C-suite, the financial picture often looks excellent. AI truly excels at the “presentation layer” generating sophisticated board commentary, modeling complex scenarios, and feeding automated dashboards. This strategic value promises to free up leadership time for higher-order decision-making. Investment intentions reflect this top-down optimism: 80% of CFOs plan to increase their spending on AI in the coming years. The economic lift is already real, as AI-driven capital spending has provided an impressive boost to US GDP growth in 2025.
However, the view from the “engine room” tells a different story. One Financial Controller noted that strategic commentary may look automated, but many operational finance staff still “export spreadsheets, fix data manually, and stitch together numbers” before AI tools can even begin their analysis. The foundational, dirty work of finance, the data preparation remains a persistent, manual slog.
The core challenge isn’t the technology itself, but the lack of foundational automation. If underlying processes are fragmented or data is inconsistent, AI simply applies a beautiful, automated presentation layer over a shaky, manual foundation.
Leading CFOs in the US and UK recognize that successful AI implementation demands a structured, bottom-up approach. They prioritize clear use cases and internal alignment to close this gap.
Veritas Capital’s Efficiency Push: CFO Jason Donner successfully focused on generating operational workflow efficiencies. His team deployed AI tools for core back-office tasks, not just reporting. Specifically, they use the technology to reconcile investor financial statements and process U.S. IRS tax reporting communications. This focus on automating high-volume, repetitive data processes directly addresses the FCs’ pain points and provides clear, immediate ROI.
Thoma Bravo’s Governance First: Rather than jumping straight to deployment, Thoma Bravo CFO Amy Coleman Redenbaugh emphasized starting with an AI governance framework. They piloted different technologies, appointed designated AI champions, and actively trained teams on use cases and prompt engineering. This disciplined, structured approach mitigates two common challenges CFOs cite as major barriers: “unclear use cases” and “lack of internal expertise”.
Your mandate is clear: Ensure your significant AI capital expenditure translates into genuine enterprise value.
Look Beneath the Dashboard: Demand an internal audit of AI implementation. Measure success not by the board report’s quality, but by the material reduction in manual processing time on the ground.
Focus on Data Quality and Governance: The effectiveness of any AI tool hinges on the quality of the underlying data. Build an interdisciplinary team that speaks both “tech” and “finance” languages; appointing an AI champion is critical to establishing this clean data layer.
Quantify the ROI Beyond Cost Control: Generative AI proves excellent for deliberate cost reduction, yet its true value lies in allowing finance professionals to pivot to strategic thinking. Use pilot programs with clear metrics, tracking data processing speed, value creation, and employee productivity to guide your long-term AI roadmap.
AI is the new engine of finance. For it to run at full power, CFOs must ensure the engine room connects fully to the cockpit. Closing that 32-point perception gap is the difference between an exciting pilot project and a genuine business transformation.