Exploring the Disparity Between AI Adoption and Transformation in U.S. Finance Functions

The Finance AI Illusion: A Deep Dive into Rillion's New Findings

In a comprehensive new report titled 2026 AI in Finance Report, Rillion has shed light on a significant complexity in the adoption of artificial intelligence (AI) across finance teams in the United States. While the data indicates a notable number of finance functions are embracing AI technologies, a deeper analysis suggests that this widespread usage may be creating an illusion of progress rather than facilitating genuine transformation within the sector.

Key Findings from the Report

According to the report, 68% of finance teams report utilizing AI in their daily processes. However, a mere 39% of Chief Financial Officers (CFOs) express comfort in allowing AI to operate independently, highlighting a critical gap between the number of organizations adopting AI and those ready to trust its outcomes without human intervention. This disparity foregrounds trust issues that could hinder the full embrace of AI in finance.

Daniel de Sousa, the CEO of Rillion, emphasizes that although finance sectors have rapidly adopted AI, mere adoption does not signify transformational change. He notes, “The interesting part of this research is the gap between how ready finance looks on paper and what's happening underneath. Trust, skills, and manual work are still holding many organizations back.”

Automation Still Needs Human Touch

The report further reveals that finance teams are encountering substantial challenges, particularly in repetitive tasks like invoice processing. Nearly 90% of CFOs acknowledge inadequacies in their existing invoice capture and data extraction systems, with around 45% asserting that human review remains a necessity even after processing. This reliance on manual input stands as evidence that much of the so-called automation still fundamentally depends on human oversight, which can lead to inefficiency.

De Sousa stresses the importance of developing processes that can effectively navigate the realities of business operations without reverting to manual interventions. “Finance teams have been automating processes for years, but too much automation still depends on people stepping in when something changes,” he explains.

The Skills Gap in Finance

Another critical finding from the report indicates a disconnect in perceived requirements for AI competencies. Currently, only 21% of finance leaders identify a lack of internal AI expertise as a major barrier to adoption. However, a significant 60% believe that an understanding of AI tools will soon become indispensable in the finance landscape. This raises the question of preparedness among finance professionals to meet evolving industry needs.

De Sousa points out that AI proficiency should not be narrowly defined as the ability to use particular software. Instead, he advocates for a deeper understanding of how to integrate AI into existing workflows and where human judgment remains essential, highlighting a need for investment in skill development.

The Need for Experience

Interestingly, finance teams that have widely integrated AI into their operations are over three times more likely to expect significant transformations in finance processes compared to those only beginning their AI journey. De Sousa notes, “The people with the most hands-on experience with AI are also the ones who see the biggest change coming.” This serves as a call to action for organizations that are hesitant or lagging in their AI adoption, underscoring the reality that waiting on the sidelines may delay potential benefits.

Conclusion

Rillion's 2026 AI in Finance Report is a crucial reminder that while the finance industry is rapidly adopting AI technologies, genuine transformation remains elusive. Organizations must bridge the gap between AI usage and trust, refocus their efforts on automation that minimizes manual work, and prioritize the development of necessary skills for their teams. The path to true innovation in finance lies not just in the technology itself but in how organizations choose to integrate and trust these systems in their operational frameworks.

In today's fast-evolving financial landscape, the insight garnered from Rillion's study underscores the imperative for finance leaders to assess their current state of AI adoption critically and to cultivate the right competencies to ensure they are prepared for the future challenges and opportunities that lie ahead.