Despite widespread awareness of agentic artificial intelligence—systems capable of independent planning, reasoning, and action—a significant paradox exists: most chief financial officers (CFOs) understand the concept but remain largely unwilling to deploy the technology. This reluctance, highlighted in a July 2025 PYMNTS Intelligence report, indicates a deep-seated skepticism that extends beyond mere unfamiliarity to encompass concerns about readiness, return on investment (ROI), and the tangible enterprise value of such advanced AI systems. The path to widespread agentic AI adoption is clearly fraught with more than just technical hurdles.
The fundamental gap between conceptual understanding and practical implementation reflects lingering doubts among business leaders regarding the maturity and proven business utility of AI agents in their current iterations. While the promise of automating intricate workflows and enhancing decision-making capabilities is undeniable, a cautious approach prevails. Concerns range from the inherent risks associated with implementation and oversight challenges to the unproven nature of ROI in real-world financial contexts, preventing a swift move towards integration.
This hesitation signals a crucial shift in the AI landscape; agentic AI is no longer merely about automating repetitive tasks but about empowering systems to make critical decisions. As a chief information security officer at Sovos aptly put it, these tools are “starting to make real decisions, not just automate tasks, and that changes the game.” This profound change demands a higher level of trust and accountability, which is currently a key roadblock hindering broader acceptance and deployment across enterprises.
Building trust in agentic systems is paramount for successful AI implementation, particularly in financially sensitive environments. According to supplemental data from the PYMNTS report, this trust hinges on several critical factors. Systems must be able to provide user-friendly reports and visualizations that transparently explain the rationale behind an AI agent’s actions. Crucially, there must be a clear ability to trace outputs back to the underlying input data and logical processes, ensuring accountability and auditability.
Furthermore, human-in-the-loop controls are indispensable, ensuring continuous human supervision and the capacity for intervention, especially when high-stakes decisions are being made. Mechanisms must also be robust enough to proactively identify and minimize bias within AI-generated content and analyses, guaranteeing fairness, accuracy, and compliance with regulatory mandates. Without these foundational elements, the path to mainstream adoption remains elusive for agentic AI solutions.
As the co-founder and COO of Tropic highlighted, finance leaders require implicit trust in their systems to be consistently accurate and predictable. True mainstream adoption will only materialize when CFOs can tangibly perceive the concrete value offered by these systems and possess absolute confidence that they will not operate autonomously in an unpredictable or “rogue” manner, a perception that regrettably tarnishes AI’s current reputation in financial sectors. Financial innovation demands reliability and transparency.
Despite these challenges, there is strong momentum in sectors like professional services, where agentic AI is seen as a highly transformative addition. Research from Certinia indicates that a significant majority—83%—of professional services firms are either deploying or planning to deploy agentic artificial intelligence within their professional services automation within the coming year. This proactive engagement underscores the perceived potential, even if broad enterprise adoption remains nascent in other sectors.
However, even among these early adopters, the results are often uneven. Many organizations continue to struggle to see the expected returns, with a notable 29% expressing that their current AI solutions fall short of expectations. The reasons cited are clear: a pervasive lack of internal skills necessary to manage and optimize these sophisticated systems, coupled with fragmented data landscapes that impede effective AI integration and performance. These internal friction points exacerbate external skepticism.
In financial settings, where adherence to regulatory mandates and rigorous audits are non-negotiable, the technical and cultural barriers to agentic AI adoption are even more pronounced. Integration poses a significant challenge, requiring agentic systems to seamlessly connect with a diverse array of internal platforms, from enterprise resource planning (ERP) software to intricate forecasting models and compliance tools. Legacy IT infrastructure can further compound these issues, creating bottlenecks, particularly concerning visibility and security, which ultimately keeps CFOs cautious and agentic AI largely confined to the realm of buzzwords rather than widespread reality.
Leave a Reply