With AI rapidly proliferating in corporate settings, finance leaders are under pressure to adopt tools that will enhance productivity and efficiency. And while agentic AI has the potential to transform finance workflows, faster outputs are not CFOs’ sole priority — they’re also responsible for decisions, controls and consequences.
That makes trust a defining requirement for AI adoption in finance. CFOs need to know not only what AI produced, but how it arrived there, whether the result can be verified and who remains accountable for the final decision.
How AI drives productivity and efficiency on finance teams
AI’s ability to replace human data entry represents a massive opportunity to streamline finance workflows. In particular, AI models can read, understand and categorize information on financial documents with impressive velocity. “AI may be more or less accurate than a human, but it’s always more efficient than a human,” says Aaron Harris, CTO at Sage.
Finance teams are increasingly exploring AI to support functions like reconciliation, forecasting and anomaly detection. AI can also help fill hiring gaps created by the shortage of talent entering the accounting profession in recent years.
Advanced AI solutions and agentic capabilities are already helping finance teams speed up crucial processes. According to Harris, one agricultural business based in Maine reduced the time it took to process invoices by one-third to one-half as a result of implementing Sage’s AI solution. The company also leveraged AI to gain a better understanding of vendor performance and relationships.
For CFOs, these gains are meaningful. But they also raise a critical question: how can finance teams move faster without compromising on the accuracy, control and accountability their work requires?
For CFOs, trust is paramount
Accounting systems include audit trails that allow reviewers to discern who has performed the work, and human professionals can explain how they arrived at their final figures and conclusions. General purpose AI models do not provide this level of transparency or explainability.
That lack of transparency is already creating friction. New research from Sage and IDC found that 71% of finance leaders would reject AI outputs they cannot explain, even if they’re accurate. Finance professionals are also spending an average of 12.9 hours each week reconstructing, validating and defending AI outputs, while 26% of potential AI time savings are lost to verification, explanation and reconstruction work. In other words, opaque AI does not eliminate work for finance teams. It can simply shift the work into validation and explanation.
Given the high stakes of their work, CFOs should exercise a bias toward keeping a human in the loop to review AI outputs. “AI will never be 100% accurate,” Harris explains.
In addition, CFOs need to know that financial data they feed into AI models remains private and secure. In other words, CFOs need AI they can trust: systems that are explainable, governed, auditable and built for the realities of finance.
What is finance-grade AI?
For finance leaders, the goal is not simply more powerful AI, but more transparent AI: a shift from “black box” systems to “glass box” AI, where outputs can be explained, verified and interrogated before teams act on them. Finance-grade AI is characterized by three pillars:
- Confidence: Results are explainable, verifiable and can be interrogated.
- Control: Humans stay in charge and consequential actions require approval.
- Accountability: Every action is traceable including who initiated it, what the agent did, who approved it and what changed.
“When a human makes a mistake, they do it at human speed. When an agent makes a mistake, they're doing it at AI speed. And something going wrong can quickly go very, very wrong,” says Harris. Therefore, all governance and controls that apply to a human user must also apply to AI agents.
Adopting finance-grade AI also requires a shift in the way human team members approach their work. “Not every employee has to become a data scientist,” says Harris. Rather, professionals should receive basic training on how AI models work, the potential risks associated with using them, which tasks are most appropriate for AI to do and how to prompt and interact with agents to achieve optimal results.
As AI becomes more embedded in finance workflows, professionals will increasingly move from manually executing every step to reviewing, validating and guiding the work. That shift does not reduce accountability. In many ways, it increases it. As this dynamic technology evolves, CFOs that prioritize balancing speed with trust will position themselves for success in the AI era.
