Sage announced new research on July 6 revealing that 71% of finance leaders would reject an AI tool that is 99% accurate if it could not explain its answers. The findings, drawn from a global IDC survey of 2,275 senior finance decision-makers, cut against the standard AI sales pitch that prioritizes speed and performance. For finance teams, an output that cannot be audited is an output that cannot be trusted in workflows tied to reporting, compliance, and financial decision-making.
The hidden cost of black-box AI
The survey quantifies what Sage calls the "verification tax." Finance professionals spend nearly 13 hours each week reconstructing, validating, and defending AI outputs. In the US, 49% spend 15 or more hours a week on verification, and 19% spend 30 or more hours. That means a model may produce an answer quickly, but the organization still pays if a person has to reverse-engineer the reasoning, check source data, and prepare for audit questions.
In high-stakes finance workflows, "almost right" can create material risk. An unexplained variance, forecast, cash-flow recommendation, or payment decision may not be usable if the team cannot trace how the system reached it. Explainability becomes an operating requirement, not a user-experience feature.
Glass-box AI and the procurement shift
Sage positioned the study around the shift from black-box AI to glass-box AI-systems that provide visibility into the reasoning, sources, and logic behind recommendations. The research found 71% of finance leaders say a vendor's move to glass-box design principles would strongly or critically elevate its status as a preferred partner. More than half of organizations would pay a premium for AI that gives greater visibility into how outputs are generated.
That changes the procurement conversation. Finance teams will still care about accuracy, usability, and productivity. But they will also demand to know how the AI system handles source data, reasoning traces, confidence, exceptions, approvals, audit logs, and human oversight. The vendor that cannot answer those questions may struggle, even if its model performs well.
Finance leadership leans into judgment
The study also shows how AI is reshaping what finance leadership skills are worth. When asked which skills matter most for a finance leader hired today, US respondents ranked risk, governance, and decision judgment first at 32%-nearly twice the share that selected deep technical accounting (17%). AI is shifting more of finance's value toward judgment, control, and accountability. As tools take on analytical work, leaders must decide when outputs can be trusted, when humans intervene, and how decisions will stand up to audit or board scrutiny.
For senior finance professionals, building the capacity to evaluate AI outputs is becoming essential. Resources like the AI Learning Path for CFOs help finance leaders develop the skills to govern AI systems, assess risk, and maintain auditability without becoming a bottleneck.
Why this matters for finance professionals
The research confirms that a 99% accurate AI tool can still fail if the remaining 1% creates audit exposure, compliance risk, or hours of manual verification. Finance teams will increasingly compare AI vendors by the cost of confidence-how much human effort it takes to trust, document, and defend the output. The winners in finance AI will be the systems that make trust cheaper, not the ones that only make answers faster. For individuals, the shift puts a premium on judgment, governance, and the ability to explain AI-driven decisions. Investing in targeted AI for Finance training can help professionals stay on the right side of that equation.
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