A big majority – 71% – of finance leaders would reject an AI tool that was 99% accurate if it could not explain its answers.
The findings from Sage’s new white paper, “The Emerging Economics of AI in Finance”, suggest that explainability is becoming a critical requirement for AI adoption in finance, as organisations seek greater confidence, control and accountability over AI-generated outputs.
Surveying over 2 000 senior finance decision-makers globally, the research found that more than half of organisations would be willing to pay more for AI solutions that provide greater visibility into how decisions are made. The findings indicate that explainability is becoming an increasingly important factor in how organisations evaluate AI.
“In finance, almost right has always been wrong. As AI takes on more complex financial workflows, the cost of uncertainty is simply too high,” says Aaron Harris, chief technology officer of Sage. “This research shows that the next era of AI won’t be won on raw model intelligence alone; it will be won on trust infrastructure. Finance teams cannot afford to spend hours playing detective with black box AI outputs. They need solutions that bring transparency, control, and traceability into the systems behind its outputs, so they can execute with absolute confidence.”
Key findings from the research include:
- The rise of the ‘Verification Tax’: Finance professionals spend nearly 13 hours every week reconstructing, validating, and defending AI outputs. Globally, nearly half (48%) spend 15+ hours a week on verification, with this figure at 47% in EMEA, while almost one in five (19%) spend 30+ hours.
- Accuracy without auditability is an unacceptable liability: The research shows that performance is not enough if AI cannot show its working, with more than half of organisations willing to pay a transparency premium.
- Finance teams are becoming AI’s trust layer: When asked what skills mattered most for a finance leader hired today, respondents placed risk, governance and decision judgment first, valuing it nearly twice as highly as deep technical accounting.
From Black Box to Glass Box AI
The findings point to a broader shift away from traditional Black Box AI systems, where outputs are difficult to interrogate, towards more transparent glass box approaches that provide visibility into the reasoning, sources and logic behind AI-generated recommendations.
Reflecting this trend, 71% of finance leaders say a vendor’s shift to Glass Box design principles would strongly or critically elevate their status as a preferred partner.
Kevin Permenter, research director: financial applications at IDC, comments: “The organisations that will achieve the most durable AI advantage are those that reframe trust infrastructure not as a constraint on AI deployment, but as the foundation on which scalable AI is built. Organisations have a choice, act early to operationalise trust or risk becoming overwhelmed by verification overhead.”
To read the full IDC White Paper, The Emerging Economics of AI in Finance, doc #EUR254634726-WP, June 2026 visit: https://sage.com/economics-agentic-ai