Rillion: Finance Teams Demand Transparency and Measurable Results from AI

The adoption of artificial intelligence (AI) in the finance sector is rapidly accelerating, with automation and machine learning tools promising to reduce manual work and provide unprecedented insights. However, a central tension is emerging: trust in AI cannot be assumed, according to a new Finance Labs report by Rillion.

Finance teams are increasingly being asked to delegate critical processes, from invoice coding to forecasting, to systems that operate like a "black box." Without visibility into AI-driven decision-making, leaders are hesitant to fully rely on automation. The report emphasizes that trust is built on transparency, measurable performance, and meaningful oversight.

Based on a survey of over 100 US finance leaders, the report found that transparency is a non-negotiable foundation for trust. Nearly every respondent indicated hesitation in using AI for tasks like invoice processing or forecasting if its decision-making couldn't be explained. A significant portion (47.6%) requires full auditability of every decision, while another 40% deem basic reasoning for key outputs sufficient.

"For finance leaders, trust in AI starts with visibility. If we lack a clear understanding of how an AI reaches its decisions, it's difficult to rely on it for something as critical as invoice processing or forecasting," stated Kristian Gylling, CFO at Rillion. He stressed the importance of strong governance around new AI tools, balanced with the freedom for teams to experiment and discover AI's value.

Beyond transparency, measurable results are key to building confidence. The survey data shows that over 40% of finance leaders have already defined key performance indicators (KPIs) to evaluate AI performance, including accuracy in invoice coding, touchless transaction rates, and time savings. This measurement approach is vital for verifying ROI and ensuring compliance during audits.