AI adoption in finance is accelerating at an unprecedented pace. Automation and machine learning tools promise to reduce manual work, accelerate processes, and provide insights that were previously impossible to achieve at scale. Yet as CFOs and finance leaders explore these opportunities, a central tension is becoming clear: trust in AI cannot be assumed.
Finance teams are being asked to hand over critical processes — from invoice coding to forecasting — to systems that may feel like a “black box.” Without visibility into how decisions are made, leaders are hesitant to rely fully on automation. Trust grows only when tools provide transparency, measurable performance, and meaningful oversight.
This month’s Finance Labs report explores what finance leaders expect from AI tools, why transparency is a must-have, and how organizations can build the confidence needed to unlock AI’s full potential.
Key takeaways
- Transparency is non-negotiable. Finance leaders will not adopt AI tools they cannot explain, audit, or control.
- Trust is tied to measurable results. Defined KPIs, performance tracking, and accuracy thresholds are essential for confidence in AI outputs.
- Adoption requires balance. Governance must ensure compliance and control without paralyzing teams’ ability to experiment and innovate.
AI transparency in finance is a must-have
Our survey of over 100 CFOs and finance leaders from across the US shows a clear consensus: Finance leaders consider transparency the foundation of trust in AI. Nearly every respondent said they would hesitate to use AI in processes like invoice processing or forecasting if they couldn’t explain how decisions were made.
When asked about the importance of transparency in AI, 47.6% of finance leaders said they require "full auditability of every decision and output," with another 40% saying "basic reasoning for key outputs (e.g. coding choices)" is enough. 11.4% stated that they are fine with "high-level explanations", while only 1% said "we don’t require transparency if the results are accurate".
Essentially, finance teams see visibility into AI decision-making as a prerequisite for adoption. This expectation is more than a cultural preference — it’s a compliance requirement. Finance teams must be audit-ready, able to justify every number, and prove adherence to internal controls. An AI tool that can’t explain its reasoning creates unacceptable risk.
As Kristian Gylling, CFO at Rillion, puts it:
Transparency also enables process optimization. With visibility into how AI arrives at decisions, teams can identify errors, improve training data, and refine performance over time. Far from slowing teams down, explainability drives continuous improvement.
How finance teams measure AI performance
Trust in AI is not just about visibility; it’s about proof. Finance leaders want clear evidence that automation is working as intended. Measurement is the bridge between confidence and capability.
Rillion survey data shows that 41.9% of finance leaders have already defined KPIs for measuring AI performance, with another 41.9% assessing success based on team feedback and satisfaction. 8.6% admitted they have no performance measures in place, while 7.6% said they track adoption rates and usage only.
This reveal that measurement is no longer optional; most teams already track success. Some of the most effective KPIs for AI in finance include:
- Coding accuracy: The percentage of invoices correctly coded by AI without human intervention.
- Touchless rate: The proportion of transactions processed end-to-end without manual involvement.
- Time saved: The reduction in hours spent on manual validation and corrections.
- Exception handling rates: The percentage of cases where AI appropriately flags anomalies or escalates issues.
By tracking these metrics, finance teams can verify performance, demonstrate ROI, and refine thresholds for automation. Measurement also supports stronger governance by giving leaders data to present during audits or compliance reviews.
Trust depends on accuracy
Accuracy is the single greatest driver of trust in AI. Finance teams are willing to automate, but only when they are confident that AI consistently meets or exceeds defined accuracy thresholds.
When asked if they consider AI-generated predictions trustworthy enough for automation without human checks, 55.2% of finance leaders said "yes, if accuracy is consistently high". 26.7% trust AI only for low-risk transactions, while 17.1% always require human review. Just 1% admitted they haven't tested AI predictions yet.
If AI proves itself accurate, the benefits are immediate: fewer manual checks, faster cycle times, and lower cost per transaction. For example, when invoice processing accuracy rises above 90%, teams can confidently reduce manual validations. This not only accelerates payment cycles but also frees staff to focus on higher-value work such as vendor management and financial analysis.
Emil Fleron, AI Engineer at Rillion, says:
Why transparency and control matter for AI adoption
Without transparency and control, AI adoption risks stalling. Low confidence forces teams to insert manual checks into every process, driving up costs and eroding the time savings AI is meant to deliver.
Worse, organizations expose themselves to strategic risks if they cannot demonstrate compliance or auditability. Finance is not just about efficiency, but also accountability. If AI systems cannot provide evidence, leaders face tough questions from regulators, auditors, and boards.
Control mechanisms provide the middle ground. By defining when AI acts autonomously and when human validation is required, finance leaders create an adoption framework that builds confidence without ceding control. This balance is critical for scaling AI responsibly.
Conclusion: Trust is earned, not given
For finance leaders, AI adoption will succeed only when tools are transparent, measurable, and controllable. Trust is not a marketing promise. It is built gradually, through visibility into decisions, proven accuracy, and oversight mechanisms that ensure compliance.
Partnering with experienced providers can accelerate this trust-building process. The right solutions are not generic AI platforms, but specialized tools designed for finance. For example:
- Specialized AI tools: Choose niche solutions like Rillion trained on historical finance patterns. Unlike general-purpose AI tools, these systems provide greater transparency and are less prone to hallucinations.
- Clear performance insights: In Rillion Analytics, users can track coding accuracy, workflow success rates, and accuracy trends to validate results in real time.
- Control over automation vs. oversight: Rillion allows finance leaders to define tolerance levels for AI predictions, route low-confidence cases for human review, and select which suppliers AI is authorized to handle.
The message is clear: AI can transform finance, but adoption depends on trust. And trust is earned only when tools give leaders the visibility, accuracy, and control they require.
Reach out to Rillion to explore how AI in accounts payable can help your team achieve measurable results with confidence.
What’s The Finance Labs?
The Finance Labs by Rillion is your go-to source for finance automation insights. Each month, we deliver bite-sized reports designed for CFOs and finance leaders, packed with the latest trends in finance and accounts payable automation.
Our insights are backed by real-world data from Rillion’s platform and anonymous surveys of finance leaders across the US and EMEA.
