AI adoption in finance has entered a new phase. The conversation has long been about automating faster, processing more transactions with fewer hands, and cutting costs. But top-performing finance teams are proving that efficiency is just the beginning.
Today’s CFOs are asking tougher questions:
- Is AI actually improving accuracy?
- Is it helping us stay compliant?
- Can we prove the return on investment to the board, and what does that look like at this point?
Key takeaways
- Measurement is the norm, but methods vary. A clear majority of finance teams track AI success, with 41.9% using defined KPIs and another 41.9% relying on feedback. This highlights a maturity gap between structured and unstructured measurement.
- ROI and ERP integration drive investments. CFOs want proof that AI delivers measurable value (54.3%) and integrates seamlessly with their core finance systems (49.5%).
- Excitement follows assurance. 41% of finance leaders are most interested in AI for fraud and anomaly detection, showing that reducing risk is their top priority for future use cases.
How finance teams are measuring the success of AI implementations
When it comes to implementing AI in finance, success is not binary. Most teams look beyond simple adoption metrics:
- 41.9% use clearly defined KPIs (e.g. error reduction, time saved) to evaluate AI success.
- 41.9% rely on team feedback and satisfaction.
- 8.6% have no defined measurement.
- 7.6% track adoption rates alone.
It’s clear that most finance teams are already measuring outcomes, not just usage. But there’s a maturity gap between structured and unstructured measurement to be addressed here.
Sure, feedback from the team can bring a lot of valuable insights. They can tell you about their experience and whether they’re happy with AI’s performance or not.
So why are outcome-based KPIs important?
Teams that track defined KPIs can quickly identify what’s working, reduce unnecessary human checks, and confidently expand automation. It's easier to build a business case or see the true impact when you have the hard data to back it up.
As Rillion’s CFO, Kristian Gylling, puts it:
“Measuring success is how you turn AI from an experiment into a trusted part of your finance process. Without clear KPIs, it’s impossible to know if automation is actually saving time, reducing errors, or improving compliance. When we track those metrics, we can scale automation with confidence.”
Why KPIs matter more than AI adoption rates
Tracking logins or usage tells you very little about whether AI is actually helping. Outcome-based KPIs answer the questions that matter to CFOs. Let’s take accounts payable as an example:
- Coding accuracy: Are invoices coded correctly without human intervention?
- Touchless rate: How many invoices flow through the process end-to-end without manual touches?
- Time saved: How many hours are reclaimed from manual work?
- Exception handling rates: How often does AI flag issues correctly?
What drives AI investments in finance?
Finance leaders are not taking risks lightly. When asked what would make them more likely to invest in AI:
- 54.3% said proven ROI or case studies.
- 49.5% prioritized seamless ERP integration.
- 40% want built-in compliance and auditability.
- 18.1% look for end-user simplicity and low learning curve.
- 11.4% value strong vendor support and training.
This shows that measurement and integration are basic requirements for investment. ROI must be demonstrable, and results must flow back into systems that finance leaders trust. That way, they can build the business case for scaling automation and avoid adoption stalls.
Mikael Rask, CPTO at Rillion, comments:
“ROI isn’t just about money saved, it’s also about risk avoided. The teams we see succeed with AI are the ones who measure accuracy, monitor exceptions, and continuously use the system so it learns from past behavior. That’s how they keep performance high and justify further investment.”
Compliance and auditability also ranked high. In other words, success is not measured by speed alone — it’s measured by whether the organization can pass an audit and prove every decision was sound.
Which AI use cases excite finance leaders the most?
Real-time fraud and anomaly detection is by far the use case that gets most finance leaders on the edge of their seats:
- 41% said real-time anomaly and fraud detection tops their list.
- 20% selected personalized insights for decision-making.
- 16.2% want fully autonomous invoice-to-payment processes.
- 15.2% are looking for predictive cash flow and scenario planning.
- 7.6% chose AI-driven budget optimization.
This finding shows that finance teams are not only looking for efficiency gains from using AI. What they are really after is to reduce risk and strengthen control. After all, finance leaders are the guardians of their business, protecting the company from financial loss and reputational damage.
Build a framework to measure the success of AI
The success of AI implementations isn’t black and white. It has to be measured and tracked to be evaluated properly. If your finance team is ready to move into the fields of AI, here’s how to build a measurement framework that scales AI responsibly:
1. Define success upfront
Don’t wait until after implementation to decide what success looks like.
- Operational KPIs: Accuracy rate, touchless processing rate, average cycle time, hours saved, number of fraud cases prevented.
- Financial KPIs: Cost per invoice processed, savings from reduced manual work, vendor discounts captured from faster payments, financial losses avoided.
- Strategic KPIs: Improved forecasting accuracy, better decision-making insights.
CFO tip: Anchor KPIs to strategic priorities — efficiency, risk, and control — so results are meaningful at the board, management, and other executive levels.
2. Include compliance and risk metrics
Efficiency alone doesn’t win trust. Finance teams also need evidence that AI strengthens governance.
- Exception handling: Percentage of anomalies flagged correctly.
- Audit trail completeness: Every AI-driven action must be traceable.
- Policy adherence: Track whether AI recommendations align with company rules or regulatory requirements.
CFO tip: Use compliance metrics as strategic evidence. They satisfy auditors as much as they strengthen governance and help you demonstrate control to management, boards, and stakeholders.
3. Establish feedback loops
Measurement isn’t just numbers. It’s also about learning and refining.
- User feedback: Collect qualitative insights from AP clerks and finance staff. They can add more nuance to the hard data.
- Performance reviews: Evaluate results quarterly against KPIs.
- Threshold tuning: Adjust confidence levels or automation rules based on trends.
CFO tip: Combine KPIs with human feedback to capture both performance and usability.
4. Communicate results broadly
AI success must be visible in the finance team and beyond.
- Report results to executive leadership to justify further investment.
- Share progress with staff to build confidence in automation.
- Highlight measurable wins (like “95% coding accuracy achieved,” “1,200 hours saved”) to maintain momentum.
CFO tip: Framing AI results as both a cost saver and a risk reducer is most effective when reporting or communicating to management, boards, and auditors.
5. Iterate and expand
Measurement is not one-and-done.
- Use trends to identify weak spots in data or processes.
- Expand automation into new areas once KPIs prove stable.
- Continuously benchmark against industry peers to stay ahead.
CFO tip: Treat measurement as your growth lever. The more disciplined your tracking, the more confidently you can scale AI adoption.
Final thoughts: Measuring AI’s impact sets up for success, now and in the future
The only way to know if AI projects succeed is to measure the outcomes. Consistently and transparently.
Finance teams that do this reduce unnecessary manual checks, accelerate processing times, and lower costs, all while staying in control of compliance and risk. They are also the ones most prepared to expand into more use cases such as fraud detection and personalized insights.
Curious to see measurable results from AI in accounts payable? Book a demo with Rillion and see how we help finance teams achieve 90% touchless invoices, close the books faster, and regain control.
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.
