The ground beneath finance leaders is shifting. In this age of faster technology, new risks, and relentless economic change, most accounts receivable (AR) operations can’t keep up. Data shows 80% of AR teams are being asked to do more despite subpar technologies. As business-to-business (B2B) suppliers steer through today’s financial storms, more are rethinking their AR tools and using AI to accelerate cash flow, strengthen their financial resilience, retain customers, mitigate risk, and enhance productivity.
These are just some of the highlights from a new research study aimed at unpacking the state of AI in accounts receivable.
AI intelligence has become the new business edge, delivering results once out of reach. But with every tech evolution comes with new trials and tribulations. Challenges, fears, and varying maturity levels. To get the full picture, Billtrust partnered with Wakefield Research to survey 500 finance decision-makers across many industries.
AR Meets AI
Data-Driven Insights from 500 Global Finance Leaders
With a mere 4% believing that AI in AR isn’t a good idea, almost everyone has skin in the game. If you want to better understand how AI is reshaping AR, where leaders are gaining ground, and what’s at stake for those who don’t move fast enough, keep reading for the big insights.
Leaders Agree: AI in AR Delivers Real, Measurable Impact
Let’s start with what every CFO wants to know: Indeed, AI in AR delivers measurable, bottom-line impact. It’s proven to be a powerful tool for protecting cash flow – today, tomorrow, and in the future.
- 99% of companies using AI have reduced Days Sales Outstanding (DSO)
- 75% saw DSO drop by 6+ days, and 14% by 11+ days
- 82% measurably improved productivity and scalability, all without adding headcount
- Almost half (47%) improved customer payment relationships and (43%) saw more predictable, stable cash flow
Nearly 90% of finance leaders agree that AI isn’t optional anymore. It’s essential for scaling AR and delivering the results today’s teams are under pressure to produce.
Where Enterprise Finance Leaders Use AI Most
Monitoring for behavioral anomalies and unusual activity (47%)
Every day, something shifts. A customer who always pays on time suddenly doesn’t, or a payment isn’t what you expected. Every moment is part of a bigger picture, but it’s impossible to track those moments across hundreds of accounts.
AI keeps a pulse on the details and the macrotrends, flagging unusual patterns, rising anomalies, and anything else that appears out-of-the-norm. Better still, advanced AI in AR goes beyond just alerting. It brings recommendations and performance improvement ideas, so AR teams can act fast and strategically. No more interpreting the data and trying to turn it into an action plan.
Confidence-based, ML-driven cash application (47%)
Machine learning (ML) -driven cash application is built on automation and confidence-based matching. This simply means that AI is:
- Equipped to handle unstructured data for every new payment that comes in. It adapts to new locations as invoice layouts change and when payment details are split across different locations or received in two different emails, for example.
- Analyzes every data match or reconciliation activity to automatically generate a confidence score based on how certain it is that the payment belongs to a specific invoice. That score is based on a threshold you set – a cutoff point that determines which payments get auto-applied versus which ones get routed for human review and manual follow-up. (This is the secret to reducing manual workloads.)
- Learns from historical matches and human validations, improving accuracy, and reducing manual workload. It’s a game-changer compared to rigid rules that only work when data aligns perfectly.
“We’re now working with a system that learns from every transaction. That shift has completely changed how we train, strategize, and tackle day-to-day work.”
Kerry Banks, Cash Application Manager at Cintas
Monitoring real-time credit worthiness (46%)
A customer’s credit health can change overnight, and those changes are easy to miss when you’re relying on static credit reports. AI monitors across every account in real-time, spotting shifts as they happen so you can act quickly and reduce risk.
Predictive payment forecasting (46%)
When monitoring accounts receivable trends in real time, AI also becomes a predictive powerhouse. Payment forecasting uses historical data and machine learning to anticipate future payment behaviors, helping businesses anticipate and therefore reduce late payments. It enables proactive decision-making by predicting and identifying potential risks. Learn more about predictive AI in AR.
Empowered by AI: AR Can Now Enter its Strategic Era
When AI in AR takes care of routine work faster and more accurately than humans ever could, teams have more time for strategic work. The study’s findings give a glimpse into how finance leaders envision the future and where they’re refocusing the attention of their departments with new-found time savings:
- Compliance and risk management (62%): identifying risks early, ensuring electronic invoice compliance, and maintaining data accuracy to protect financial integrity
- Financial analysis and forecasting (57%): With AI connecting the dots, humans can dive deep into the critical insights about credit, liquidity, and the buyer experience – everything executives need to know to confidently drive growth and innovation
- Strategic planning and process improvement (55%): Teams have more time for clarity and direction — mental space for rethinking workflows, automating smarter, improving policies to move cash faster and making the buyer experience smoother.
- Training and development (52%): Nearly 90% of finance leaders say they won’t fully capitalize on AI’s potential until their teams update their mindset about using it. AI is fast, new, and changes how employees have worked their whole careers – there’s a lot to be wary of. Shifting that mindset is crucial, and it takes time and intentionality.
- Customer relationship building (50%): AR is a key finance function that touches the customer. With AI handling more administrative work, teams can resolve complex issues early, ensure that empathy is conveyed, and deliver the type of service quality that keeps people coming back for more.
Leaders Agree on AI’s Value, but They’re Not Ready to Trust
Most leaders agree that AI in AR is vital, but they draw different lines between what AI should handle and what still requires human management. In essence, they’re struggling to balance innovation with control.
Here’s where they currently stand.
- AI advocates (29%): These leaders see AI as an essential part of AR’s future – not just for automation, but as a core engine for strategy and decision-making. They’re comfortable letting AI run, so long as there are guardrails and transparency in place.
- AI pragmatists (40%): This is the majority. They want AI to assist meaningfully, but within well-defined limits that humans control (i.e., AI drafts and humans approve; AI matches and humans verify, AI alerts and humans act).
- AI skeptics (26%): These leaders are still cautious. They’re fine using AI for narrow tasks like sending reminders, but only under strict restrictions and with a human in the loop. What’s most interesting? Nearly half of C-level executives fall into this group.
- AI opponents (4%): A small minority still prefer AR to stay fully human-driven – likely due to trust issues, regulatory concerns, or fear of losing control.
Amid diversity, one point is universally accepted
Despite these differences, almost everyone agrees that AI and humans will need to work together long-term. That’s why 97% expect “fact-checking” or reviewing AI-generated work to become a standard part of AR. This points to a future built on collaboration vs. replacement – a reassuring sign for those still cautious.
Hold-ups, Hang-ups, and How to Break Through
Despite the proven benefits of AI in AR, adoption isn’t always a straight path. Familiar challenges keep getting in the way: integration, outdated technology, employee resistance, limited skillsets, and tight budgets. In fact, a previous study also found the biggest barrier to AR process automation is integrating AR with other systems – like your ERP.
The right AR software provider will help you clear these hurdles with ease.
How Billtrust Smooths Your Path to AI Automation
Here’s how Billtrust sets the bar.
- We seamlessly tie into existing systems with dozens of connectors to ERPs, banks, and financial institutions. There’s 260+ AP portal integrations and custom integrations are also available, creating an on-ramp to success.
- We’ll work closely with you to build trust. Remember Cintas, the client we mentioned above? Before they could see the full value of ML-driven cash application, their team needed to get comfortable with the idea of AI in AR. Billtrust guided them through that journey, not just implementing AI but creating a clear strategy to earn buy-in.
- Our platform empowers you to start using AI at every level – from basic automation to next-gen innovation like GenAI and Agentic AI – without needing to understand how it all works. Even the least tech-savvy person on your team should find the tool easy to use with intuitive dashboards, guided insights, and one-click approvals.
We can’t change your opinion on AI in AR, but we can say that the more you understand how AI makes decisions – and the more success you see firsthand – the faster trust is established. The study validates this: 71% of finance leaders plan to increase investment in AI for AR over the next 12 months, and those who actively use AI plan to spend even more.
Quick tip: Where to Start
Start small, then scale. Prove value with one AR function – one win – and expand from there. If you were to ask our experts where to start, they’d tell you wherever your biggest challenges are – say payments, cash application, or collections. The best solutions will grow with you, allowing you expand across the order-to-cash cycle.
Ready for the AR Revolution?
With almost all finance leaders agreeing that AI in AR is non-negotiable, the risk of standing still is real: more cash trapped in manual processes, more risk hiding in plain sight, and more opportunities falling through the cracks.
The technology is ready, and the data proves it works. Get the complete research findings for even more insights.
