I Investigated How AI is Used in Accounting. The Answer is Accounts Receivable.

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I get asked a lot about AI. People want to know where it’s actually making a difference in business right now. Not in the future. Today.

I was reading a report from CPA Practice Advisor and found a surprisingly clear answer: accounts receivable.

This isn’t a hypothesis. It’s a place where companies are using AI and seeing real, immediate impact. AR has all the ingredients that make a process ripe for AI. It involves reading unstructured data, making predictions, and communicating with customers.

Here’s a look at how AI is fundamentally changing how companies manage cash flow.

The Numbers Don’t Lie: A Big Impact on DSO

The first thing that caught my eye was the impact on Days Sales Outstanding (DSO). This is a core metric for working capital.

The study I read had some striking numbers.

• 99% of enterprises using AI in their AR process have reduced their DSO.
• 75% cut it by six days or more.

Why does this matter? It has an immediate cash flow impact. Small changes here translate to large amounts of cash. For example, if a $2 million revenue company trims just six days off its DSO, it frees up approximately $33,000 in cash.

How We Got Here: From Paper Invoices to Predictive AI

AR automation isn’t new. I’ve seen its evolution firsthand.

1. The Manual Era: This was all paper invoices, manual cash applications, and gut-feel collections.
2. Early Automation: Then came ERPs and EDI. We started using electronic invoicing and basic workflow automation with tools like Excel, Alteryx, and Power Query to build structure and reduce manual data entry.
3. The RPA Era: Robotic Process Automation (RPA) was a big step. It helped automate the repetitive clicks and simple tasks where you just had to do the same thing over and over.
4. The AI Leap: Now we’re in the AI and Machine Learning stage. We can use predictive analytics to figure out which customers are likely to pay late. We can use Generative AI to handle outreach. It’s a huge jump from the previous stages.

While older automation helped, it left behind persistent pain points. Things like manual exception handling, poor forecasting, and costly dispute resolution. AI is finally addressing these “thinking” parts of the process.

What’s Driving the Change with AI?

So what makes AI different from older automation? It comes down to a few key breakthroughs.

• Learning Systems vs. Rules-Based Systems: Older automation relied on complex, pre-defined rules. Think of massive “if-then” statements. Machine learning (ML) flips this around. It learns from the data to improve payment matching and make accurate predictions without needing every rule spelled out.
• Generative AI Adds Language: Generative AI brings communication to the table. It can draft outreach emails, summarize long dispute threads, and even power conversational self-service portals. This adds a layer of intelligent communication without adding more work for your team.
• Predictive and Agentic Capabilities: AI can forecast payment behavior and help with complex dispute resolution. It can take in a lot of context and facts to give your team a head start, saving them from reading through everything themselves.

What This Means for Accountants and Finance Teams

This shift has real implications for finance and accounting professionals.

First, it improves estimations for things like the allowance for doubtful accounts. AI provides more granular, forward-looking inputs like “payment propensity scores” to supplement traditional aging buckets. This leads to more accurate reserves.

Second, it introduces new control requirements. AI isn’t a magic wand. It’s a new system that must be governed with strong internal controls.

Key controls will include:

• Robust audit trails
• Strict change management for model updates
• Data quality validation
• Clear segregation of duties for configuring and overriding AI systems

You can’t just turn it on and walk away. You have to document your methodologies, perform testing, and make sure you aren’t over-reliant on the models. The controls burden doesn’t disappear, it just changes.

Key Takeaways

If someone asks you for a real-world example of AI in accounting, accounts receivable is the answer.

• The Benefits Are Measurable and Immediate. Nearly all adopters are reducing their Days Sales Outstanding and improving cash flow.
• It Uses Both Predictive and Generative AI. Machine learning helps with forecasting, while Generative AI improves communication and outreach.
• You Can Start Small. You don’t have to boil the ocean. Start by cleaning up your data, baselining your KPIs, and running a pilot with a human in the loop.
• It Has a Real Cash Impact. This isn’t just an efficiency play. It directly ties into your working capital strategy and puts cash back into the business.

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Further Reading

• AI is Reshaping Accounts Receivable: 99% of Enterprises Report Faster Payments – CPA Practice Advisor
• How AI Can Improve Cash Flow And Empower Accounts Receivable – Fazeshift
• How Predictive AI Is Transforming Accounts Receivable in 2025 – Gaviti
• A Complete Guide To Allowance For Doubtful Accounts (ADA) In Financial Accounting – Emagia
• How to Implement AI in Accounts Receivable – Fazeshift
• AI-powered Automation for Accounts Receivable Management – Esker