Quick Summary

AI is changing how companies collect cash. In this article, we explain how AI in accounts receivable helps to streamline workflows, walk you through steps to implement it, and show how Lunos, one of the best AI coworkers for AR teams, strengthens cash visibility and forecasting.

Today’s Accounts Receivable Teams Are Reaching a Breaking Point

To outperform today, accounts receivable teams need speed, efficiency, and a little bit of artificial intelligence.

Invoice volumes are rising fast, while critical data is scattered across inboxes, systems, and spreadsheets that don’t always connect smoothly. The result is slower collections and teams spending more time fixing system issues and repetitive tasks than on work that actually moves cash.

What once worked now yields unreliable forecasts and a widening competitive gap. But it doesn’t have to stay this way. 

Leading teams are using AI in accounts receivable to work smarter, not harder. In this article, we explain what AI in accounts receivable means, how AI simplifies AR workflows, and how you can implement it step by step.

But first…

Why Listen to Us

At Lunos, we work directly inside post-invoice collections every day. Our team brings over 50 years of combined experience across finance and payments, applied to building software shaped by the realities AR teams face under real pressure. We’re a global team that works inside post-invoice collections, helping finance teams keep things moving fast so they can get paid.

What Does “AI in Accounts Receivable” Really Mean?

When finance teams hear the term AI in accounts receivable, many assume it means ripping out existing systems or handing over full control to machines. In reality, it is far more grounded than that. 

Human collectors and analysts are not replaced. They still own judgment, strategy, and customer relationships, but they are no longer carrying the entire workload alone. They have an AI coworker to help them handle invoice volume, follow-ups, and the day-to-day tasks that typically drain time and energy.

When compared to traditional automation, AI accounts receivable software is more efficient. Traditional tools are rule-based and execute actions on fixed schedules, such as sending reminders after a set number of days, regardless of what the customer says or does. 

AI accounts receivable agents, on the other hand, read replies, understand intent, track payment promises, adjust timing, and follow up based on real conversations. This ability to adapt makes AI increasingly relevant in modern accounts receivable today.

8 Ways AI Is Changing Accounts Receivable

AI's role in accounts receivable cannot be overlooked. Here are eight ways it is changing day-to-day AR workflows and helping teams operate with confidence today.

1. It Responds to Customer Emails Quickly

With traditional AR workflows, it’s common to have customer replies go unopened or sit half-read in collectors’ inboxes.  This is one place AI is already changing accounts receivable. It can read inbound payment emails, understand what the customer is saying, and send an appropriate reply in the same thread. 

If the customer asks for a copy of an invoice or says they will pay on a certain date, it responds accordingly instead of ignoring the message or sending another generic reminder.  As a result, financial controllers can spend less time chasing context and more time acting on it, thereby shortening the gap between the invoice sent and cash received.

2. It Predicts Cash Flow Based on Live Customer Intent

With AI, accounts receivable analysts can build reliable cash flow forecasts by basing projections on real customer intent. Instead of waiting for invoices to age out, risk and confidence take shape at the same time conversations happen.

This leads to more accurate predictions, not because the assumptions are bulletproof, but because the emerging set of assumptions is informed by feedback from customers. Month-end closes feel more tangible, executives are less surprised by DSO, and cash visibility is enhanced without the need for extra processing to analyze.

3. It Prioritizes Accounts Based on Behavior, Not Static Rules

When all invoices look the same in a system, everything feels urgent. Finance managers jump from account to account in reactive mode, while high-priority signals get lost in the noise.

Using AI models, accounts are prioritized based on how they behave. Those who respond on time and pay as promised naturally move down the list and receive less attention. On the other hand, accounts that delay, hedge, or stop responding move up, while there is still time to do something about it.

Instead of trying to chase everything at once, attention goes to accounts that actually affect cash. Less effort is wasted on low-risk accounts, and collections improve because time is spent where it matters.

4. It Tracks Payment Promises and Adjusts Follow-Ups Automatically

Follow-ups are where most AR teams lose precious time. The work drags on, priorities blur, and as volume grows, it becomes harder to stay consistent.

Traditional schedules keep firing on day 7, day 14, or day 30, regardless of what the customer said yesterday. Teams can use an accounts receivable AI solution to automatically adjust follow-ups.

This means that collectors no longer have to remember who promised what or when. They step in only when judgment is needed and follow-ups remain steady and controlled.

5. It Keeps Track of Every Outreach, Reply, and Promise

AR work is full of “what did we say” and “when did they promise.” If that history lives only in email threads, it is very easy to lose the paper trail. 

AI helps teams to log every outreach, customer response, and payment promise in one place. This means that controllers can access critical data more easily and improve their forecasts.

Furthermore, if accounts escalate, teams remain protected because they can point to the exact sequence of communication. They no longer have to rely on memory or screenshots as the record is already there and can be reviewed quickly.

6. It Helps Match Payments to Invoices

Reconciliation becomes frustrating when payments, emails, and systems stop lining up. Time gets lost matching transactions, chasing down discrepancies, and fixing records after the fact. 

With an AI coworker, your payments and records stay aligned as they come in. This means you spend less time cleaning up, catch mismatches and inconsistencies early, and keep your balances accurate without having to manually correct them constantly.

7. It Flags Accounts That Are Likely to Slip

Overdue invoices rarely come without warning. Slower replies and vague payment commitments are signals that are easy to miss when teams are stretched. 

AI pays attention to these subtle shifts and catches them early. When patterns suggest risk, this software alerts finance managers to these accounts early, while outcomes can still change. 

As a result, teams have enough time to act thoughtfully rather than reactively. Collections also stop being a last-minute scramble, and cash flow becomes steadier.

8. It Absorbs Workload as Volume Grows

Traditional AR systems often break down as invoice volume increases. What worked at lower volume collapses when invoices multiply, and teams stay the same size. 

AI changes this dynamic by absorbing work even when volume increases, and team capacity is stretched. It continues to monitor inboxes, track responses, and send out follow-ups without any lag.

This helps prevent backlogs or burnout, allowing teams to scale collections without adding headcount and still achieve consistent results.

Signs Your Accounts Receivable Team Needs AI

How do you know when it is time to switch to an AI model for your accounts receivable team? Well, for most teams, it happens when collecting cash starts taking more effort than it should.

Sometimes it shows up as volume. Invoice counts grow, customer behavior seems just about unpredictable, and all you’re left with is partial payments, disputes, and long email threads.

For others, it looks like a slowdown. Your finance managers may spend hours copying between inboxes, ERPs, and spreadsheets instead of focusing on risk and resolution. Follow-ups may start to slip, and oversight ends up increasing the workload rather than decreasing it.

That is usually the point where an AI coworker starts to make sense. Not to replace judgment or people, but to support them by handling volume and repetition, so the team can focus on decisions that actually move cash.

How to Implement AI in Your Accounts Receivable: 7 Practical Steps for Teams

Adopting AI in accounts receivable works best when it is done deliberately. These seven steps outline how you can implement AI into your AR processes without disrupting any existing workflows.

1. Map Out Your Current Accounts Receivable Workflows

The first step is to understand how your work actually flows day-to-day, not how it’s supposed to. 

Start by tracing what happens from the moment a customer receives an invoice till the cash is received. Next, make notes of what you see. Is the process fragmented across multiple tools? What are the things preventing teams from acting optimally? What part of the process is routine or judgment-heavy? 

This will help you gain key insight into your cash collection process before you start optimizing it. Getting this clarity is the first step toward ensuring that AI is not layered on top of chaos, but rather reduces it.

2. Identify Where Decisions Slow the Team Down

AI delivers the most value where decisions create friction. Identify those moments where collectors pause to interpret replies, decide whether to follow up, or determine if an issue is a dispute or a delay. 

These decisions consume far more time than teams realize. Flagging these decision-heavy points helps avoid automating the wrong things. 

3. Prepare and Centralize Your AR Data

AI doesn’t need perfect data, but it does need access to the right data. Start by gathering your key AR inputs, like customer communication, invoice status, and payment history, into one place. 

Don’t worry about how clean the data is just yet. Cleaning it up matters later for smooth integrations and reporting, but for now, focus on getting insights. When data remains scattered, AI cannot see the full picture.

4. Start With One High-Impact Use Case

Trying to implement AI as widely as possible often backfires. Start where the pain is not only the most acute but also where the value will be easiest to measure.

For some teams, this may be follow-ups; for others, it may be inbox triage. Regardless of which one it is, choose one case that is causing recurring issues. 

This will help your team to understand how AI fits into daily work. Also, once people see some results, they’re less likely to approach the new system with resistance.

5. Define Human-in-the-Loop Controls Early

One of the biggest concerns teams have is loss of control. You want to make sure this is addressed upfront.

Determine the scenarios in which AI systems can operate autonomously and those in which human validation is necessary. These guidelines will instill confidence in AI systems and help prevent any mistakes.

It will also help safeguard customer relations in highly sensitive contexts that require human intervention. Always remember that AI systems perform better as teammates rather than replacements at work.

6. Set Performance and Safety Metrics

Don’t measure implementation just by DSO alone. Also, keep a finger on metrics such as workload reduction, response handling time, and follow-up consistency, as they reflect operational health, not just financial outcomes. 

Safety matters too. Look at how often humans have to intervene, where there are exception clusters, and whether messaging remains appropriate. These signals help fine-tune behavior without stopping progress.

7. Expand Incrementally Based on Results

Finally, remember to expand based on results, not ambition. Once the first use case is stable, review your results to determine whether you can expand to additional use cases.

Is the workload still high? What of visibility? Is it still lacking? 

Make sure that AI support is added gradually, one function at a time. This will keep changes manageable and help prevent disruption. Additionally, incremental rollouts allow teams to adjust processes as they learn. 

Real-World Example of AI in the Workplace

When AI is introduced the right way, it feels like extra hands keeping things steady when the workload piles up. That’s what ServiceUp experienced after introducing Lunos into its accounts receivable workflow.

Before, collections felt overwhelming, replies were easy to miss, and staying on top of everything took constant effort. With Lunos as an AI coworker, it worked around the clock to manage customer conversations. It read responses, handled outreach, tracked payment commitments, and responded naturally rather than sending generic reminder templates.

The result? Customer conversations stayed active, commitments were tracked, and the team stayed in control. They continued working with the tools they already used, gained clearer visibility across accounts, and nothing fell through the cracks as volume increased.

Within weeks, collections felt manageable again. Stress dropped, confidence returned, and AR stopped feeling like a constant fire drill.

5 Common Mistakes Teams Make when Adopting AI in Accounts Receivable

When AI adoption falls short, it is rarely because the technology cannot work. These are the five most common mistakes teams make while adopting it.

  1. Treating AI as a One-Off Project: There are teams that start working on AI, see results once, and then forget about it.  This limits its impact. When AI adoption stops at “go live,” learning stalls and value plateaus. 
  2. Over-Automating Relationship-Sensitive Tasks: Accounts receivable depends on tone, timing, and context. Teams that automate everything risk damaging customer relationships. Strong teams are deliberate about where AI runs independently and where human judgment stays involved.
  3. Starting in Too Many Places at Once: Trying to deploy AI across every AR process creates confusion. Teams jump into complex scenarios before proving value in simpler ones. That overwhelms users and makes it hard to see what is actually working. 
  4. Measuring Success with the Wrong Metrics: Looking only at DSO hides progress. Early improvements often show up as steadier follow-ups, fewer missed replies, or clearer payment intent. When those signals are ignored, momentum fades even when operations are improving. 
  5. Ignoring Control and Transparency Concerns: If team members don’t know when AI is acting, suggesting, or escalating, their confidence erodes quickly. People hesitate, second-guess outcomes, and override decisions just to be safe.

How to Avoid These Mistakes

Teams that succeed treat AI as part of daily AR work, not a quick fix. They stay involved where judgment matters, expand carefully, and focus on consistency and visibility. Used this way, AI supports faster, more predictable collections without slowing down operations.

Wrapping Up: AI Is Bringing Order Back to Modern Receivables

When it comes to accounts receivable, agility is survival. As volume, complexity, and pressure rise, AI in accounts receivable keeps teams afloat and in control of their work. Used well, it supports collectors without stripping away the judgment and relationships that collections still depend on.

For teams ready to put these ideas into practice, Lunos shows what this looks like day-to-day. As an AI partner, it tracks conversations and promises, and keeps follow-ups steady so nothing slips through the cracks. Start for free today if you’re ready to see it in action.