Thinking about using AI in Accounts Receivable (AR), but worried about how your longtime customers will react? That concern is worth taking seriously in construction supply, where accounts are built on years of trust. We recommend starting with work your customers never see, then giving AI more of the job as your team learns to trust it. Here’s how to approach each stage, and what to ask before moving to the next.

The short answer: start AI on internal work like cash application, and give it customer-facing work only after it has proven itself on your accounts, with your team reviewing along the way.

Why construction suppliers should be deliberate about AI

This industry runs on relationships. Many of your customers have bought from you for years, and they know your credit manager by name. A collections email that reads like it came from a machine lands differently with a contractor like that than it would with a new account.

Payment timing also depends on things your customers don’t control. Subcontractors and trade contractors often wait on the general contractor to be paid before they can pay you. A collections approach that treats every late invoice the same way can strain an account that is doing what it can.

So the goal is to use AI where it removes manual work without weakening those relationships. When efficiency and customer service pull in different directions, side with the relationship.

AI vs. automation in accounts receivable

At a recent AD show, many of the people we talked with used “AI” to describe any software that does work on its own. Much of what gets called AI in AR is AR automation: rules your team sets up that run the same way each time, such as sending a reminder before an invoice comes due.

AI handles work that doesn’t fit a fixed rule, and it learns from your team’s corrections. In Dill, sending monthly statements is an automation. Matching a payment that came in short to the right invoices is AI.

Here’s an example. A contractor pays $98 on a $100 invoice. A fixed rule can’t tell whether the $2 is an early-payment discount, a sales tax exemption, or a bank fee. AI can check the customer’s terms and history, propose the most likely reason, and learn from your team when it guesses wrong.

When you evaluate AR tools, ask which features are rules and which are AI. For plenty of tasks, a well-built automation is the better fit.

Where should you start with AI in AR?

We recommend starting with internal work, where a mistake can be caught before a customer notices it. AI cash application is a good first candidate. Matching incoming payments to open invoices is repetitive, and the hard cases follow patterns AI can learn. Analysts point to it too: Forrester lists cash application, along with collection management, among the top AI use cases for AR.

Internal work gives your team a low-risk way to learn how AI behaves. They can see which matches AI gets right and how it improves after they correct it. That experience tells you whether you’re ready to trust AI with more.

Another good early task is letting AI recommend how and when to reach each past-due customer, while your team decides what to do. Using AI in collections to draft notices for your team to edit is a sensible next step, since the notice reaches a customer only after someone on your team reviews it.

We suggest holding off on anything a customer hears from you directly until your team has watched AI work on internal tasks.

Get your data and your team ready

AI works from the information in your systems. Before you start, check that the open invoices, customer records, and payment terms in your ERP are accurate, since an error there becomes an error in what AI proposes. Ask how the tool connects to your ERP, and whether it posts results back automatically or leaves posting to your team.

You’ll also want to bring your credit and AR team in early. They know which customers take discounts and which accounts need a phone call instead of an email. Their corrections are also what AI learns from, so their buy-in affects how well it works.

Then run a small pilot. Pick one task, such as cash application for one branch, and review the results until your team is comfortable before you expand.

How far should you take AI in AR?

The right amount of AI depends on your team’s comfort level. We think about AI in accounts receivable at three levels of automation, and we recommend moving through them in order.

Suggestions. AI recommends, and your team acts. In cash application, AI proposes a match and a person posts it. In collections, AI might suggest how to reach a customer, how firm to be, and how often to follow up. Your team keeps every decision and gets a feel for how often AI is right.

Assisted. AI does the work, and a person approves it. AI prepares the match or drafts the notice, and someone on your team reviews it before anything is posted or sent. This saves more time than suggestions while keeping a person at every handoff.

Full agentic. AI handles the task end to end and learns from your team’s feedback. Your team steps in on exceptions instead of reviewing every item. We recommend this level for work where AI has a track record with your data and where an error is easy to catch and fix.

Each task can move at its own pace. A team might let AI post high-confidence cash matches on its own while still approving every collection notice. We think that mix makes sense, since the two tasks carry different risks to your customer relationships.

Keep your team in control at every level

Whatever level you choose, your team should be able to see into AI’s work and override it. These are the controls we recommend looking for in any tool.

Confidence thresholds. Look for AI that gives each decision a confidence score. You set the score AI must reach before it acts on its own, and anything below it goes to a person. We recommend starting with a high threshold and lowering it as you see how AI performs on your accounts. In Dill’s AI Cash Application, for example, each match gets a confidence score, and your team sets the level at which matches post on their own. Everything below that level goes to a review queue.

Dollar limits. Some decisions should reach a person whatever the confidence score. Ask whether you can set an amount above which a short pay or write-off always goes to review. In Dill, your team can set that amount in AI Cash Application.

A review queue. The items AI isn’t sure about should land in a queue your team works through, with enough detail to see what AI proposed and why. Each correction your team makes there is feedback AI can learn from.

An off switch. You should be able to turn AI off for a task, or drop back a level, without waiting on the vendor. Knowing you can pull back makes it easier for your team to agree to the next step.

A record of each decision. Look for a log that shows what AI did, the confidence score behind it, and who on your team approved or changed it. That record helps when a customer questions a payment, and when you’re deciding whether to give AI more of the work. Dill keeps this history for every AI Cash Application match.

Clear answers about your data. Ask where your customer and payment data is stored, who can access it, and whether it’s used to train AI for other companies.

How to tell whether AI is working

Measure where you are before you start, so you have a baseline to compare against. Then track:

• How often AI matches payments correctly on your accounts, and whether that improves after corrections

• How many items land in the review queue each week

• How often your team overrides AI

• How much time your team spends on manual matching and follow-up

• Questions or complaints from customers about payments or notices

Dill shows your match rate and override counts, so your team doesn’t have to track them by hand. DSO is worth watching over a longer stretch, but keep in mind that it also depends on how quickly general contractors pay your customers.

Questions to ask before giving AI more of your accounts receivable work

Before moving a task up a level, we recommend your team talk through these questions together:

• How often has your team overridden AI on this task lately, and is that happening less over time?

• When AI gets something wrong, how quickly does someone notice, and how hard is it to fix?

• Does the task touch customers, directly or through a message they’ll read?

• Would a longtime customer notice a change in how you work with them, and would they mind?

• Is your team comfortable with the change, or being asked to trust something they haven’t seen work?

• Can you drop back to the previous level quickly if results slip?

• Would a simple automation handle this task just as well?

When AI has been accurate for a while and its mistakes are easy to fix, the task is likely ready for more automation. Answers that raise doubts are a reason to stay at the current level a while longer.

To map where AI fits in your own AR department, download our AI worksheet.

Frequently asked questions about AI in accounts receivable

What is AI in accounts receivable?

AI in accounts receivable handles AR work that doesn’t fit a fixed rule, such as matching a payment to the right invoices when the amount comes in short. It learns from your team’s corrections over time.

What’s the difference between AI and AR automation?

AR automation follows rules your team sets and runs the same way each time, such as sending monthly statements or payment reminders. AI makes judgment calls and improves as your team corrects it.

Where should a construction supplier start with AI in AR?

We recommend starting with internal work your customers never see. AI cash application is a good first step, since a person can catch a wrong match before it reaches a customer.

Will AI replace our credit or AR team?

It shouldn’t. Use AI to take manual work off your team’s plate, and keep the people who own customer relationships and credit decisions in charge of them.

How much control should we keep over AI?

Your team should be able to override AI’s work or switch it off at any time. Confidence thresholds and a review queue let you decide which work AI handles on its own.

Should AI make credit decisions?

We recommend keeping your team in charge of credit decisions. Use automation for the manual steps around them, such as fraud checks and credit report pulls, and set the approval rules yourself so you decide which applications a person reviews.

Is it safe to use AI in collections?

Start with AI suggesting how to reach each customer and drafting notices your team edits before they go out. Since collections reaches your customers directly, keep a person on every message until your team trusts the drafts.

How can we tell whether a vendor’s feature is AI or automation?

Ask whether the feature learns from your team’s corrections or follows rules you set. Both are useful. Since AI makes judgment calls, check that your team can review and override what it decides.

What data does AI in accounts receivable need?

Accurate open invoices, customer records, and payment terms from your ERP, plus the remittance details that arrive with payments, such as check stubs and remittance emails. Errors in that data lead to more items in your review queue.

We’re happy to walk you through how we apply this thinking in Dill. Read How Dill Uses AI or book a demo.

About the author: Tara Porter is Product Marketing Manager at Dill, where she regularly interviews AR and finance teams at construction supply companies. Dill builds credit and accounts receivable software for construction suppliers.