Accounts receivable is already a fairly automated function in most businesses. Invoices go out on time, reminder workflows are scheduled, the ERP is reconciled, and ageing reports arrive every Monday morning. But anyone who has worked in AR knows that the harder part of collections starts after the invoices go out, especially in B2B. A customer disputes a line item, and another asks for the correct PO number. Every one of those replies and queries lands in the shared AR inbox for someone to read, pull the right invoice data from other systems, and respond with context. The same team also has to keep following up to ensure payment is made. Multiply that across hundreds of customers, and it compounds quickly. This is where accounts receivable remain manual and where AI agents are becoming valuable.
I spent years working in AR before joining Paraglide. The inbox problem wasn't theoretical, it was the reality of my job. In this article, I will cover what AI agents actually do inside an AR workflow, where they make a real difference, and what to think about before deploying one.
What are AI agents in accounts receivable?
An AI agent is software that can read unstructured data like text, understand what is being asked, decide what should happen next, and take action. When applied to accounts receivable, AI agents operate across the communication, prioritisation and execution layer of AR operations. They integrate directly into finance tools such as ERP systems, shared inbox, and billing systems to extract data that they work with.
AI agents work like a good AR analyst; they read multiple inputs at once, make a judgment call, and act. The word "agent" matters here as there is a general assumption that AI agents are chatbot that answers questions. But, unlike rule-based tools that trigger actions when a condition is met, accounts receivable AI agents act. Finance teams are exploring AI in live workflows to improve speed, visibility and decision-making, particularly areas tied to working capital.
6 common use cases of AI agents in accounts receivable
The most common use cases of AI agents in AR are the parts of collections that are repetitive, high-volume and still heavily manual.
Billing query automation
AI agents can automate replies to routine customer queries such as requests for invoice copies, statements, account balances, remittance confirmation or clarification on charges from the finance inbox.
Collections and dunning automation
AI agents can automate and personalise payment reminders and follow-up messages in a way that reflects the customer’s situation, account history and previous responses. They can also manage responses to these reminders and follow-up when responses are delayed.
Dispute resolution
When a customer challenges an invoice, AI agents can resolve minor issues and flag high-risk cases for review and resolution.
Deduction management
AI agents can spot short payments and deductions, help identify the likely reason behind them and send them to the right person for review.
Contact management
Matching payments to open invoices is often more complicated than it should be, especially when amounts do not line up neatly or one payment covers several invoices. AI agents can help handle that matching work automatically, reducing unapplied cash and saving the team a lot of time.
Promise-to-pay tracking
AI agents can capture payment commitments made by customers in conversations, record expected payment dates and send a follow-up when payment does not arrive as promised.
AI agents vs traditional accounts receivable automation
The difference between traditional accounts receivable automation tools and AI agents is execution. Traditional tools follow predefined rules. AI agents are better suited to handling the messy, variable parts of AR work where the next step depends on what the customer has actually said or done.
Task | Traditional AR automation | AI agents in AR |
Payment reminders | Sends a scheduled email based on due date rules | Sends follow-up based on customer status, previous replies and payment behaviour |
Billing queries | Usually relies on a person, portal or static auto-reply | Identifies the request, pulls the right document and replies in the thread |
Promise-to-pay tracking | Leaves the team to note it and remember to follow up | Captures the promise to pay, tracks the date and follows up if payment does not arrive |
Finance inbox triage | Routes by keyword or leaves emails for manual review | Reads incoming messages, identifies intent and prioritises what needs attention |
Dispute management | Depends on someone opening the email and recognising the issue | Detects the dispute, classifies it and routes it to the right workflow |
Decide which overdue accounts need attention first | Uses ageing rules or fixed thresholds | Prioritises based on risk, response patterns and likelihood of delay |
5 benefits of automating accounts receivable with AI agents
The benefits of AI agents for AR are tied to financial and operational results
Lower DSO (Day Sales Outstanding)
AI agents help teams resolve billing queries faster and follow up on payment commitments more consistently. That keeps conversations moving, removes avoidable delays and helps shorten the time it takes to collect cash.
Less manual work for the AR team
A lot of AR time still goes into managing inboxes, drafting reminders and keeping track of who said they would pay and when. AI agents take on much of that repetitive work, which reduces pressure on the team and frees people up for higher-value tasks.
More scalable inbox management
As invoice volumes grow, so does the volume of customer communication. AI agents can help absorb that load by sorting messages, prioritising what matters and responding to routine queries, so teams can stay responsive without adding more headcount.
Better cash flow visibility
By tracking payment promises, customer behaviour and open issues, AI agents give finance teams a clearer view of what cash is likely to come in and where delays are building. That makes forecasting and working capital planning more reliable.
A stronger customer experience
Accounts receivable has a direct effect on how customers experience your finance function. AI agents help teams respond more quickly, follow up more consistently and avoid the kind of fragmented communication that creates friction and slows payment.
A practical implementation checklist for automating accounts receivable with AI agents in 2026
The teams that get value from it are usually the ones that know where their process is already strong, where it breaks down, and where a person still needs to stay involved.
Assess finance inbox volume and communication patterns
Look at how much time the team spends on repetitive queries, follow-ups and inbox management. This helps identify where automation will remove real operational pressure.
Map existing AR workflows and escalation paths
Be clear on how billing queries, disputes and collections issues move through the team today. The agent should fit into that process, not create a separate one.
Evaluate data quality across systems
Check that ERP, billing and CRM data are accurate, consistent and accessible. An AI agent can only respond well if the underlying invoice and customer data are reliable.
Define clear escalation boundaries
Set clear rules for what the agent can handle on its own and what should go to a person. Routine queries may be automated, but sensitive cases and complex disputes still need human judgment.
Measure impact using clear KPIs
Track outcomes such as response times, inbox backlog, promise-to-pay follow-up and DSO. That makes it easier to see whether the deployment is creating real value.
Implementation usually works best when teams start with communication automation first, then expand into wider workflow optimisation once the basics are working well.
3 Ways Paraglide Automates Accounts Receivable with AI Agents
Paraglide is an AI-native accounts receivable solution built for the full AR conversation. Paraglide's AI agents help finance teams manage both inbound and outbound receivables work with the context, control, and flexibility needed to keep cash moving. Across its customer base, Paraglide customers reduce DSO by an average of 34%.
1. Billing query automation
Paraglide’s Billing Support Agent works directly in the finance inbox, where billing queries, document requests, and payment-related questions often create delays. It reads incoming customer emails, checks live invoice data and account history, reviews the full conversation thread, and responds with accurate, context-aware replies. Routine queries can be resolved automatically from start to finish, while more complex cases are passed to the AR team with the relevant context already pulled together. This helps teams reduce inbox backlog, respond faster, and prevent unresolved queries from delaying payment.
2. Collections automation
Paraglide’s Collections Agent manages collections as an ongoing customer conversation rather than a series of one-off reminders. It sends personalised outreach based on customer history and payment behaviour, handles replies to payment reminders, follows up on unresolved threads, and keeps conversations moving until an issue is settled or payment is received. This gives finance teams more consistent follow-up, better coverage across accounts, and less manual chasing.
3. Credit approvals
Paraglide supports credit teams by helping them make faster, better-informed decisions. Its Credit Agent summarises the key information and context behind a case so credit teams can review requests with a clearer picture of customer status, history, and risk.
The future of accounts receivable with AI agents
The biggest gains in accounts receivable will not come from adding more reminders or tightening policy alone. They will come from improving the part of the process where cash collection usually slows down. This is why the operating model of AR is starting to change. The finance teams that deploy AI agents in their AR workflows over the next few years will be the ones that build more structure and consistency for their working capital.