How Has AI Changed Accounts Receivable in 2026?
Until recently, AI in accounts receivable meant predictions and drafts: tools scored accounts, forecast payment dates and suggested replies, and a person still sent every answer. Every customer reply, billing query, dispute, portal rejection and unmatched payment still landed in the AR inbox for someone to read, answer and log.
In 2026, AI agents act on those inputs themselves. They read the email, check the ERP and the conversation history, reply in the same thread, route disputes and match payments, and hand a case to a person only when it reaches a limit the team has defined.
The table below shows how each type of AI in accounts receivable handles AR work, from rules-based automation to AI agents, and where a person still has to step in.
Type | What it does | AR example | Where a person still steps in |
Rules-based automation | Runs a fixed step when a condition is met | Sends a reminder 7 days after the due date | Every reply, query and exception |
Predictive AI | Scores accounts and estimates outcomes from past data | Predicts which invoices will pay late and ranks the collections worklist | Contacting the customer and acting on the answer |
Generative AI | Writes or summarises text on request | Drafts a reply to a customer email for a collector to edit and send | Deciding what to say, checking the facts and sending |
AI agents | Read the input, decide the next step and act in connected systems | Reads a request for an invoice copy, attaches the PDF from the ERP and replies in the same thread | Cases the team has not approved the agent to handle alone |
An AI agent is not a chatbot waiting for questions. It works through the queue of AR tasks the way an analyst would, using the invoice, payment and conversation history for each account, and hands a case to a person with that context attached when it reaches a limit the team has defined. For a wider view of where agents fit across finance, see Paraglide's guide to AI agents in finance.
AI accounts receivable software compared: five categories and three platforms
AI accounts receivable software falls into five categories, and the category tells you where manual work will remain after go-live. Most finance teams already own one of them through their ERP or billing platform.
Category | What it automates | Example tools | Where manual work remains |
AI-native agents | Two-way customer conversations and exceptions: queries, collections replies, disputes, portals, unmatched payments | Paraglide | Invoicing and payments stay in the ERP and billing system |
Enterprise order-to-cash (O2C) suites | Covers credit, collections, cash application, deductions and forecasting in one vendor platform | HighRadius (founded 2006) | Project-based rollouts; non-standard replies are drafted for a person |
Predictive collections | Payment-date predictions, risk scores and cash forecasts | Tesorio | Customer replies and follow-up |
Collections-first automation | Reminder sequences, payment plans and collections task lists | Chaser | Replies, queries and disputes |
Billing, portal and cash application specialists | Supports invoice delivery, customer payment portals and payment matching | Billtrust (founded 2001), Versapay (founded 2006) | Collections conversations outside the portal |
How we compared the three platforms
We compared one platform from each of three categories (AI-native agents, enterprise O2C suites and billing specialists) on the same 12 fields.
Field | Paraglide | HighRadius | Billtrust |
Best for | Mid-market and enterprise B2B teams whose delays sit in replies, queries, disputes, portals and exceptions | Large enterprises, including financial services firms, that want one suite beyond AR: accounts payable, B2B payments, treasury and cash forecasting, and financial close | US-focused B2B companies that want invoice delivery to 260+ AP portals and B2B payment acceptance on the same platform as cash application |
Software category | AI-native agents | Enterprise O2C suite | Billing, payments and cash application |
AI model | AI agents that read, decide and act in connected systems, with approval rules defined by the team | Payment-date prediction, customer segmentation and drafted or automatic replies to standard emails | Payment-timing prediction and drafted email replies for collector review |
Collections and inbound handling | Full two-way conversation: outreach, replies and follow-ups in existing threads; inbound billing queries answered in any language | Collections worklists; standard emails such as invoice copy requests answered automatically | Inbound emails categorised and summarised, with a drafted response for the collector to review |
Cash application | Reads remittances in PDF, Excel, email body, lockbox and scanned formats; matches short, bulk and over-payments, missing references and typos | Supports remittance capture from email and accounts payable (AP) portals, and payment-to-invoice matching | Supports payment matching with remittance capture |
Disputes | Detects disputes, escalates to the right approver with context and reminds until resolved | Dispute resolution module | Dispute and case management |
Forecasting | Not a forecasting tool; feeds dispute status and customer replies into the AR record | Cash flow forecasting module | Self-updating cash forecast |
ERP integrations | SAP, Oracle, Oracle NetSuite, Microsoft Dynamics 365 and Sage; CRM (Salesforce, HubSpot), billing (Stripe, Chargebee), Gmail and Outlook inboxes, and portals such as SAP Ariba and Coupa; custom integrations available | 50+ ERP and accounting connectors, including SAP, Oracle NetSuite, Microsoft Dynamics, Workday and Sage Intacct; API for QuickBooks and Business Central | Infor, SAP, Oracle (including JD Edwards, PeopleSoft and E-Business Suite), Epicor, QAD, Microsoft, Acumatica and others |
Human controls | Team decides when the agent acts alone and when approval is needed, starting with low-risk tasks and long-tail customers | Collector review of drafted replies | Collector review of drafted replies |
Pricing | Quote-based; book a demo | Quote-based; outcome-based model promoted | Quote-based |
Implementation | Go live in 3 days, start collecting in 7 | HighRadius states go-live in under 6 months; return on investment stated at 3 to 6 months for collections and cash application | Billtrust states its Collections Quickstart goes live in 45 days or less |
Main limitation | Not a billing system, ERP or payments network | Founded in 2006 with a project-based rollout; non-standard replies still go to a person; pricing only through sales | Founded 2001 and US-focused; replies needing judgement go to a collector; built around its own payments network |
Why accounts receivable still depends on manual conversations
The bottleneck in accounts receivable is the conversation that follows the invoice, not the invoice itself. When payment reminders go out, some customers reply with questions or disputes and others go quiet, and both outcomes create work for the AR team.
I spent years in AR before joining Paraglide, and the inbox was the job. A customer disputes a line item. Another asks for the right purchase order (PO) number. A third says the invoice was never received. Each reply needs someone to open the ERP, find the invoice, check the history and write back, while the same person keeps chasing the accounts that have not replied at all.
Sending more reminders makes this worse. Every extra reminder creates more replies, and every reply goes back to the shared inbox. Across hundreds of customers, collectors end up working through open threads and trying to remember which ones need a follow-up.
The cost shows up in cash. UK companies report bad debt losses of up to 2% of B2B invoices, and in Europe days sales outstanding (DSO) rose 1% to 48.5 days, according to The Hackett Group's 2025 European working capital survey.
6 use cases of AI agents in accounts receivable
The best use cases for AI agents in accounts receivable are the tasks that are high-volume, repetitive and triggered by something a customer or system sends. The table below maps each workflow to its manual bottleneck and the work an agent takes over.
Workflow | Manual bottleneck | What an AI agent does |
Billing queries | Requests for invoice copies, statements and balances wait in the shared inbox | Reads the request, pulls the document from the ERP and replies in the thread |
Collections | Templated reminders go out; replies and follow-ups are handled by hand | Chases every account, replies in the existing thread and follows up until payment |
Disputes | Disputes sit in the inbox without an owner | Detects the dispute, routes it to the right approver with context and sends reminders until it is resolved |
Deductions | Short payments are spotted late and the reason is unclear | Compares the deduction to policy, escalates it and opens a dispute where needed |
Cash application | Payments with missing references, typos or foreign exchange (FX) differences go to unapplied cash | Reads the remittance in any format, matches the payment and contacts the customer when the remittance is missing |
Supplier portals | Invoices have to be uploaded to Ariba, Coupa or Tungsten by hand, and rejections go unnoticed | Logs in, uploads the invoice, matches the PO number, tracks status and escalates rejections |
1. Billing query automation
AI agents answer routine customer queries from the finance inbox: invoice copies, statements, account balances, remittance confirmations and questions about charges. The agent reads the email, finds the invoice in the ERP, attaches what the customer asked for and replies in the same thread. Queries outside its approved scope go to the AR team with the account history already pulled together. This is what Paraglide means by finance inbox automation.
2. Collections and dunning automation
AI agents write payment reminders that reflect the customer's account history and previous replies, then handle the answers. For a customer with 20 overdue invoices, the agent follows up on the whole account in one conversation instead of sending 20 separate reminders. When a customer goes quiet, the agent chases again in the same thread rather than starting a new one.
3. Dispute resolution
AI agents detect disputes from the content and tone of a customer email, then route them to the person who can approve a resolution. The agent gathers the invoice, order and conversation history for the approver and keeps sending reminders internally until the dispute is closed. This matters because a dispute handed to a salesperson often gets forgotten while the invoice stays unpaid.
4. Deduction management
AI agents spot short payments and deductions, compare them to the customer's terms or the company's policy, and escalate the ones that need review. Invalid deductions go into the dispute workflow, so recovery has an owner and a follow-up date.
5. Cash application
Matching payments to open invoices breaks down when amounts do not line up, one payment covers several invoices, or the remittance arrives as a PDF, a spreadsheet, an email body or a photo. AI agents read the remittance, match the payments that rules-based engines reject (short payments, bulk payments, over-payments, missing references and typos) and log every match decision for audit. When a remittance is missing or a deduction is unclear, the agent can ask the customer directly, for example: "It looks like the bank fees weren't included, so 250 USD is missing."
6. Supplier portal submission
Large customers often require suppliers to upload invoices to a procurement portal such as Ariba, Coupa or Tungsten before an invoice enters approval. Robotic process automation (RPA) bots for portals need every click scripted, so they break when a field moves. AI agents look for the PO number rather than a box at a fixed position: they log in, upload the invoice, match it to the correct PO, track its status, resolve rejections and download remittances. Paraglide's guide to managing supplier portals with AI agents covers the workflow in detail.
AI agents vs traditional accounts receivable automation
The difference between traditional AR automation and AI agents is what happens after the customer responds. Rules-based tools trigger a fixed action on a fixed condition. AI agents decide the next step from what the customer actually said or did.
The table below compares how each approach handles the same AR tasks. The middle column is the work that stays with your team under traditional automation.
Task | Traditional AR automation | AI agents in AR |
Payment reminders | Sends a scheduled email based on due-date rules | Sends a follow-up based on account status, previous replies and payment history |
Billing queries | Relies on a person, a portal or a static auto-reply | Identifies the request, pulls the right document and replies in the thread |
Finance inbox triage | Routes by keyword or leaves emails for manual review | Reads each message, identifies what is being asked 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 owner |
Cash application exceptions | Rejects payments that do not match a reference exactly | Reads the remittance and matches short, bulk and mistyped payments |
Supplier portals | Uses RPA scripts that break when the portal layout changes | Finds the PO field on each portal and escalates rejections |
Prioritising overdue accounts | Uses ageing buckets or fixed thresholds | Ranks accounts on payment history, response patterns and open issues |
Context changes the right action. Take a customer who pays monthly and has paid every invoice for three months, but has one 300 USD item open for 120 days. A rules-based sequence pushes the whole account down an escalation path. An agent reads the pattern and asks about that one item, as an experienced collector would.
5 benefits of AI agents in accounts receivable
The benefits of AI agents in accounts receivable show up in cash collected, team hours and forecast accuracy, because the agents take over the conversations that hold up payment.
1. Lower days sales outstanding (DSO)
Queries answered the same day and follow-ups sent in the same thread remove the waits that push invoices into older ageing buckets. Disputes get an owner on day one instead of when someone finally opens the email.
2. Less manual work for the AR team
Inbox triage, document requests and routine chasing move to the agent. The team spends its time on disputes, key accounts and the exceptions that need judgement.
3. Inbox capacity that grows with invoice volume
More invoices mean more replies, queries and portal submissions. An agent absorbs that volume across every language customers write in, so the team stays responsive without adding headcount.
4. More reliable cash forecasts
A payment-date prediction tells you when an invoice is likely to be paid based on how the customer paid in the past. It cannot tell you that the customer disputed the invoice yesterday or is waiting for a corrected PO number. Accurate AR forecasting needs both.
Predictive models work from history: average days to pay, seasonality and ageing. Live operational inputs come from the conversation itself: an open dispute, an unanswered query, a portal rejection or a customer reply confirming the payment run. When an agent handles those conversations, each event updates the AR record, so the invoices at risk are visible before they age into the next bucket.
For a finance director, the practical difference is which invoices you count on. An invoice predicted to pay on day 35 but sitting behind an open dispute should not be in this month's cash forecast. The data to spot that is in the inbox, not the ERP ageing report.
5. A better customer experience
Customers get an answer in the thread they wrote in, in their own language, instead of waiting for someone to pick up the shared inbox. One conversation per account replaces a stack of separate templated reminders.
How much autonomy to give AI agents in accounts receivable
AI agents in AR work best when the team decides which tasks the agent handles alone and which need approval, then widens that scope as results are reviewed. Finance teams that switch everything on at once tend to lose confidence at the first mistake.
The control model has two modes. In suggest mode, the agent drafts the reply or action and a person approves it. In act mode, the agent acts on its own within rules the team defines, and people monitor the results. Paraglide lets teams decide when the agent can act autonomously and when human approval is required, starting with low-risk tasks and long-tail customers.
A typical split looks like this:
Agent acts alone: re-sending invoice copies, confirming payment status, chasing the long tail of small balances, handling bounced emails, capturing PO numbers.
Agent drafts, person approves: replies to key accounts, dispute responses, credit notes, refunds, master data changes and collections escalations.
A staged rollout also matches what finance teams report. In a Gartner survey published in May 2026, 63% of finance organisations said AI implementation was slower than expected in 2025. Starting narrow and widening scope on evidence keeps the project moving.
How Paraglide's AI agents work in accounts receivable
Paraglide builds AI agents for order-to-cash and accounts receivable. Its agents handle the two-way AR conversation and the exceptions that rules-based tools pass back to the team, working inside the existing finance inbox and connected to the ERP. Paraglide is not built to replace invoicing, the ERP or a payments platform. For how agents work across the full O2C cycle, see AI agents in order-to-cash.
Billing Support Agent
The Billing Support Agent manages the AR inbox, replying to incoming billing queries 24/7 in any language. It reads each email, checks invoice data and the conversation thread, and either replies or passes the case to the team with context.
Collections Agent
The Collections Agent handles the full two-way collections conversation: outreach, replies and contextual follow-ups, from the first reminder to debt recovery. It follows up in existing threads like a person would, so customers see one conversation rather than a stack of templated reminders. Spiideo reduced invoices more than 30 days overdue by 43% with Paraglide.
Reconciliation Agent
The Reconciliation Agent captures remittances in any format from the inbox or portals and matches the payments rules-based engines reject: short payments, bulk payments, typos and missing remittances. It also checks whether deductions are valid and reconciles self-bills.
Supplier Portal Agent
The Supplier Portal Agent logs into portals such as SAP Ariba and Coupa, uploads invoices, matches them to the correct PO, tracks statuses and escalates rejections.
Use cases the agents cover
Paraglide's four agents work together across six AR use cases, and each use case draws on the invoice, customer, payment and conversation data the agents read from the ERP, CRM, inbox and portals.
AR inbox. The Billing Support Agent reads every incoming email in the finance inbox and works out what the customer is asking for. It answers invoice and billing questions, sends copies of invoices and statements, confirms payment status, captures remittances and PO numbers, and pushes customer master data updates back to the ERP. Anything outside its approved scope goes to the AR team with the account history attached.
Collections. The Collections Agent chases every overdue customer and invoice, however small the balance. It sends personalised reminders based on each customer's history, handles the replies, and follows up in the same thread when a customer goes quiet. When an email bounces or the contact is wrong, it reroutes the message to the right person, and it escalates to a colleague when a case needs a human decision.
Cash application. The Reconciliation Agent reads remittances in any format, whether they arrive by email, as attachments or through a portal. It matches the payments that rules-based engines reject, including short payments, bulk payments, typos and missing remittances, and it reconciles self-billing statements. When a remittance is missing or unclear, it can contact the customer to get the information needed to apply the cash.
Disputes. Paraglide's dispute management detects disputes from the email subject and tone, then routes each one to the AR owner who can resolve it. The agent gathers the invoice, order and conversation history so the owner can decide quickly, and it sends reminders until the dispute is closed. Over time, the dispute log also shows which root causes keep recurring.
Deductions. The Reconciliation Agent checks each short payment or deduction against the customer's terms and the company's policy. Valid deductions are applied, and invalid ones are escalated and moved into the dispute workflow, so every recovery has an owner and a follow-up date.
Credit. Paraglide's credit management software combines external credit scores with the customer's internal payment behaviour. It approves credit increases against configurable rules before orders get blocked, flags high-risk accounts early (for example, a customer who stops paying and stops responding), automates order blocks and releases, and runs periodic credit reviews. A person approves wherever the team decides a sign-off is needed.
To see how the agents fit together, read the Paraglide product overview.
Implementation checklist: how to deploy AI agents in accounts receivable
Teams that get value from AI agents in AR know where their process works, where it breaks and where a person needs to stay involved. Work through these steps in order.
Measure inbox volume and patterns. Count inbound emails per week and tag a sample by type: invoice copy, payment status, dispute, remittance, PO request. The biggest categories are where to start.
Map current workflows and escalation paths. Write down how queries, disputes and collections cases move through the team today. The agent should fit that process, not run beside it.
Check data quality across systems. Confirm that invoice, customer and contact data in the ERP, billing system and customer relationship management (CRM) system are accurate. An agent can only answer well if the invoice data behind it is right.
Define escalation boundaries. Decide which tasks the agent handles alone and which need approval, by task type and customer segment. Keep disputes, refunds and key accounts in suggest mode at first.