The order-to-cash function has never moved faster than it is moving right now, and that is because of AI agents. Having spent the better part of my career in finance leadership running AR teams, owning cash collection targets, and evaluating every generation of automation that promised to fix the process, I can say with confidence that what agents make possible today was simply not possible even twelve months ago. Anthropic's release of Opus 4.5 was a game-changer and has unlocked capabilities we could only dream of.
The shift is fundamental. For decades, the model was humans doing the work, using software to manage and track it. That model is inverting. AI agents now execute the work itself — reading billing queries, responding to customers, managing collections conversations, parsing remittances — while humans shift to approving tasks, monitoring actions, and intervening where judgement is required. It is not a better tool for the same workflow. It is a different operating model for the finance function.
What RPA Did for Order-to-Cash, and Where It Stopped
RPA automation in O2C focused on structured, rule-based data tasks: extracting invoice data from PDFs, matching remittance files to open items, populating ERP fields, generating ageing reports. For high-volume, low-variability tasks, it delivered measurable efficiency gains. According to Gartner's Predicts 2024: Finance and Accounting Technology report, finance functions deploying RPA at scale reduced transaction processing costs by 25–50% in targeted workflows — but adoption stalled when the inputs became unstructured.
That stall point matters. In O2C, the majority of the high-value, payment-critical work is unstructured. Disputes, billing queries, collections conversations, credit assessments, and deduction notifications all arrive as free text, referencing account histories and prior conversation threads that require judgement to interpret. RPA cannot read a customer email, understand that they are querying a line-item discrepancy on invoice 4721, cross-reference their account history to confirm the pricing was correct, and respond. As soon as the input was unstructured, a human took over — and in a busy AR team, that human was already behind.
Automation Type | Input Format | O2C Tasks Covered | Handles Conversation |
RPA | Structured data only | Data extraction, ERP population, remittance matching | ❌ None |
Traditional software and RPA | Semi-structured | Outbound reminders, dunning workflows, reporting | ❌ None |
Agentic AI (Paraglide) | Structured + unstructured, free text, full threads | Full O2C cycle: inbound queries, collections conversations, cash application, credit management | ✅ End-to-end |
What Agentic AI Actually Means in an O2C Context
Agentic AI in order-to-cash refers to AI systems that can perceive an input — an inbound email, a remittance file, a credit application — reason about it using live account data and prior conversation context, decide on an action, and execute it without requiring human instruction at each step. Unlike RPA, which follows a fixed script on structured data, agentic AI adapts to the content it is processing. Unlike a chatbot, it acts across systems rather than responding to prompted inputs.
McKinsey's 2024 report The state of AI in 2024: GenAI adoption spikes and starts to generate value identified finance operations as one of the highest-adoption areas for generative AI, with accounts receivable — specifically inbound query handling and collections conversation management — cited among the workflows delivering the fastest measurable returns. EY's CFO Imperative: How to embed AI in your finance function (2024) similarly points to AR inquiry management and collections as the primary manual bottlenecks in shared services O2C that AI is now equipped to address at scale.
In practice, an AI agent in O2C can:
Read an inbound billing query, understand the intent, retrieve live invoice and account data, and send an accurate reply — without a human reading the email
Manage a collections conversation end-to-end: send a personalised payment reminder, handle the reply, follow up on unresolved threads, and escalate disputes with full context assembled
Parse a remittance file regardless of format, match payments to open invoices, and flag exceptions for human review
Apply cash to the AR ledger based on remittance data and matching rules
Summarise account history and risk signals to support a credit decision
This is a different category of automation from anything available before. It operates at the level of the conversation, not the transaction.
Invoice Inquiry Management: The Inbound Problem Every Legacy Platform Ignored
Invoice inquiry management is the process of receiving, triaging, and resolving inbound customer queries about invoices, billing, and payment. It is the largest unautomated workload in most AR operations, and — in my experience running AR teams — the primary cause of payment delays that neither RPA nor reminder software could address.
I watched this play out with every platform we evaluated or implemented. Esker, HighRadius, and Sidetrade are all capable outbound systems. They automate dunning sequences, track payment promises, and integrate deeply with ERPs. None of them were built to handle what comes back.
When a customer replies to a reminder saying their PO number is missing and the invoice cannot be processed, that reply lands in the finance inbox. When a customer emails asking why their invoice amount doesn't match their purchase order, that lands in the inbox. When a customer follows up on a dispute they raised three weeks ago and wants a status update, that lands in the inbox. In every organisation I have been part of, those queries were handled by AR specialists working manually, one email at a time, out of a shared Outlook mailbox.
The consequence is measurable. Every unresolved billing query is a blocked payment. A customer cannot process an invoice with an incorrect PO number. A customer will not pay a disputed amount. A customer waiting on a statement cannot reconcile their accounts. KPMG's 2024 Finance Transformation Survey found that invoice query resolution delays were among the top three cited causes of extended DSO in B2B finance operations, yet fewer than 15% of respondents had any automation in place specifically for inbound billing query handling.
Paraglide's Billing Support Agent operates directly in the finance inbox. It reads every incoming email, identifies the query type, retrieves live data from billing systems and account records, reads the full conversation thread, and responds — automatically, accurately, and in context. Standard queries are resolved end-to-end without human involvement. Complex queries are routed to an AR specialist with everything assembled: the customer's query, the account history, the prior thread, and a draft response. What previously required fifteen to twenty minutes of investigation per email takes two.
Query Type | Esker / HighRadius / Sidetrade | Paraglide Billing Support Agent |
Invoice copy request | ❌ Manual — lands in inbox | ✅ Retrieves and sends invoice automatically |
Amount discrepancy | ❌ Manual investigation required | ✅ Cross-references invoice and account data, responds with explanation |
Missing PO number | ❌ Manual | ✅ Flags for reissue, confirms with customer |
Follow-up on prior email | ❌ Treated as new query, context lost | ✅ Reads full thread, provides accurate status update |
Multi-issue email | ❌ Not handled | ✅ Addresses all issues in a single response |
Dispute notification | ❌ Manual escalation | ✅ Captures details, routes to AR team with full context |
Statement request | ❌ Manual | ✅ Generates and sends statement with live data |
After-hours query | ❌ Waits until next business day | ✅ Resolved within minutes, 24/7 |
Paraglide customers reduce DSO by an average of 34%. That reduction is not driven by better reminders. It is driven by faster resolution of the inbound queries blocking payment — the work that every other platform left to the AR team.
Collections Conversations: Why One Reminder Was Never the Full Picture
Every legacy AR platform was built around the same model: send a dunning sequence, escalate if no payment, hand off to collections. HighRadius added payment portals and predictive analytics. Esker added workflow routing. Sidetrade layered in scoring and segmentation. These are genuine improvements. But the model remained fundamentally one-directional — the platform handled message one, and everything that followed was manual.
The core insight I kept coming back to in my time running AR teams is that a payment reminder is the first message in a conversation that may require many exchanges before payment moves. The customer has a query. They need a credit note applied first. They are waiting on sign-off from a department head. They want to confirm their remittance has been received. Each of these replies requires a response before the associated payment can progress — and in every organisation I worked in, those responses were handled manually by the same specialists who were also supposed to be working their collections strategy.
Paraglide's Collections Agent manages outbound collections as complete conversations. It sends personalised AI-generated outreach tailored to each customer's account history and payment behaviour — not templated reminders, but messages that reference the specific account context. It handles replies to payment reminders. It follows up on unresolved threads. It manages the end-to-end collection conversation until payment is resolved or the case is escalated with full context to the AR team.
According to Deloitte's Global Shared Services and Outsourcing Survey 2024, 67% of shared services finance leaders cited inbound query management and collections follow-up as the highest-manual-burden processes in their O2C operations — ahead of cash application and reporting. More outbound automation without inbound handling increases that burden. Every reminder sent is an invitation to reply.
Remittance Parsing and Cash Application
Cash application — matching incoming payments to open invoices on the AR ledger — has historically been one of the most labour-intensive processes in shared services O2C. Customers send remittances in inconsistent formats: structured EDI files, PDF attachments, free-text emails with payment references, or no remittance at all. RPA helped where formats were consistent. It failed on everything else.
Agentic AI handles remittance parsing at the level that RPA could not reach: extracting payment information from unstructured formats, interpreting ambiguous references, applying matching logic against live AR ledger data, and flagging exceptions — short payments, unapplied cash, unidentified deductions — for human review.
Deductions management deserves specific mention. Deductions arrive embedded in remittance data or as separate claim notifications, reference a variety of claim types — pricing disputes, delivery shortfalls, promotional allowances — and require cross-referencing against account history and invoice data before a decision can be made. In the AR teams I ran, deductions management was consistently the process with the longest resolution cycle and the most write-off risk from cases that simply aged out without proper investigation. AI agents capture, categorise, and route deductions with full context assembled, reducing time from receipt to resolution.
Credit Management: AI Agents for Credit Decisioning
Credit management is the upstream function of O2C that determines which customers receive credit terms, at what limits, and under what conditions. The bottleneck in credit management is not analysis capability. It is information assembly.
A credit controller reviewing an account must gather payment history, aging data, dispute history, current exposure, and external credit signals before making a recommendation. In every shared services environment I have been part of, this information was spread across the ERP, the AR platform, and the inbox — and assembling it manually for each decision consumed most of the handling time per case.
Paraglide's Credit Agent assembles account history, payment behaviour, dispute patterns, and current exposure into a structured decision brief for each case. The credit controller receives the analysis ready for approval — not a data dump. EY's CFO Imperative (2024) specifically identifies credit assessment and AR inquiry management as the finance workflows where AI-generated decision support is delivering the fastest reduction in processing time in early deployments.
What This Means for Shared Services and Finance Transformation
Shared services centres and global business services operations face the O2C automation challenge at scale: high invoice volumes, global customer bases across time zones and languages, multiple entities, and pressure to reduce cost per transaction while maintaining or improving cash performance.
The RPA and legacy platform investments most shared services teams made over the past decade addressed a subset of the problem. Transaction processing costs came down. Outbound collections was partially automated. But the inbound query volume — which scales with both invoice volume and outbound automation activity — remained manual, and the headcount required to manage it remained constant or grew. I saw this directly: implementing reminder automation in a previous role reduced the time spent on outbound chasing and immediately increased the time spent managing the replies it generated.
Agentic AI changes the economics in three specific ways. First, it handles the inbound query volume that was previously managed by AR specialists, freeing headcount for higher-complexity work. Second, it operates 24/7 across time zones without the coverage gaps that drive 12-to-16-hour response delays in global operations. Third, it handles multiple languages, removing the operational complexity of routing queries to language-specific team members.
O2C Function | RPA | Traditional software | Paraglide (Agentic AI) |
Invoice data extraction | ✅ | ✅ | ✅ |
Outbound payment reminders / dunning | ❌ | ✅ Templated sequences | ✅ AI-personalised conversations |
Inbound invoice inquiry handling | ❌ | ❌ | ✅ Full AI agent, end-to-end |
Collections conversation management | ❌ | ❌ | ✅ End-to-end |
Remittance parsing (unstructured) | ⚠️ Structured only | ⚠️ Limited | ✅ |
Cash application and matching | ⚠️ Rule-based | ⚠️ Partial | ✅ |
Deductions management | ❌ | ⚠️ Manual with workflow routing | ✅ |
Credit decisioning support | ❌ | ⚠️ Scoring models only | ✅ |
Full conversation thread context | ❌ | ❌ | ✅ |
24/7 multilingual coverage | ❌ | ❌ | ✅ |
For finance transformation leads evaluating the next phase of O2C investment, the question is not whether to adopt agentic AI, but which processes to prioritise. Invoice inquiry management and collections conversation management typically offer the fastest and most quantifiable return — because the cost of manual handling and the DSO impact of slow resolution are both directly measurable.
Closing: The AR Conversation Is the Automation Frontier
RPA automated the transaction. Reminder software — Esker, HighRadius, Sidetrade — automated the outbound. Neither touched the conversation: the inbound queries, the dispute notifications, the collections follow-ups, the remittance questions that determine whether and when customers actually pay.
Having run AR teams where that gap was a daily operational reality, the shift to agentic AI is not incremental. It is the capability that makes the rest of the automation stack work. The bottleneck was never sending more reminders. It was resolving the issues blocking payment faster — and doing it at a volume and speed that no human team can match.
Paraglide is the only AI-native AR platform built for the full AR conversation. Customers reduce DSO by an average of 34%.
Read more about how Paraglide's AI agents are changing the economics of order-to-cash.